Market Segmentation and product development strategies .ipynb

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   "cell_type": "markdown",
   "id": "5069389b-f9f5-4f3a-907d-97dd9133f6dd",
   "metadata": {},
   "source": [
    "# **MARKET SEGMENTATION AND PRODUCT DEVELOPMENT STRATEGIES**\n",
    "----------------------------------------------------------------- \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c403fa5e-4c25-4af0-8a7e-c396557bc239",
   "metadata": {},
   "source": [
    "The research topic is \"Market Segmentation and Product Development Strategies.\" This research addresses a important component of modern business strategy, especially in the rapidly growing e-commerce sector. One way for online retailers to gain competitive advantage, increase customer satisfaction and drive profitability is by understanding how to efficiently segment markets and develop products that suit particular groups of customers. This implies that businesses must understand who their customers are exactly before designing a product or service for them. Furthermore, as big data and advanced analytics continue to rise, there are new opportunities for using customer data in making more informed decisions about markets. The aim of this research is bridging the gap between market segmentation insights and practical product development strategies that ultimately lead to better business outcomes. \n",
    "<br>\n",
    "<br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db860b2e-db7d-49e5-bbb8-dc3cc3c6297c",
   "metadata": {},
   "source": [
    "# **1.0 Introduction**\n",
    "-------------------------\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3361b7be-eff8-4493-8923-fa22603de11b",
   "metadata": {},
   "source": [
    "In this notebook, we utilize the publicly available Online Retail dataset to explore customer segmentation and provide product development strategies based on the customer data.\n",
    "\n",
    "### 1.1 Dataset Introduction\n",
    "\n",
    "The <a href=\"https://www.kaggle.com/datasets/puneetbhaya/online-retail\"> Online Retail </a>  a transnational data set which contains all the transactions occurring between `01/12/2010` and `09/12/2011` for a UK-based and registered non-store online retail.The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers.\n",
    "\n",
    "**Additional variable information**\n",
    "\n",
    "1. **InvoiceNo:** A unique identifier for the invoice. An invoice number shared across rows means that those transactions were performed in a single invoice (multiple purchases). \n",
    "2. **StockCode:** Identifier for items contained in an invoice.<br>\n",
    "3. **Description:** Textual description of each of the stock item.<br>\n",
    "4. **Quantity:** The quantity of the item purchased.<br>\n",
    "5. **InvoiceDate:** Date of purchase.<br>\n",
    "6. **UnitPrice:** Value of each item.<br>\n",
    "7. **CustomerID:** Identifier for customer making the purchase.<br>\n",
    "8. **Country:** Country of customer.<br>\n",
    "\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "791ccb6d-3c34-4497-958c-c11dcf8d94d2",
   "metadata": {},
   "source": [
    "## 1.2 Import Libraries\n",
    "-----------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c8e39d5-60da-477d-b096-8c70d2e05715",
   "metadata": {},
   "source": [
    "Import all the necessary libraries required for the analysis of customer data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "997ed833-6f21-4b60-8198-619bcbcc6364",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "import re\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "warnings.simplefilter('ignore')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84c6490e-a73e-46f5-8e0f-645fa9e880d1",
   "metadata": {},
   "source": [
    "\n",
    "## 1.3 Import Dataset\n",
    "-----------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fda56cc8-ddac-42e2-bf81-a6373851709d",
   "metadata": {},
   "source": [
    "Importing the Online Retail dataset from an Excel file into a pandas **DataFrame**. This will allow us to easily **manipulate** and analyze the data using Python's powerful data analysis libraries.\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3864a706-e6b0-496d-a30d-33fc39d0a78f",
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom  \n",
       "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom  \n",
       "3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('Online Retail.xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2104be52-20d4-46bc-8ed8-bc2f59515ba7",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "It's important to understand the overall size of the dataset, including the number of rows (transactions) and columns (attributes)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ececced7-815e-4c5b-a213-aff564224a6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(541909, 8)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3d94b59-6cdf-42f8-bdf1-7b3a4aa46b9b",
   "metadata": {},
   "source": [
    "# **2.0 Cleansing Dataset**\n",
    "-----------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f909912-97cb-4c3d-8f25-52db255912ee",
   "metadata": {},
   "source": [
    "Before proceeding with any analysis, it's essential to check for **missing values** in the dataset. Missing values can affect the quality of analysis, so we need to identify and address them appropriately (e.g., by removing or imputing the missing data)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ecb2e914-bdd6-48dc-8392-5434d854c72a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo           0\n",
       "StockCode           0\n",
       "Description      1454\n",
       "Quantity            0\n",
       "InvoiceDate         0\n",
       "UnitPrice           0\n",
       "CustomerID     135080\n",
       "Country             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "34b30535-b3e4-4a61-b343-99db477e64ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(406829, 8)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping rows with missing values\r\n",
    "df = df.dropna()\r\n",
    "\r\n",
    "# Checking the new dimensions of the dataset\r\n",
    "df.shape\r\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40dc4aac-5f35-4c74-b0e6-2fdbbf83aa77",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "It's important to verify the **data types** of each column in the dataset to ensure they are correctly formatted for analysis. This step helps identify if any columns need to be converted (e.g., dates, numerical values) before further processing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1614c739-e319-48e0-b290-a2f7844e0692",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo              object\n",
       "StockCode              object\n",
       "Description            object\n",
       "Quantity                int64\n",
       "InvoiceDate    datetime64[ns]\n",
       "UnitPrice             float64\n",
       "CustomerID            float64\n",
       "Country                object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01707624-4606-45f0-b074-2c73696c7de4",
   "metadata": {},
   "source": [
    "<br>\n",
    "Re-checking for missing values in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e0563d0f-3635-4f03-abbc-becac1692c6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo      0\n",
       "StockCode      0\n",
       "Description    0\n",
       "Quantity       0\n",
       "InvoiceDate    0\n",
       "UnitPrice      0\n",
       "CustomerID     0\n",
       "Country        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a888c80-7c1c-45a2-8ea5-00d8fd00d47d",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "## 2.1 Cleaning String Data and Data Types\n",
    "-------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ff0c915-e596-4714-8cf7-606e27778cf3",
   "metadata": {},
   "source": [
    "In this step, we perform several data cleaning tasks to standardize and format the dataset appropriately. These tasks include:\n",
    "\n",
    "1. **Cleaning Text Data:** The `Description` column is cleaned by removing periods, converting text to uppercase, and trimming any extra spaces.\n",
    "2. **Converting Data Types:** We ensure that columns such as `InvoiceNo` and `CustomerID` are treated as strings for consistency and proper analysis.\n",
    "   \n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "34c6923e-6cc2-4eeb-8349-65b4546aa77c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo      string[python]\n",
       "StockCode              object\n",
       "Description            object\n",
       "Quantity                int64\n",
       "InvoiceDate    datetime64[ns]\n",
       "UnitPrice             float64\n",
       "CustomerID     string[python]\n",
       "Country                object\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Description'] = df['Description'].str.replace('.','').str.upper().str.strip()\n",
    "df['Description'] = df['Description'].replace(r\"\\s+\",' ', regex = True)\n",
    "df['InvoiceNo'] = df['InvoiceNo'].astype('string')\n",
    "df['CustomerID'] = df['CustomerID'].astype('string')\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d4e093d-d55e-4155-bc98-e0855e39d6b3",
   "metadata": {},
   "source": [
    "<br>\n",
    "After performing data cleaning and type conversions, It’s important to review the first few rows of the DataFrame to ensure that the cleaning operations were applied correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d22474f4-9393-4a81-8b58-f8a9dded25c6",
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       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
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       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
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       "      <td>United Kingdom</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E        RED WOOLLY HOTTIE WHITE HEART         6   \n",
       "\n",
       "          InvoiceDate  UnitPrice CustomerID         Country  \n",
       "0 2010-12-01 08:26:00       2.55    17850.0  United Kingdom  \n",
       "1 2010-12-01 08:26:00       3.39    17850.0  United Kingdom  \n",
       "2 2010-12-01 08:26:00       2.75    17850.0  United Kingdom  \n",
       "3 2010-12-01 08:26:00       3.39    17850.0  United Kingdom  \n",
       "4 2010-12-01 08:26:00       3.39    17850.0  United Kingdom  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aafeaa5-a09c-44c6-9ad0-568389beec1b",
   "metadata": {},
   "source": [
    "## 2.2 Addressing negative prices & quantities\n",
    "-------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b85e095-db09-419e-89cc-deead4fae44d",
   "metadata": {},
   "source": [
    "0.6% of the data set has negative price points while 2% has negative recorded quantities.\n",
    "\n",
    "**More precisely:**\n",
    "\n",
    "1. 99% of Invoices that are cancelled have negative Quantity rows. This perhaps represents returns, etc.\n",
    "<br>\n",
    "2. 85% of transactions with negative quantities are cancelled invoices. We'll need to do further digging on what these other transactions represent.\n",
    "   <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2a34c8d5-60db-4d02-8076-3c03f4774558",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>406829.000000</td>\n",
       "      <td>406829</td>\n",
       "      <td>406829.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.061303</td>\n",
       "      <td>2011-07-10 16:30:57.879207424</td>\n",
       "      <td>3.460471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-80995.000000</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2011-04-06 15:02:00</td>\n",
       "      <td>1.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>2011-07-31 11:48:00</td>\n",
       "      <td>1.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>2011-10-20 13:06:00</td>\n",
       "      <td>3.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80995.000000</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>38970.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>248.693370</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.315162</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Quantity                    InvoiceDate      UnitPrice\n",
       "count  406829.000000                         406829  406829.000000\n",
       "mean       12.061303  2011-07-10 16:30:57.879207424       3.460471\n",
       "min    -80995.000000            2010-12-01 08:26:00       0.000000\n",
       "25%         2.000000            2011-04-06 15:02:00       1.250000\n",
       "50%         5.000000            2011-07-31 11:48:00       1.950000\n",
       "75%        12.000000            2011-10-20 13:06:00       3.750000\n",
       "max     80995.000000            2011-12-09 12:50:00   38970.000000\n",
       "std       248.693370                            NaN      69.315162"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a8e8521-e386-4ef2-967f-021cfb3b7349",
   "metadata": {},
   "source": [
    "This is strange if our dataset contains **high negative values**. If we look carefully, not all of our dataset contain transactions from selling products activities. Some of fees and costs are included in this dataset. This fees and cost are in StockCode that contain only letter or alphabet (including space) values.\n",
    "<br><br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7e576c4-520d-48f7-a3b8-5bc38db7dfcb",
   "metadata": {},
   "source": [
    "#### 2.2.1 Cleaning Data Frame where StockCode contains space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f29c03fa-0cce-41f3-8be9-9530c5fe2bac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(371020, 8)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new column to identify if StockCode contains only alphabetic characters\n",
    "df['string'] = df.StockCode.str.isalpha()\n",
    "\n",
    "# Filter the dataset to keep rows where StockCode contains only alphabetic characters\n",
    "df = df[df['string'] != False]\n",
    "\n",
    "# Create a new column to identify if StockCode contains spaces\n",
    "df['space'] = df.StockCode.str.contains(' ')\n",
    "\n",
    "# Filter the dataset to keep rows where StockCode contains spaces\n",
    "df = df[df['space'] != False]\n",
    "\n",
    "# Drop the auxiliary columns used for filtering\n",
    "df = df.drop(columns=['string', 'space'])\n",
    "\n",
    "# Display the dimensions of the cleaned DataFrame\n",
    "df.shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95a4b415-e7b5-4d2f-a932-80ecbdff7f12",
   "metadata": {},
   "source": [
    "Next we have to filtering out canceled transaction. In the description of dataset, it is written that there are some canceled transactions presented in dataset. Canceled transactions is indicated by invoice number which starts with letter 'C'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "eefa9ad2-069d-4a1b-932c-dcfa70dcbf8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(363119, 8)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[~df.InvoiceNo.str.contains('C')]\n",
    "df.shape\n",
    "     "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2410bf4b-573e-4ea8-b520-dd23ff96e5ab",
   "metadata": {},
   "source": [
    "Now, it is time to remove duplicate data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "623a7eb0-2465-4434-a0f3-cb416183da3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(358309, 8)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates(inplace = True)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5729450-735e-4533-8992-6889a4073695",
   "metadata": {},
   "source": [
    "## 2.3 Handling Outliers Using IQR Method\n",
    "-------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab5b1dc7-d76b-46a0-a2be-c809a5acc73c",
   "metadata": {},
   "source": [
    "Outliers can significantly impact statistical analyses and model performance. The **Interquartile Range (IQR)** method is a widely used approach for detecting and removing outliers. In this step, we will use the IQR method to identify and exclude outliers in the UnitPrice column of the dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f2a38d85-72aa-4ec7-af98-e7ac8d3667ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original DataFrame shape: (358309, 8)\n",
      "Cleaned DataFrame shape: (326249, 8)\n"
     ]
    }
   ],
   "source": [
    "# Finding the IQR (Interquartile Range) for UnitPrice\n",
    "percentile25 = df['UnitPrice'].quantile(0.25)\n",
    "percentile75 = df['UnitPrice'].quantile(0.75)\n",
    "iqr = percentile75 - percentile25\n",
    "\n",
    "# Calculating the upper and lower limits for outlier detection\n",
    "upper_limit = percentile75 + 1.5 * iqr\n",
    "lower_limit = percentile25 - 1.5 * iqr\n",
    "\n",
    "# Removing outliers from UnitPrice\n",
    "new_df = df[(df['UnitPrice'] < upper_limit) & (df['UnitPrice'] > lower_limit)]\n",
    "\n",
    "print(\"Original DataFrame shape:\", df.shape)\n",
    "print(\"Cleaned DataFrame shape:\", new_df.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0b6ceea-df0b-474f-a35a-89dcbdc9feae",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "\n",
    "After removing outliers in the UnitPrice column, it’s important to also address potential outliers in the Quantity column to ensure that the dataset is robust and reliable for analysis. We will use the IQR method to identify and remove outliers in the Quantity column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "38f128e9-60c6-4064-8cd5-10da1e9dbc87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cleaned DataFrame shape after Quantity outlier removal: (303169, 8)\n"
     ]
    }
   ],
   "source": [
    "# Finding the IQR (Interquartile Range) for Quantity\n",
    "percentile25 = new_df['Quantity'].quantile(0.25)\n",
    "percentile75 = new_df['Quantity'].quantile(0.75)\n",
    "iqr = percentile75 - percentile25\n",
    "\n",
    "# Calculating the upper and lower limits for outlier detection in Quantity\n",
    "upper_limit = percentile75 + 1.5 * iqr\n",
    "lower_limit = percentile25 - 1.5 * iqr\n",
    "\n",
    "new_df = new_df[(new_df['Quantity'] < upper_limit) & (new_df['Quantity'] > lower_limit)]\n",
    "\n",
    "print(\"Cleaned DataFrame shape after Quantity outlier removal:\", new_df.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a48209ee-3c85-4596-94d4-0c4bc16bc04b",
   "metadata": {},
   "source": [
    "### 2.3.1 Extracting Date Components from `InvoiceDate` column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8105e2c1-b84b-4434-ab91-1ce7bb3b710d",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "new_df['Date'] = new_df['InvoiceDate'].dt.normalize()               # Extract the date without time\n",
    "new_df['Year'] = new_df['InvoiceDate'].dt.year                      # Extract the year\n",
    "new_df['Month'] = new_df['InvoiceDate'].dt.month_name().str[:3]     # Extract the abbreviated month name\n",
    "new_df['Day'] = new_df['InvoiceDate'].dt.day_name()                 # Extract the day of the week\n",
    "new_df['Hour'] = new_df['InvoiceDate'].dt.hour                      # Extract the hour of the day\n",
    "new_df['month_year'] = new_df['InvoiceDate'].dt.to_period('M')      # Extract the month and year as a period\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b79a5da-6d91-4545-805a-19dbc83f3135",
   "metadata": {},
   "source": [
    "### 2.3.2 Calculating Total `Sales`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08f8b990-cba7-40dc-a22d-0ce281a397e9",
   "metadata": {},
   "source": [
    "Finally, for the last step, it's time to create columns **Sales** to know the total value for each transaction.\n",
    "\n",
    "To enhance the dataset with financial information, we calculate the total sales amount for each transaction by multiplying the `Quantity` by the `UnitPrice`. This new column, Sales, will help in analyzing revenue and transaction values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "08f2789e-a7b8-4495-89c1-fe6a96314148",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>Date</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>month_year</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>2010</td>\n",
       "      <td>Dec</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>536365</td>\n",
       "      <td>21730</td>\n",
       "      <td>GLASS STAR FROSTED T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>4.25</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>2010</td>\n",
       "      <td>Dec</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12</td>\n",
       "      <td>25.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>536366</td>\n",
       "      <td>22633</td>\n",
       "      <td>HAND WARMER UNION JACK</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:28:00</td>\n",
       "      <td>1.85</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>2010</td>\n",
       "      <td>Dec</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12</td>\n",
       "      <td>11.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>536366</td>\n",
       "      <td>22632</td>\n",
       "      <td>HAND WARMER RED POLKA DOT</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:28:00</td>\n",
       "      <td>1.85</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>2010</td>\n",
       "      <td>Dec</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12</td>\n",
       "      <td>11.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>536367</td>\n",
       "      <td>22745</td>\n",
       "      <td>POPPY'S PLAYHOUSE BEDROOM</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:34:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>13047.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>2010</td>\n",
       "      <td>Dec</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12</td>\n",
       "      <td>12.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   InvoiceNo StockCode                        Description  Quantity  \\\n",
       "1     536365     71053                WHITE METAL LANTERN         6   \n",
       "6     536365     21730  GLASS STAR FROSTED T-LIGHT HOLDER         6   \n",
       "7     536366     22633             HAND WARMER UNION JACK         6   \n",
       "8     536366     22632          HAND WARMER RED POLKA DOT         6   \n",
       "10    536367     22745          POPPY'S PLAYHOUSE BEDROOM         6   \n",
       "\n",
       "           InvoiceDate  UnitPrice CustomerID         Country       Date  Year  \\\n",
       "1  2010-12-01 08:26:00       3.39    17850.0  United Kingdom 2010-12-01  2010   \n",
       "6  2010-12-01 08:26:00       4.25    17850.0  United Kingdom 2010-12-01  2010   \n",
       "7  2010-12-01 08:28:00       1.85    17850.0  United Kingdom 2010-12-01  2010   \n",
       "8  2010-12-01 08:28:00       1.85    17850.0  United Kingdom 2010-12-01  2010   \n",
       "10 2010-12-01 08:34:00       2.10    13047.0  United Kingdom 2010-12-01  2010   \n",
       "\n",
       "   Month        Day  Hour month_year  Sales  \n",
       "1    Dec  Wednesday     8    2010-12  20.34  \n",
       "6    Dec  Wednesday     8    2010-12  25.50  \n",
       "7    Dec  Wednesday     8    2010-12  11.10  \n",
       "8    Dec  Wednesday     8    2010-12  11.10  \n",
       "10   Dec  Wednesday     8    2010-12  12.60  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['Sales'] = new_df['Quantity'] * new_df['UnitPrice']\n",
    "\n",
    "# Displaying the first few rows to verify the new column\n",
    "new_df.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b0b8975-8e25-4855-ab60-ef95dea7e57f",
   "metadata": {},
   "source": [
    "# **3.0 Exploratory Analysis**\n",
    "---------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8a610e5-4b89-4bf4-b90a-aead2101cfb3",
   "metadata": {},
   "source": [
    "**Exploratory Data Analysis (EDA)** is a critical step in understanding the characteristics and underlying patterns of a dataset. This phase involves summarizing the main features of the data, often through **visualizations** and **statistical measures**, to uncover insights and guide further data preprocessing and analysis.\n",
    "\n",
    "`In this section`, we will perform EDA on our dataset to gain a comprehensive understanding of the transaction data. By leveraging various data visualization techniques, such as plots and charts, we aim to identify trends, detect anomalies, and highlight key patterns. This process will help us to better grasp the data's structure, distribution, and relationships, setting the stage for more advanced analyses and modeling."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21f1aefe-90f2-47ae-8125-29ea82b4e4cc",
   "metadata": {},
   "source": [
    "## 3.1 Sales and Quantity Analysis by Country\n",
    "----------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb58c9d8-c07a-4609-9eb9-b20f3f95bc26",
   "metadata": {},
   "source": [
    "To understand how sales and quantity vary across different countries, we aggregate the data by country. This analysis will provide insights into the total number of orders, total sales, and total quantity sold for each country."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "78d74d99-ca3a-4cb2-a0d5-3228233bba48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                Total_Order       Sales  Quantity\n",
      "Country                                          \n",
      "United Kingdom       272708  3352647.62   1970315\n",
      "Germany                6934   126673.74     78420\n",
      "France                 6535   115502.90     72293\n",
      "EIRE                   4920    95928.40     53805\n",
      "Spain                  1849    27338.06     16945\n",
      "Switzerland            1313    26943.79     16635\n",
      "Belgium                1527    25941.26     16325\n",
      "Portugal               1176    18982.35     11214\n",
      "Norway                  717    16711.56      8772\n",
      "Netherlands             576    13980.84      6703\n"
     ]
    }
   ],
   "source": [
    "Sales_Country = new_df.groupby(['Country']).agg({\n",
    "    'InvoiceNo': ['nunique'],                              # Count of unique invoices\n",
    "    'InvoiceNo': 'count',                                  # Total number of transactions\n",
    "    'Sales': 'sum',                                        # Total sales amount\n",
    "    'Quantity': 'sum'                                      # Total quantity sold\n",
    "}).rename(columns={'InvoiceNo': 'Total_Order'}).sort_values('Sales', ascending=False)\n",
    "\n",
    "# Displaying the top 10 countries by sales\n",
    "print(Sales_Country.head(10))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0695edb6-b2be-4ccb-9339-a02308398b40",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "To visualize the distribution of sales, quantity, and total orders across different countries, we use **pie charts**. Each chart provides a visual representation of the proportion of each country’s contribution to the total sales, quantity, and number of orders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ab5a5590-fd0a-4ebe-a515-5e5c296c64f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(10,6))\n",
    "\n",
    "def autopct(pct):\n",
    "    return ('%1.2f%%' % pct) if pct > 80 else ''\n",
    "\n",
    "# Pie chart for Sales by Country\n",
    "p, t, a = ax1.pie(Sales_Country.Sales, autopct=autopct, colors=plt.cm.Paired(range(len(Sales_Country))))\n",
    "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "ax1.set_title('Sales')\n",
    "\n",
    "p, t, a = ax2.pie(Sales_Country.Quantity, autopct=autopct, colors=plt.cm.Paired(range(len(Sales_Country))))\n",
    "ax2.axis('equal')\n",
    "ax2.set_title('Quantity')\n",
    "\n",
    "p, t, a = ax3.pie(Sales_Country.Total_Order, autopct=autopct, colors=plt.cm.Paired(range(len(Sales_Country))))\n",
    "ax3.axis('equal')\n",
    "ax3.set_title('Total Order')\n",
    "\n",
    "# Normalize dataframe for legend\n",
    "normsizes = Sales_Country.Sales / Sales_Country.Sales.sum() * 100\n",
    "normsizes2 = Sales_Country.Quantity / Sales_Country.Quantity.sum() * 100\n",
    "normsizes3 = Sales_Country.Total_Order / Sales_Country.Total_Order.sum() * 100\n",
    "\n",
    "# Create handles and labels for legend, taking only those where value is > 1%\n",
    "h, l = zip(*[(h, lab) for h, lab, i in zip(p, Sales_Country.index.values, normsizes) if i > 80])\n",
    "h, j = zip(*[(h, lab) for h, lab, i in zip(p, Sales_Country.index.values, normsizes2) if i > 80])\n",
    "h, k = zip(*[(h, lab) for h, lab, i in zip(p, Sales_Country.index.values, normsizes3) if i > 80])\n",
    "\n",
    "# Add legends to each pie chart\n",
    "ax1.legend(h, l, loc=\"best\", bbox_to_anchor=(1, 1))\n",
    "ax2.legend(h, j, loc=\"best\", bbox_to_anchor=(1, 1))\n",
    "ax3.legend(h, k, loc=\"best\", bbox_to_anchor=(1, 1))\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b6dadb3-6e6e-4af6-85b3-5e8c9b7bd201",
   "metadata": {},
   "source": [
    "#### Note:\n",
    "\n",
    "From the previous chart, we observed that over **80%** of transactions, in terms of total orders, sales, and quantity, are concentrated in the United Kingdom (UK) or domestic transactions. Now, let's identify which foreign markets are the most promising for online retail."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d882b6a0-8dbd-4b4c-b76f-9bac4859b2bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Total_Order</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Germany</td>\n",
       "      <td>6934</td>\n",
       "      <td>126673.74</td>\n",
       "      <td>78420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>France</td>\n",
       "      <td>6535</td>\n",
       "      <td>115502.90</td>\n",
       "      <td>72293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EIRE</td>\n",
       "      <td>4920</td>\n",
       "      <td>95928.40</td>\n",
       "      <td>53805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Spain</td>\n",
       "      <td>1849</td>\n",
       "      <td>27338.06</td>\n",
       "      <td>16945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>1313</td>\n",
       "      <td>26943.79</td>\n",
       "      <td>16635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>1527</td>\n",
       "      <td>25941.26</td>\n",
       "      <td>16325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>1176</td>\n",
       "      <td>18982.35</td>\n",
       "      <td>11214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Norway</td>\n",
       "      <td>717</td>\n",
       "      <td>16711.56</td>\n",
       "      <td>8772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>576</td>\n",
       "      <td>13980.84</td>\n",
       "      <td>6703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Australia</td>\n",
       "      <td>467</td>\n",
       "      <td>11709.71</td>\n",
       "      <td>4876</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Country  Total_Order      Sales  Quantity\n",
       "1       Germany         6934  126673.74     78420\n",
       "2        France         6535  115502.90     72293\n",
       "3          EIRE         4920   95928.40     53805\n",
       "4         Spain         1849   27338.06     16945\n",
       "5   Switzerland         1313   26943.79     16635\n",
       "6       Belgium         1527   25941.26     16325\n",
       "7      Portugal         1176   18982.35     11214\n",
       "8        Norway          717   16711.56      8772\n",
       "9   Netherlands          576   13980.84      6703\n",
       "10    Australia          467   11709.71      4876"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Foreign_Sales = Sales_Country.reset_index().query('Country != \"United Kingdom\"')\n",
    "Foreign_Sales.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "251d803f-4c9d-4c8c-892e-4f8801530634",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "\n",
    "After observing that most transactions originate from the United Kingdom (domestic transactions), the goal now is to examine foreign markets. The code extracts data for all **non-UK countries** and identifies the top-performing ones based on sales, quantity, and total orders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "29308324-6bd1-41a7-bc9b-ec866d52482f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(10,6))\n",
    "\n",
    "def autopct(pct):\n",
    "    return ('%1.2f%%' % pct) if pct > 15 else ''\n",
    "\n",
    "# Define custom colors for each pie chart\n",
    "colors1 = ['#ff9999','#66b3ff','#99ff99','#ffcc99','#c2c2f0','#ffb3e6']\n",
    "colors2 = ['#66b3ff','#ff9999','#ffcc99','#99ff99','#c2c2f0','#ffb3e6']\n",
    "colors3 = ['#ffcc99','#99ff99','#66b3ff','#ff9999','#c2c2f0','#ffb3e6']\n",
    "\n",
    "p,t,a = ax1.pie(Foreign_Sales.Sales, autopct=autopct, colors=colors1)\n",
    "p,t,a = ax2.pie(Foreign_Sales.Quantity, autopct=autopct, colors=colors2)\n",
    "p,t,a = ax3.pie(Foreign_Sales.Total_Order, autopct=autopct, colors=colors3)\n",
    "ax1.axis('equal')\n",
    "ax2.axis('equal')\n",
    "ax3.axis('equal')\n",
    "\n",
    "# normalize dataframe (not actually needed here, but for general case)\n",
    "normsizes = Foreign_Sales.Sales/Foreign_Sales.Sales.sum()*100\n",
    "normsizes2 = Foreign_Sales.Quantity/Foreign_Sales.Quantity.sum()*100\n",
    "normsizes3 = Foreign_Sales.Total_Order/Foreign_Sales.Total_Order.sum()*100\n",
    "\n",
    "# create handles and labels for legend, take only those where value is > 15\n",
    "h,l = zip(*[(h,lab) for h,lab,i in zip(p,Foreign_Sales.Country.values,normsizes.values) if i > 15])\n",
    "h,j = zip(*[(h,lab) for h,lab,i in zip(p,Foreign_Sales.Country.values,normsizes2.values) if i > 15])\n",
    "h,k = zip(*[(h,lab) for h,lab,i in zip(p,Foreign_Sales.Country.values,normsizes3.values) if i > 15])\n",
    "ax1.set_title('Sales')\n",
    "ax2.set_title('Quantity')\n",
    "ax3.set_title('Total Order')\n",
    "\n",
    "ax1.legend(h, l,loc=\"best\", bbox_to_anchor=(1,1))\n",
    "ax2.legend(h, j,loc=\"best\", bbox_to_anchor=(1,1))\n",
    "ax3.legend(h, k,loc=\"best\", bbox_to_anchor=(1,1))\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8869b67f-8a8f-489d-87a6-283cea32a961",
   "metadata": {},
   "source": [
    "The top **3** foreign transaction countries are Germany, France, and Ireland."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0a373a9-48f6-4788-a336-2a07bdd618e8",
   "metadata": {},
   "source": [
    "# **4.0 Sales MoM**\n",
    "-----------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0e215fe-4731-4ffb-bb7d-23b9bfe5912a",
   "metadata": {},
   "source": [
    "In this section, we will analyze the **month-over-month (MoM)** growth in sales and product quantity. Tracking MoM growth helps identify trends and patterns in customer purchasing behavior, allowing for a clearer understanding of sales performance over time. By evaluating the fluctuation in sales and product quantity across different months, we can gain insights into seasonal demand, promotional impacts, and potential growth opportunities for the business."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "21d69172-7bec-4f67-8450-3e84a0e40abe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Sales_MoM = pd.DataFrame(new_df.groupby(['month_year']).agg({'Sales':'sum', 'Quantity':'sum'})).reset_index()\n",
    "Sales_MoM.plot(x='month_year');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe1258f8-7262-41da-8eb3-9ac0c1598620",
   "metadata": {},
   "source": [
    "From the previous chart, we can observe that growth began around August and peaked in November 2011."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c720f6b1-d3bd-4721-b46c-afa39b6db1b1",
   "metadata": {},
   "source": [
    "## 4.1 Top Sales by Product\n",
    "----------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f199c763-3ef6-42bd-be19-7d3c67a04e53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                  Total_Order     Sales\n",
      "Description                                            \n",
      "PARTY BUNTING                            1268  34952.85\n",
      "SPOTTY BUNTING                            972  23379.40\n",
      "ASSORTED COLOUR BIRD ORNAMENT            1144  22848.80\n",
      "JAM MAKING SET WITH JARS                  824  21137.25\n",
      "HOT WATER BOTTLE KEEP CALM                723  20617.35\n",
      "SET OF 3 CAKE TINS PANTRY DESIGN         1113  20583.97\n",
      "CHILLI LIGHTS                             443  18379.80\n",
      "CHOCOLATE HOT WATER BOTTLE                684  18308.10\n",
      "RETROSPOT TEA SET CERAMIC 11 PC           806  17941.60\n",
      "NATURAL SLATE HEART CHALKBOARD            936  17750.77\n",
      "\n",
      "------------------------------------------------------------\n",
      "\n",
      "                                    Total_Order  Quantity\n",
      "Description                                              \n",
      "PACK OF 72 RETROSPOT CAKE CASES             945     14986\n",
      "ASSORTED COLOUR BIRD ORNAMENT              1144     13520\n",
      "LUNCH BAG RED RETROSPOT                    1195      9776\n",
      "JAM MAKING SET PRINTED                      809      8671\n",
      "PACK OF 60 PINK PAISLEY CAKE CASES          601      8498\n",
      "60 TEATIME FAIRY CAKE CASES                 617      8376\n",
      "VICTORIAN GLASS HANGING T-LIGHT             661      7601\n",
      "LUNCH BAG SUKI DESIGN                      1003      7478\n",
      "RED RETROSPOT WRAP                          299      7447\n",
      "LUNCH BAG BLACK SKULL                      1021      7328\n"
     ]
    }
   ],
   "source": [
    "# Top Sales by Product\n",
    "Sales_Product = new_df.groupby(['Description']).agg({'InvoiceNo': ['nunique'],\n",
    "                                      'InvoiceNo': 'count', 'Sales':'sum'}).rename(columns={'InvoiceNo':'Total_Order'}).sort_values('Sales', ascending=False)[0:10]\n",
    "\n",
    "# Top Quantity by Product\n",
    "Quantity_Product = new_df.groupby(['Description']).agg({'InvoiceNo': ['nunique'],\n",
    "                                      'InvoiceNo': 'count', 'Quantity':'sum'}).rename(columns={'InvoiceNo':'Total_Order'}).sort_values('Quantity', ascending=False)[0:10]\n",
    "\n",
    "\n",
    "\n",
    "# Print the top sales and quantity by product\n",
    "print(Sales_Product)\n",
    "\n",
    "print(\"\\n\" + \"-\"*60 + \"\\n\")\n",
    "\n",
    "print(Quantity_Product)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3966d8c8-bc47-4f09-80b4-610b3ee44e13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2,figsize=(8, 10))\n",
    "\n",
    "# Plot Sales by Product\n",
    "sns.barplot(x='Sales', y=Sales_Product.index, data = Sales_Product,\n",
    "            label=\"Sales\", ax=axes[0])\n",
    "\n",
    "# Plot Quantity by Product\n",
    "sns.barplot(x='Quantity', y=Quantity_Product.index, data=Quantity_Product,\n",
    "            label=\"Quantity\", ax=axes[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bcbf99a-4189-41ae-8152-0132d1b4f0c5",
   "metadata": {},
   "source": [
    "#### Note:\n",
    "\n",
    "This is quite intriguing. The differences between the Top 10 products by sales and the Top 10 products by quantity indicate that higher quantities sold **do not necessarily** result in higher sales revenue. To gain more insight, we should analyze the Top 10 products that not only have high quantities sold but also generate the **highest revenue**.\n",
    "\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4e926392-8417-476d-b1f5-ed449ae05f51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Order_x</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Total_Order_y</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Description</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ASSORTED COLOUR BIRD ORNAMENT</th>\n",
       "      <td>1144</td>\n",
       "      <td>13520</td>\n",
       "      <td>1144</td>\n",
       "      <td>22848.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Total_Order_x  Quantity  Total_Order_y    Sales\n",
       "Description                                                                   \n",
       "ASSORTED COLOUR BIRD ORNAMENT           1144     13520           1144  22848.8"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Quantity_Sales = Quantity_Product.merge(Sales_Product, left_index=True, right_index=True, how='inner')\n",
    "Quantity_Sales"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8df3ecb-1cda-45c8-b5bf-8fb52d445022",
   "metadata": {},
   "source": [
    "#### Note:\n",
    "\n",
    "Indeed, the analysis reveals that **ASSORTED COLOUR BIRD ORNAMENT** stands out as the only product that ranks highest in both sales revenue and quantity sold. This finding highlights that this particular product not only generates the **highest sales** but also enjoys the **greatest demand** in the market. Retailers should focus on this product, as its strong performance in both sales and quantity indicates a significant market demand.\n",
    "\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d34a35b-31bc-4f08-ba01-b8a5c24e7bef",
   "metadata": {},
   "source": [
    "## 4.3 Highest and Lowest Month of Sales\n",
    "-----------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c17e253-0a94-49f5-9059-30daab6c7a61",
   "metadata": {},
   "source": [
    "In the previous analysis, we observed that **November** exhibited the highest peak in transactions. To gain a clearer understanding of the sales patterns throughout the year, it's essential to identify the months that generated both the highest and lowest sales and quantities. This analysis will help us pinpoint specific months of peak performance and those with lower sales activity, offering valuable insights into seasonal trends and overall performance throughout the year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6313616d-d95f-44ad-a5ec-9ee9a6b00d47",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Monthly_Sales = new_df.groupby(['Month']).agg({'Sales':'sum', 'Quantity':'sum'})\n",
    "\n",
    "# Reordering months to ensure correct sequence in the plot\n",
    "months_ordered = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
    "Monthly_Sales = Monthly_Sales.reindex(months_ordered)\n",
    "\n",
    "# Creating subplots for visualizing sales and quantity by month\n",
    "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n",
    "\n",
    "sns.barplot(x=Monthly_Sales.index, y='Sales', data = Monthly_Sales,\n",
    "            label=\"Sales\", ax=axes[0])\n",
    "sns.barplot(x=Monthly_Sales.index, y='Quantity', data = Monthly_Sales,\n",
    "            label=\"Quantity\", ax=axes[1])\n",
    "plt.show()\n",
    "     "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ad161b8-75f0-492b-8adf-e42531e8d433",
   "metadata": {},
   "source": [
    "## 4.4 Highest Sales of Day\n",
    "---------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7d453b0-9703-4bc8-a5da-54fee6b62101",
   "metadata": {},
   "source": [
    "To gain further insights into sales patterns, we'll examine how sales and quantity vary by **day of the week**. Rather than focusing on total sales, we'll calculate the average sales and quantity for each day. It's important to note that there are no transactions on Saturdays. This analysis will help us identify which days of the week typically generate the highest average sales and quantities, providing a clearer understanding of daily sales performance and trends."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "63be5984-ea32-4937-a261-115eed73ae9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Day_Sales = new_df.groupby(['Day']).agg({'Sales':'mean', 'Quantity':'mean'})\n",
    "day_ordered = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Sunday']\n",
    "Day_Sales = Day_Sales.reindex(day_ordered)\n",
    "fig, axes = plt.subplots(1,2,figsize=(15,5))\n",
    "\n",
    "# Plotting the average sales by day of the week\n",
    "sns.barplot(x=Day_Sales.index, y='Sales', data = Day_Sales,\n",
    "            label=\"Sales\", ax=axes[0])\n",
    "\n",
    "# Plotting the average quantity sold by day of the week\n",
    "sns.barplot(x=Day_Sales.index, y='Quantity', data = Day_Sales,\n",
    "            label=\"Quantity\", ax=axes[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0654f462-7a00-4f24-b81f-45e3b61e3bd4",
   "metadata": {},
   "source": [
    "It’s evident that **Friday** is the most popular day for shopping, while **Sunday** is the least favored day for transactions.\n",
    "\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4028f9c-9c7d-4fb0-8cc0-d8d5a599baac",
   "metadata": {},
   "source": [
    "## 4.5 Hourly based highest sales"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55eace6b-5d75-4a06-850d-1e519dc9920c",
   "metadata": {},
   "source": [
    "Now, we delve into the sales performance on an **hourly basis**. By examining the average sales and quantity of transactions throughout different hours of the day, we aim to identify peak shopping hours and understand customer purchasing behavior more granularly. This analysis helps in optimizing store operations and marketing strategies by focusing efforts during the most profitable times of the day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1807a048-a960-4eb5-b86a-cdd2233b465b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Average sales and quantity by hour of the day\n",
    "Hour_Sales = new_df.groupby(['Hour']).agg({'Sales':'mean', 'Quantity':'mean'})\n",
    "fig, axes = plt.subplots(1,2,figsize=(15,5))\n",
    "sns.barplot(x=Hour_Sales.index, y='Sales', data = Hour_Sales,\n",
    "            label=\"Sales\", ax=axes[0])\n",
    "sns.barplot(x=Hour_Sales.index, y='Quantity', data = Hour_Sales,\n",
    "            label=\"Quantity\", ax=axes[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0cbb497-3159-438a-bac1-bffa728a7ca5",
   "metadata": {},
   "source": [
    "The peak period for transactions is in the **morning**, particularly around **7 a.m. and from 8 to 10 a.m**.\n",
    "\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5964ceda-1b63-4c2a-a5d4-478432d37305",
   "metadata": {},
   "source": [
    "# **5. Customer**\n",
    "-----------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46249ac5-8670-4c69-9d82-bddd3737f5ef",
   "metadata": {},
   "source": [
    "#### Customer Demography\n",
    "\n",
    "By analyzing customer data, we aim to gain insights into various demographic attributes, such as geographical location and purchasing behavior. This understanding will help identify key customer segments, tailor marketing strategies, and enhance customer experience based on their demographic profiles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3a6ccc4c-8593-401a-a365-927a16f78d70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                CustomerID\n",
      "Country                   \n",
      "United Kingdom      272708\n",
      "Germany               6934\n",
      "France                6535\n",
      "EIRE                  4920\n",
      "Spain                 1849\n",
      "Belgium               1527\n",
      "Switzerland           1313\n",
      "Portugal              1176\n",
      "Norway                 717\n",
      "Netherlands            576\n",
      "\n",
      "------------------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Customer_Demog = new_df.groupby('Country').agg({'CustomerID': ['nunique'],\n",
    "                                      'CustomerID': 'count'})\n",
    "print(Customer_Demog.sort_values('CustomerID', ascending=False)[0:10])\n",
    "\n",
    "f, (ax) = plt.subplots(1, figsize=(5,5))\n",
    "def autopct(pct):\n",
    "    return ('%1.2f%%' % pct) if pct > 70 else ''\n",
    "\n",
    "p,t,a = ax.pie(Customer_Demog.CustomerID, autopct=autopct)\n",
    "ax.axis('equal')\n",
    "\n",
    "# normalize dataframe (not actually needed here, but for general case)\n",
    "normsizes = Customer_Demog.CustomerID/Customer_Demog.CustomerID.sum()*100\n",
    "\n",
    "# create handles and labels for legend, take only those where value is > 1\n",
    "h,l = zip(*[(h,lab) for h,lab,i in zip(p,Customer_Demog.index.values,normsizes.values) if i > 70])\n",
    "\n",
    "ax.set_title('Customer Demography')\n",
    "ax.legend(h, l,loc=\"best\", bbox_to_anchor=(1,1))\n",
    "\n",
    "print(\"\\n\" + \"-\"*60 + \"\\n\")\n",
    "\n",
    "plt.show()\n",
    "     "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27677ae2-d719-4f53-be18-b437d48826d2",
   "metadata": {},
   "source": [
    "Based on the previous chart, we observed that a significant majority of customers—over **80%** are from the United Kingdom (UK) or domestic sources. Now, let's investigate the distribution of foreign customers to identify the best foreign markets for online retail.\n",
    "\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "82a6f75d-e5a7-46f2-a97b-ecd94ee01fc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>CustomerID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Germany</td>\n",
       "      <td>6934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>France</td>\n",
       "      <td>6535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>EIRE</td>\n",
       "      <td>4920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Spain</td>\n",
       "      <td>1849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>1527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>1313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>1176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Norway</td>\n",
       "      <td>717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Italy</td>\n",
       "      <td>570</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Country  CustomerID\n",
       "14      Germany        6934\n",
       "13       France        6535\n",
       "10         EIRE        4920\n",
       "30        Spain        1849\n",
       "3       Belgium        1527\n",
       "32  Switzerland        1313\n",
       "26     Portugal        1176\n",
       "24       Norway         717\n",
       "23  Netherlands         576\n",
       "18        Italy         570"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Foreign_Demog = Customer_Demog.reset_index().query('Country != \"United Kingdom\"')\n",
    "Foreign_Demog.sort_values('CustomerID', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b4542ff-8acc-45c8-bdc0-efe0725840d9",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "Noow, we are going to visualize the distribution of customers from different countries who have made purchases in the online retailer shop as shown in the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fb7f52d7-0047-4804-8362-5203ae3f5bd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize the distribution of customers by country using a pie chart.\n",
    "\n",
    "f, ax = plt.subplots(1, figsize=(9,6))\n",
    "\n",
    "def autopct(pct):\n",
    "    return ('%1.2f%%' % pct) if pct > 15 else ''\n",
    "\n",
    "p,t,a = ax.pie(Foreign_Demog.CustomerID, autopct=autopct)\n",
    "ax.axis('equal')\n",
    "\n",
    "# normalize dataframe (This is not actually needed here, but for general case)\n",
    "normsizes = Foreign_Demog.CustomerID/Foreign_Demog.CustomerID.sum()*100\n",
    "\n",
    "# create handles and labels for legend, take only those where value is > 1\n",
    "h,l = zip(*[(h,lab) for h,lab,i in zip(p,Foreign_Demog.Country.values,normsizes.values) if i > 15])\n",
    "ax.set_title('Total Customer')\n",
    "ax.legend(h, l,loc=\"best\", bbox_to_anchor=(1,1))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "822f4a69-6d5b-4bd7-92bc-2b18565bd8d2",
   "metadata": {},
   "source": [
    "Just like with transactions and quantities, the top foreign customers are predominantly from **Germany**, **France**, and **Ireland**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66f14bfd-ed55-4741-b25b-60258bc58021",
   "metadata": {},
   "source": [
    "## 5.1 Top Customer\n",
    "-----------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16f4548e-92cd-434c-b1c6-5183e0a12ad2",
   "metadata": {},
   "source": [
    "In this section, we will identify the top customers who have contributed the most to **sales and quantity**. By analyzing the data, we aim to pinpoint which customers have generated the highest sales revenue and purchased the largest quantities, providing valuable insights into customer behavior and purchasing patterns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "860421c2-f43d-4cac-9449-742d0bdd07cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               Sales\n",
      "CustomerID          \n",
      "14911.0     76424.67\n",
      "13089.0     32957.08\n",
      "14096.0     32949.41\n",
      "17841.0     29176.25\n",
      "14298.0     20823.48\n",
      "12748.0     18388.33\n",
      "14156.0     17179.68\n",
      "13081.0     16012.81\n",
      "15311.0     14220.87\n",
      "17675.0     13717.15\n"
     ]
    }
   ],
   "source": [
    "Sales_Customer = new_df.groupby(['CustomerID']).agg({'Sales':'sum'}).sort_values('Sales', ascending=False)[0:10]\n",
    "Quantity_Customer = new_df.groupby(['CustomerID']).agg({'Quantity':'sum'}).sort_values('Quantity', ascending=False)[0:10]\n",
    "print(Sales_Customer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1a90189e-6bc3-4533-b315-65ab2155ad41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Quantity\n",
      "CustomerID          \n",
      "14911.0        45072\n",
      "13089.0        18858\n",
      "17841.0        17602\n",
      "14298.0        14482\n",
      "12748.0        13372\n",
      "14096.0        11819\n",
      "13081.0        11129\n",
      "14156.0         7422\n",
      "17735.0         7197\n",
      "12921.0         7174\n"
     ]
    }
   ],
   "source": [
    "print(Quantity_Customer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56fd68be-56a7-40a0-8f4f-3a210700c0e7",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "\n",
    "Now, let's visualize the top customers based on their **total sales** and **quantity of purchases**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2836b1f6-3598-41a7-8818-b4989b27db0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2,figsize=(8, 10))\n",
    "\n",
    "# based on total sales\n",
    "sns.barplot(x='Sales', y=Sales_Customer.index, data = Sales_Customer,\n",
    "            label=\"Sales\", ax=axes[0])\n",
    "\n",
    "# based on quantity of purchases\n",
    "sns.barplot(x='Quantity', y=Quantity_Customer.index, data=Quantity_Customer,\n",
    "            label=\"Quantity\", ax=axes[1])\n",
    "plt.show()\n",
    "     "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec52e39f-d5f1-42ee-9879-508b2dd2c00b",
   "metadata": {},
   "source": [
    "This shows that the customer with the **Customer ID: 14911** is the top customer based on their totals product sales and quality of purcheses.\n",
    "\n",
    "<br>\n",
    "\n",
    "Now, let's find out that Who is the customer that generated **BOTH** the highest quantity and sales?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e6c342ad-8c89-4459-bc5f-9f2a0f13c5f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14911.0</th>\n",
       "      <td>45072</td>\n",
       "      <td>76424.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13089.0</th>\n",
       "      <td>18858</td>\n",
       "      <td>32957.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17841.0</th>\n",
       "      <td>17602</td>\n",
       "      <td>29176.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14298.0</th>\n",
       "      <td>14482</td>\n",
       "      <td>20823.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12748.0</th>\n",
       "      <td>13372</td>\n",
       "      <td>18388.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14096.0</th>\n",
       "      <td>11819</td>\n",
       "      <td>32949.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13081.0</th>\n",
       "      <td>11129</td>\n",
       "      <td>16012.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14156.0</th>\n",
       "      <td>7422</td>\n",
       "      <td>17179.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Quantity     Sales\n",
       "CustomerID                    \n",
       "14911.0        45072  76424.67\n",
       "13089.0        18858  32957.08\n",
       "17841.0        17602  29176.25\n",
       "14298.0        14482  20823.48\n",
       "12748.0        13372  18388.33\n",
       "14096.0        11819  32949.41\n",
       "13081.0        11129  16012.81\n",
       "14156.0         7422  17179.68"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Quantity_Sales_Cust = Quantity_Customer.merge(Sales_Customer, left_index=True, right_index=True, how='inner')\n",
    "Quantity_Sales_Cust"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccc18105-9445-49d7-aef9-777f3d33fc8e",
   "metadata": {},
   "source": [
    "It's fascinating to see that **8** customers are responsible for **both** the highest quantity purchases and the greatest sales revenue."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a921daed-c755-4262-b3e1-f7823974f9d7",
   "metadata": {},
   "source": [
    "# **6.0 Order vs Sales**\n",
    "-------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34167ec8-90cc-42b0-9123-fd6c72923891",
   "metadata": {},
   "source": [
    "In this section, we will analyze the relationship between the number of orders and the total sales revenue generated by customers. It is common for a single order to include multiple products. By identifying the top **10 customers** with the highest total number of orders, we can also determine the sales revenue they have generated. This analysis will provide insights into how order volume correlates with revenue and highlight key customers contributing significantly to sales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b0b4c030-f728-49d7-ae2c-d45905bf9c21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Order</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17841.0</th>\n",
       "      <td>6504</td>\n",
       "      <td>29176.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14911.0</th>\n",
       "      <td>4042</td>\n",
       "      <td>76424.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14096.0</th>\n",
       "      <td>3968</td>\n",
       "      <td>32949.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12748.0</th>\n",
       "      <td>3634</td>\n",
       "      <td>18388.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14606.0</th>\n",
       "      <td>2087</td>\n",
       "      <td>6845.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15311.0</th>\n",
       "      <td>1631</td>\n",
       "      <td>14220.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13263.0</th>\n",
       "      <td>1435</td>\n",
       "      <td>5970.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13089.0</th>\n",
       "      <td>1397</td>\n",
       "      <td>32957.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15039.0</th>\n",
       "      <td>1086</td>\n",
       "      <td>11738.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18118.0</th>\n",
       "      <td>1054</td>\n",
       "      <td>3957.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Total_Order     Sales\n",
       "CustomerID                       \n",
       "17841.0            6504  29176.25\n",
       "14911.0            4042  76424.67\n",
       "14096.0            3968  32949.41\n",
       "12748.0            3634  18388.33\n",
       "14606.0            2087   6845.35\n",
       "15311.0            1631  14220.87\n",
       "13263.0            1435   5970.94\n",
       "13089.0            1397  32957.08\n",
       "15039.0            1086  11738.60\n",
       "18118.0            1054   3957.69"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Customer_Order = new_df.groupby('CustomerID').agg({'InvoiceNo': ['nunique'],\n",
    "                                      'InvoiceNo': 'count', 'Sales':'sum'}).rename(columns={'InvoiceNo':'Total_Order'}).sort_values('Total_Order', ascending=False)[0:10]\n",
    "Customer_Order\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8de64bef-9255-41e5-903f-856280f94810",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define colors for the bars\n",
    "col1 = 'mediumorchid'\n",
    "col2 = 'lightsteelblue'\n",
    "\n",
    "# Create a new figure with a specific size\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Plot Sales data on the primary y-axis\n",
    "ax = sns.barplot(x=Customer_Order.index, y=Customer_Order.Sales, data=Customer_Order, label='Sales', color = col1)\n",
    "width_scale = 0.5\n",
    "for bar in ax.containers[0]:\n",
    "    bar.set_width(bar.get_width() * width_scale)\n",
    "    ax.legend(bbox_to_anchor=(1.02, 1.05), loc='upper left', borderaxespad=0)\n",
    "\n",
    "# Create a secondary y-axis for Total Orders\n",
    "ax2 = ax.twinx()\n",
    "sns.barplot(x =Customer_Order.index, y = Customer_Order.Total_Order, data=Customer_Order, alpha=0.7, color = col2, label = 'Total Order', ax=ax2)\n",
    "\n",
    "# Adjust the width and position of Total Order bars\n",
    "for bar in ax2.containers[0]:\n",
    "    x = bar.get_x()\n",
    "    w = bar.get_width()\n",
    "    bar.set_x(x + w * (1- width_scale))\n",
    "    bar.set_width(w * width_scale)\n",
    "    ax2.legend(bbox_to_anchor=(1.02, 0.98), loc='upper left', borderaxespad=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28380374-499a-4295-abb7-78d3a79c7296",
   "metadata": {},
   "source": [
    "From the previous chart, we observed that **not all customers** with high transaction volumes also generate high sales revenue. In the following analysis, we will identify which customers rank in both the Top 10 for total orders and the Top 10 for sales revenue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2260e837-5f11-4e56-8b3e-27949e69e7de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Order</th>\n",
       "      <th>Sales_x</th>\n",
       "      <th>Sales_y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17841.0</th>\n",
       "      <td>6504</td>\n",
       "      <td>29176.25</td>\n",
       "      <td>29176.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14911.0</th>\n",
       "      <td>4042</td>\n",
       "      <td>76424.67</td>\n",
       "      <td>76424.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14096.0</th>\n",
       "      <td>3968</td>\n",
       "      <td>32949.41</td>\n",
       "      <td>32949.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12748.0</th>\n",
       "      <td>3634</td>\n",
       "      <td>18388.33</td>\n",
       "      <td>18388.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15311.0</th>\n",
       "      <td>1631</td>\n",
       "      <td>14220.87</td>\n",
       "      <td>14220.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13089.0</th>\n",
       "      <td>1397</td>\n",
       "      <td>32957.08</td>\n",
       "      <td>32957.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Total_Order   Sales_x   Sales_y\n",
       "CustomerID                                 \n",
       "17841.0            6504  29176.25  29176.25\n",
       "14911.0            4042  76424.67  76424.67\n",
       "14096.0            3968  32949.41  32949.41\n",
       "12748.0            3634  18388.33  18388.33\n",
       "15311.0            1631  14220.87  14220.87\n",
       "13089.0            1397  32957.08  32957.08"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Order_Sales_Cust = Customer_Order.merge(Sales_Customer, left_index=True, right_index=True, how='inner')\n",
    "Order_Sales_Cust"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7bccd13-cf53-48aa-ab1d-9299ec8c64ef",
   "metadata": {},
   "source": [
    "It's quite fascinating that there are **6 customers** who not only place the highest number of orders but also generate the most sales revenue.\n",
    "\n",
    "<br>\n",
    "\n",
    "Next, let's examine the **Average Revenue Per User (ARPU)** to understand the revenue we earn per transaction. ARPU is a **key metric** that measures the profitability of a product by calculating the average revenue generated from each user or subscriber."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6af1bbd9-95ab-454d-a871-a84cc6344e7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--------------------------------------------------\n",
      "\n",
      "       Total_Order         Sales         arpu\n",
      "count  4173.000000   4173.000000  4173.000000\n",
      "mean     72.650132    939.617012    16.633622\n",
      "std     180.921991   1997.793054    11.446925\n",
      "min       1.000000      1.900000     0.735217\n",
      "25%      13.000000    179.440000     9.553939\n",
      "50%      33.000000    425.770000    15.995000\n",
      "75%      80.000000   1050.270000    19.379038\n",
      "max    6504.000000  76424.670000   166.800000\n",
      "\n",
      "--------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "customer_arpu = new_df.groupby('CustomerID').agg({'InvoiceNo': ['nunique'],\n",
    "                                      'InvoiceNo': 'count', 'Sales':'sum'}).rename(columns={'InvoiceNo':'Total_Order'})\n",
    "\n",
    "# Calculating the Average Revenue Per User (ARPU) by dividing total sales by the total number of orders for each customer.\n",
    "customer_arpu['arpu'] = customer_arpu.Sales / customer_arpu.Total_Order\n",
    "\n",
    "print(\"\\n\" + \"-\"*50 + \"\\n\")\n",
    "\n",
    "print(customer_arpu.describe())\n",
    "\n",
    "print(\"\\n\" + \"-\"*50 + \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "df0f3f8e-9a41-4b65-8dbf-33fe3db831ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--------------------------------------------------\n",
      "\n",
      "            Total_Order  Sales        arpu\n",
      "CustomerID                                \n",
      "16716.0               1  166.8  166.800000\n",
      "16319.0               3  404.4  134.800000\n",
      "14434.0               8  964.8  120.600000\n",
      "15269.0               1  118.8  118.800000\n",
      "13147.0               2  237.6  118.800000\n",
      "13261.0               6  670.5  111.750000\n",
      "13763.0               2  218.4  109.200000\n",
      "12882.0               9  924.6  102.733333\n",
      "15619.0               1   99.6   99.600000\n",
      "13188.0               1   99.6   99.600000\n",
      "\n",
      "--------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\" + \"-\"*50 + \"\\n\")\n",
    "\n",
    "print(customer_arpu.sort_values('arpu', ascending=False)[0:10])\n",
    "\n",
    "print(\"\\n\" + \"-\"*50 + \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f0a56ed-1491-4843-b190-866cf9f1d418",
   "metadata": {},
   "source": [
    "It's quite intriguing. The top **10 ARPU customers** are those who make relatively few transactions or orders. Next, let's examine the ARPU for the top 10 customers based on their sales."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5147f48-25f5-451c-8722-408cf986a441",
   "metadata": {},
   "source": [
    "## 6.1 Arpu TOP Customer\n",
    "-------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d896efd9-9eab-4942-8512-a698e4a32331",
   "metadata": {},
   "source": [
    "Now, we'll focus on identifying the top 10 customers based on their Average Revenue Per User (ARPU). By analyzing these top ARPU customers, we can gain insights into which customers generate the most revenue per transaction. This will help us understand the profitability of our highest-paying customers and their impact on overall sales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "87e68a1a-5151-4e13-9118-3549672defed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Order</th>\n",
       "      <th>Sales</th>\n",
       "      <th>arpu</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14911.0</th>\n",
       "      <td>4042</td>\n",
       "      <td>76424.67</td>\n",
       "      <td>18.907637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13089.0</th>\n",
       "      <td>1397</td>\n",
       "      <td>32957.08</td>\n",
       "      <td>23.591324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14096.0</th>\n",
       "      <td>3968</td>\n",
       "      <td>32949.41</td>\n",
       "      <td>8.303783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17841.0</th>\n",
       "      <td>6504</td>\n",
       "      <td>29176.25</td>\n",
       "      <td>4.485893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14298.0</th>\n",
       "      <td>852</td>\n",
       "      <td>20823.48</td>\n",
       "      <td>24.440704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12748.0</th>\n",
       "      <td>3634</td>\n",
       "      <td>18388.33</td>\n",
       "      <td>5.060080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14156.0</th>\n",
       "      <td>765</td>\n",
       "      <td>17179.68</td>\n",
       "      <td>22.457098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13081.0</th>\n",
       "      <td>777</td>\n",
       "      <td>16012.81</td>\n",
       "      <td>20.608507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15311.0</th>\n",
       "      <td>1631</td>\n",
       "      <td>14220.87</td>\n",
       "      <td>8.719111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17675.0</th>\n",
       "      <td>542</td>\n",
       "      <td>13717.15</td>\n",
       "      <td>25.308395</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Total_Order     Sales       arpu\n",
       "CustomerID                                  \n",
       "14911.0            4042  76424.67  18.907637\n",
       "13089.0            1397  32957.08  23.591324\n",
       "14096.0            3968  32949.41   8.303783\n",
       "17841.0            6504  29176.25   4.485893\n",
       "14298.0             852  20823.48  24.440704\n",
       "12748.0            3634  18388.33   5.060080\n",
       "14156.0             765  17179.68  22.457098\n",
       "13081.0             777  16012.81  20.608507\n",
       "15311.0            1631  14220.87   8.719111\n",
       "17675.0             542  13717.15  25.308395"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "Top_Arpu = customer_arpu.sort_values('Sales', ascending=False)[0:10]\n",
    "Top_Arpu\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "374c0877-941c-4068-9518-672269b16348",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Order</th>\n",
       "      <th>Sales</th>\n",
       "      <th>arpu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.00000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2411.200000</td>\n",
       "      <td>27184.97300</td>\n",
       "      <td>16.188253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2007.549573</td>\n",
       "      <td>18818.67204</td>\n",
       "      <td>8.505240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>542.000000</td>\n",
       "      <td>13717.15000</td>\n",
       "      <td>4.485893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>795.750000</td>\n",
       "      <td>16304.52750</td>\n",
       "      <td>8.407615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1514.000000</td>\n",
       "      <td>19605.90500</td>\n",
       "      <td>19.758072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3884.500000</td>\n",
       "      <td>32006.12000</td>\n",
       "      <td>23.307768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6504.000000</td>\n",
       "      <td>76424.67000</td>\n",
       "      <td>25.308395</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Total_Order        Sales       arpu\n",
       "count    10.000000     10.00000  10.000000\n",
       "mean   2411.200000  27184.97300  16.188253\n",
       "std    2007.549573  18818.67204   8.505240\n",
       "min     542.000000  13717.15000   4.485893\n",
       "25%     795.750000  16304.52750   8.407615\n",
       "50%    1514.000000  19605.90500  19.758072\n",
       "75%    3884.500000  32006.12000  23.307768\n",
       "max    6504.000000  76424.67000  25.308395"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "Top_Arpu.describe()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bcbc727-fa9e-4dcf-bd07-fd9cce8e8d39",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "\n",
    "Now, we will visualize the top **10 customers** with the **highest Average Revenue Per User (ARPU)**. The chart compares both the total sales and ARPU for these top customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "97ef3e1b-067c-48c2-bf73-71ef3d4545ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col1 = 'mediumorchid'\n",
    "col2 = 'lightsteelblue'\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "ax = sns.barplot(x=Top_Arpu.index, y=Top_Arpu.Sales, data=Top_Arpu, label='Sales', color = col1)\n",
    "width_scale = 0.5\n",
    "for bar in ax.containers[0]:\n",
    "    bar.set_width(bar.get_width() * width_scale)\n",
    "    ax.legend(bbox_to_anchor=(1.02, 1.05), loc='upper left', borderaxespad=0)\n",
    "\n",
    "ax2 = ax.twinx()\n",
    "sns.barplot(x =Top_Arpu.index, y =Top_Arpu.arpu, data=Top_Arpu, alpha=0.7, color = col2, label = 'arpu', ax=ax2)\n",
    "for bar in ax2.containers[0]:\n",
    "    x = bar.get_x()\n",
    "    w = bar.get_width()\n",
    "    bar.set_x(x + w * (1- width_scale))\n",
    "    bar.set_width(w * width_scale)\n",
    "    ax2.legend(bbox_to_anchor=(1.02, 0.98), loc='upper left', borderaxespad=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32117bec-4ad5-4f41-98b4-9056a2a32861",
   "metadata": {},
   "source": [
    "The chart reveals a variation in ARPU relative to sales generated, indicating that a higher ARPU does not necessarily correspond to higher total sales. Additionally, it suggests that customers with higher ARPU among the top 10 tend to purchase more expensive items.\n",
    "\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaffa4a4-302c-41d0-b524-0a9dc706481d",
   "metadata": {},
   "source": [
    "# **7.0 Customer Segmentation using RFM Analysis**\n",
    "-------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d41e83e-f7aa-416a-b6b1-7622452eb005",
   "metadata": {},
   "source": [
    "In this section, we will explore Customer Segmentation through `RFM (Recency, Frequency, and Monetary) analysis`. RFM analysis is a powerful marketing technique that helps businesses categorize their customers based on their transaction behaviors. By analyzing the recency, frequency, and monetary value of customer interactions, we can identify key customer segments and tailor marketing strategies accordingly.\n",
    "\n",
    "\n",
    "* **Recency** measures how recently a customer has made a purchase. This helps in identifying customers who are still actively engaged.\n",
    "* **Frequency** tracks how often a customer makes a purchase, indicating their loyalty and purchasing habits.\n",
    "* **Monetary** assesses how much money a customer spends, revealing their overall value to the business.\n",
    "\n",
    "\n",
    "By applying RFM analysis, businesses can segment customers into various groups, such as `high-value`, `medium-value`, and `low-value customers`. This segmentation allows for more targeted marketing efforts and better resource allocation, ultimately enhancing customer relationship management and driving sales growth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "73287c05-1e3f-4583-9ca9-3675ca69db64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>Date</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>month_year</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>2010</td>\n",
       "      <td>Dec</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>536365</td>\n",
       "      <td>21730</td>\n",
       "      <td>GLASS STAR FROSTED T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>4.25</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>2010</td>\n",
       "      <td>Dec</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12</td>\n",
       "      <td>25.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>536366</td>\n",
       "      <td>22633</td>\n",
       "      <td>HAND WARMER UNION JACK</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:28:00</td>\n",
       "      <td>1.85</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>2010</td>\n",
       "      <td>Dec</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12</td>\n",
       "      <td>11.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>536366</td>\n",
       "      <td>22632</td>\n",
       "      <td>HAND WARMER RED POLKA DOT</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:28:00</td>\n",
       "      <td>1.85</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>2010</td>\n",
       "      <td>Dec</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12</td>\n",
       "      <td>11.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>536367</td>\n",
       "      <td>22745</td>\n",
       "      <td>POPPY'S PLAYHOUSE BEDROOM</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:34:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>13047.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>2010</td>\n",
       "      <td>Dec</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12</td>\n",
       "      <td>12.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   InvoiceNo StockCode                        Description  Quantity  \\\n",
       "1     536365     71053                WHITE METAL LANTERN         6   \n",
       "6     536365     21730  GLASS STAR FROSTED T-LIGHT HOLDER         6   \n",
       "7     536366     22633             HAND WARMER UNION JACK         6   \n",
       "8     536366     22632          HAND WARMER RED POLKA DOT         6   \n",
       "10    536367     22745          POPPY'S PLAYHOUSE BEDROOM         6   \n",
       "\n",
       "           InvoiceDate  UnitPrice CustomerID         Country       Date  Year  \\\n",
       "1  2010-12-01 08:26:00       3.39    17850.0  United Kingdom 2010-12-01  2010   \n",
       "6  2010-12-01 08:26:00       4.25    17850.0  United Kingdom 2010-12-01  2010   \n",
       "7  2010-12-01 08:28:00       1.85    17850.0  United Kingdom 2010-12-01  2010   \n",
       "8  2010-12-01 08:28:00       1.85    17850.0  United Kingdom 2010-12-01  2010   \n",
       "10 2010-12-01 08:34:00       2.10    13047.0  United Kingdom 2010-12-01  2010   \n",
       "\n",
       "   Month        Day  Hour month_year  Sales  \n",
       "1    Dec  Wednesday     8    2010-12  20.34  \n",
       "6    Dec  Wednesday     8    2010-12  25.50  \n",
       "7    Dec  Wednesday     8    2010-12  11.10  \n",
       "8    Dec  Wednesday     8    2010-12  11.10  \n",
       "10   Dec  Wednesday     8    2010-12  12.60  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# import libraries\n",
    "import datetime as dt\n",
    "\n",
    "df_UK = new_df[new_df['Country'] == \"United Kingdom\"]\n",
    "df_UK.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0f60d3b4-f21b-4f3b-94a2-61d184627113",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(272708, 4)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp=['CustomerID', 'Quantity', 'InvoiceDate', 'Sales']\n",
    "RFM_data=df_UK[temp]\n",
    "RFM_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "cf39f60c-6fc0-4bff-b875-1a6451664af7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2010-12-01 08:26:00\n",
      "2011-12-09 12:49:00\n"
     ]
    }
   ],
   "source": [
    "print(df_UK['InvoiceDate'].min())\n",
    "print(df_UK['InvoiceDate'].max())\n",
    "     "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4c41531-7977-4c93-9aea-a71bec4e92ef",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "\n",
    "Let's prepare the **RFM (Recency, Frequency, Monetary)** table for customer segmentation analysis:\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a7c8489c-d132-495e-8da2-6c59fc000611",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary_value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12747.0</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>2029.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12748.0</th>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>18388.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12749.0</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>2600.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12820.0</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>905.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12821.0</th>\n",
       "      <td>214</td>\n",
       "      <td>3</td>\n",
       "      <td>75.72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            recency  frequency  monetary_value\n",
       "CustomerID                                    \n",
       "12747.0           2         10         2029.71\n",
       "12748.0           0         22        18388.33\n",
       "12749.0           3         10         2600.53\n",
       "12820.0           3         10          905.92\n",
       "12821.0         214          3           75.72"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NOW = dt.datetime(2011,12,10)           # Define the reference date for recency calculations\n",
    "\n",
    "#Convert ORDERDATE to datetime format.\n",
    "RFM_data['InvoiceDate'] = pd.to_datetime(RFM_data['InvoiceDate'])\n",
    "\n",
    "# Calculate the RFM metrics for each customer\n",
    "RFM_table=RFM_data.groupby('CustomerID').agg({'InvoiceDate': lambda x: (NOW - x.max()).days,\n",
    "                                                'Quantity': lambda x: len(x.unique()), \n",
    "                                                'Sales': lambda x: x.sum()}) \n",
    "\n",
    "RFM_table['InvoiceDate'] = RFM_table['InvoiceDate'].astype(int)\n",
    "\n",
    "\n",
    "# Rename columns for clarity\n",
    "RFM_table.rename(columns={'InvoiceDate': 'recency', \n",
    "                         'Quantity': 'frequency',\n",
    "                         'Sales': 'monetary_value'}, inplace=True)\n",
    "\n",
    "\n",
    "RFM_table.head()           # Display the first few rows of the RFM table\n",
    "     "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbbf77b1-be8f-4700-b770-bc99492ed5bb",
   "metadata": {},
   "source": [
    "#### Insights:\n",
    "\n",
    "\n",
    "* **Customer 12748.0:** Very recent transactions `(Recency = 0)`, high frequency `(22 transactions)`, and highest monetary value.\n",
    "* **Customer 12747.0:** Recent transactions `(Recency = 2)`, moderate frequency `(10 transactions)`, and substantial monetary value.\n",
    "* **Customer 12821.0:** Older transactions `(Recency = 214)`, low frequency `(3 transactions)`, and lower monetary value.\n",
    "\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "afc03ced-af05-4d12-93e7-3f58c36d8cd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>17.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>172.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>50.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>409.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <td>143.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1007.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      recency  frequency  monetary_value\n",
       "0.25     17.0        5.0          172.23\n",
       "0.50     50.0        7.0          409.91\n",
       "0.75    143.5       10.0         1007.75"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quantiles = RFM_table.quantile(q=[0.25,0.5,0.75])\n",
    "quantiles"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "feacfdae-9129-40be-8ce5-9a52b11826ad",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "\n",
    "In this section, we perform **RFM (Recency, Frequency, Monetary)** segmentation to categorize customers based on their purchasing behavior. The goal is to classify customers into different segments to better target marketing efforts.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a0e7fce5-c54a-472b-8c02-516dee7d067b",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Converting quantiles to a dictionary, easier to use.\n",
    "quantiles = quantiles.to_dict()\n",
    "\n",
    "##  RFM Segmentation ----\n",
    "RFM_Segment = RFM_table.copy()\n",
    "\n",
    "# Arguments (x = value, p = recency, monetary_value, frequency, k = quartiles dict)\n",
    "def R_Class(x,p,d):\n",
    "    if x <= d[p][0.25]:\n",
    "        return 4\n",
    "    elif x <= d[p][0.50]:\n",
    "        return 3\n",
    "    elif x <= d[p][0.75]: \n",
    "        return 2\n",
    "    else:\n",
    "        return 1\n",
    "    \n",
    "# Arguments (x = value, p = recency, monetary_value, frequency, k = quartiles dict)\n",
    "def FM_Class(x,p,d):\n",
    "    if x <= d[p][0.25]:\n",
    "        return 1\n",
    "    elif x <= d[p][0.50]:\n",
    "        return 2\n",
    "    elif x <= d[p][0.75]: \n",
    "        return 3\n",
    "    else:\n",
    "        return 4\n",
    "\n",
    "# Applying the classification functions to create quartile columns\n",
    "RFM_Segment['R_Quartile'] = RFM_Segment['recency'].apply(R_Class, args=('recency',quantiles,))\n",
    "RFM_Segment['F_Quartile'] = RFM_Segment['frequency'].apply(FM_Class, args=('frequency',quantiles,))\n",
    "RFM_Segment['M_Quartile'] = RFM_Segment['monetary_value'].apply(FM_Class, args=('monetary_value',quantiles,))\n",
    "\n",
    "# Combining the quartile columns to form the RFM class\n",
    "RFM_Segment['RFMClass'] = RFM_Segment.R_Quartile.map(str) \\\n",
    "                            + RFM_Segment.F_Quartile.map(str) \\\n",
    "                            + RFM_Segment.M_Quartile.map(str)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "182fd989-416f-4554-a2b8-f3af283e8fea",
   "metadata": {},
   "source": [
    "# **8.0 Who are the lost customers?**\n",
    "--------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67c4d1e6-bed5-443c-8ab3-f2d09a3dd62e",
   "metadata": {},
   "source": [
    "Now, we will identify customers who have been classified as `\"lost\"` based on their `RFM (Recency, Frequency, Monetary) scores`. These are customers whose recent activity, purchase frequency, and spending are all low. By focusing on the RFM class '111', which signifies low values across all three metrics, we can pinpoint the customers who are `least engaged` with the business. This analysis helps in understanding which customers have become inactive and may benefit from targeted re-engagement strategies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7abba436-d1ec-4b42-83ca-0a313ab1ac0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary_value</th>\n",
       "      <th>R_Quartile</th>\n",
       "      <th>F_Quartile</th>\n",
       "      <th>M_Quartile</th>\n",
       "      <th>RFMClass</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18011.0</th>\n",
       "      <td>373</td>\n",
       "      <td>5</td>\n",
       "      <td>63.03</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13065.0</th>\n",
       "      <td>373</td>\n",
       "      <td>5</td>\n",
       "      <td>120.71</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14237.0</th>\n",
       "      <td>373</td>\n",
       "      <td>4</td>\n",
       "      <td>77.45</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17643.0</th>\n",
       "      <td>373</td>\n",
       "      <td>3</td>\n",
       "      <td>58.80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15350.0</th>\n",
       "      <td>373</td>\n",
       "      <td>2</td>\n",
       "      <td>115.65</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            recency  frequency  monetary_value  R_Quartile  F_Quartile  \\\n",
       "CustomerID                                                               \n",
       "18011.0         373          5           63.03           1           1   \n",
       "13065.0         373          5          120.71           1           1   \n",
       "14237.0         373          4           77.45           1           1   \n",
       "17643.0         373          3           58.80           1           1   \n",
       "15350.0         373          2          115.65           1           1   \n",
       "\n",
       "            M_Quartile RFMClass  \n",
       "CustomerID                       \n",
       "18011.0              1      111  \n",
       "13065.0              1      111  \n",
       "14237.0              1      111  \n",
       "17643.0              1      111  \n",
       "15350.0              1      111  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Customers who's recency, frequency as well as monetary values are low \n",
    "\n",
    "RFM_Segment[RFM_Segment['RFMClass']=='111'].sort_values('recency',ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06e15e6e-177c-40cb-98dd-b247e98e5258",
   "metadata": {},
   "source": [
    "# **9.0 Who are loyal customers?**\n",
    "-------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc19a63a-2fd6-4b68-ab3d-c14e4f356ea6",
   "metadata": {},
   "source": [
    "In this section, we focus on identifying **loyal customers** based on their `RFM (Recency, Frequency, Monetary) scores`, specifically looking at those with high frequency values. `Loyal customers` are characterized by their **frequent purchases**, which often indicates a strong and ongoing relationship with the business. By examining customers who fall into the higher quartiles for frequency, we can identify those who have demonstrated consistent purchasing behavior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "03253181-e90b-4f40-8d15-2834ba6367a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary_value</th>\n",
       "      <th>R_Quartile</th>\n",
       "      <th>F_Quartile</th>\n",
       "      <th>M_Quartile</th>\n",
       "      <th>RFMClass</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13089.0</th>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>32957.08</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14096.0</th>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>32949.41</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17841.0</th>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>29176.25</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14298.0</th>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>20823.48</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12748.0</th>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>18388.33</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            recency  frequency  monetary_value  R_Quartile  F_Quartile  \\\n",
       "CustomerID                                                               \n",
       "13089.0           4         15        32957.08           4           4   \n",
       "14096.0           4         26        32949.41           4           4   \n",
       "17841.0           1         25        29176.25           4           4   \n",
       "14298.0           8         12        20823.48           4           4   \n",
       "12748.0           0         22        18388.33           4           4   \n",
       "\n",
       "            M_Quartile RFMClass  \n",
       "CustomerID                       \n",
       "13089.0              4      444  \n",
       "14096.0              4      444  \n",
       "17841.0              4      444  \n",
       "14298.0              4      444  \n",
       "12748.0              4      444  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Customers with high frequency value\n",
    "\n",
    "RFM_Segment[RFM_Segment['F_Quartile'] >= 3 ].sort_values('monetary_value', ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0e4492d-25aa-4d18-a2f8-7238bf858cee",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "\n",
    "Now, let's count the number of **unique RFM segments** to understand the diversity of customer profiles based on their `recency`, `frequency`, and `monetary values`. This will provide insight into how many distinct customer segments exist within the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "03fd73ca-78ed-4937-b166-77abcc35cf4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63\n",
      "CustomerID\n",
      "12747.0    11\n",
      "12748.0    12\n",
      "12749.0    11\n",
      "12820.0    10\n",
      "12821.0     3\n",
      "Name: RFM_Score, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Count num of unique segments\n",
    "RFM_count_unique = RFM_Segment.groupby('RFMClass')['RFMClass'].nunique()\n",
    "print(RFM_count_unique.sum())\n",
    "\n",
    "# Calculate RFM_Score\n",
    "RFM_Segment['RFM_Score'] = RFM_Segment[['R_Quartile','F_Quartile','M_Quartile']].sum(axis=1)\n",
    "print(RFM_Segment['RFM_Score'].head())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9af8a06-96b0-4cfb-919e-83ec4dcf6b75",
   "metadata": {},
   "source": [
    "# **10.0 Defining Customer Segments with RFM Levels**\n",
    "------------------------------------------------------\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "31663845-7e2c-40a9-b145-bd458b9e0509",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary_value</th>\n",
       "      <th>R_Quartile</th>\n",
       "      <th>F_Quartile</th>\n",
       "      <th>M_Quartile</th>\n",
       "      <th>RFMClass</th>\n",
       "      <th>RFM_Score</th>\n",
       "      <th>RFM_Level</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12747.0</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>2029.71</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>434</td>\n",
       "      <td>11</td>\n",
       "      <td>Can't Loose Them</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12748.0</th>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>18388.33</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "      <td>12</td>\n",
       "      <td>Can't Loose Them</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12749.0</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>2600.53</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>434</td>\n",
       "      <td>11</td>\n",
       "      <td>Can't Loose Them</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12820.0</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>905.92</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>433</td>\n",
       "      <td>10</td>\n",
       "      <td>Can't Loose Them</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12821.0</th>\n",
       "      <td>214</td>\n",
       "      <td>3</td>\n",
       "      <td>75.72</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>3</td>\n",
       "      <td>Demands Activation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12822.0</th>\n",
       "      <td>70</td>\n",
       "      <td>8</td>\n",
       "      <td>743.28</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>233</td>\n",
       "      <td>8</td>\n",
       "      <td>Champions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12824.0</th>\n",
       "      <td>59</td>\n",
       "      <td>4</td>\n",
       "      <td>306.22</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>212</td>\n",
       "      <td>5</td>\n",
       "      <td>Promising</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12826.0</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1367.80</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>434</td>\n",
       "      <td>11</td>\n",
       "      <td>Can't Loose Them</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12827.0</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>375.05</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>412</td>\n",
       "      <td>7</td>\n",
       "      <td>Loyal/Commited</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12828.0</th>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>874.36</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>443</td>\n",
       "      <td>11</td>\n",
       "      <td>Can't Loose Them</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            recency  frequency  monetary_value  R_Quartile  F_Quartile  \\\n",
       "CustomerID                                                               \n",
       "12747.0           2         10         2029.71           4           3   \n",
       "12748.0           0         22        18388.33           4           4   \n",
       "12749.0           3         10         2600.53           4           3   \n",
       "12820.0           3         10          905.92           4           3   \n",
       "12821.0         214          3           75.72           1           1   \n",
       "12822.0          70          8          743.28           2           3   \n",
       "12824.0          59          4          306.22           2           1   \n",
       "12826.0           2          9         1367.80           4           3   \n",
       "12827.0           5          5          375.05           4           1   \n",
       "12828.0           2         11          874.36           4           4   \n",
       "\n",
       "            M_Quartile RFMClass  RFM_Score           RFM_Level  \n",
       "CustomerID                                                      \n",
       "12747.0              4      434         11    Can't Loose Them  \n",
       "12748.0              4      444         12    Can't Loose Them  \n",
       "12749.0              4      434         11    Can't Loose Them  \n",
       "12820.0              3      433         10    Can't Loose Them  \n",
       "12821.0              1      111          3  Demands Activation  \n",
       "12822.0              3      233          8           Champions  \n",
       "12824.0              2      212          5           Promising  \n",
       "12826.0              4      434         11    Can't Loose Them  \n",
       "12827.0              2      412          7      Loyal/Commited  \n",
       "12828.0              3      443         11    Can't Loose Them  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Define FMCG_rfm_level function\n",
    "def rfm_level(df):\n",
    "    if df['RFM_Score'] >= 9:\n",
    "        return 'Can\\'t Loose Them'\n",
    "    elif ((df['RFM_Score'] >= 8) and (df['RFM_Score'] < 9)):\n",
    "        return 'Champions'\n",
    "    elif ((df['RFM_Score'] >= 7) and (df['RFM_Score'] < 8)):\n",
    "        return 'Loyal/Commited'\n",
    "    elif ((df['RFM_Score'] >= 6) and (df['RFM_Score'] < 7)):\n",
    "        return 'Potential'\n",
    "    elif ((df['RFM_Score'] >= 5) and (df['RFM_Score'] < 6)):\n",
    "        return 'Promising'\n",
    "    elif ((df['RFM_Score'] >= 4) and (df['RFM_Score'] < 5)):\n",
    "        return 'Requires Attention'\n",
    "    else:\n",
    "        return 'Demands Activation'\n",
    "        \n",
    "# Create a new variable RFM_Level\n",
    "RFM_Segment['RFM_Level'] = RFM_Segment.apply(rfm_level, axis=1)\n",
    "\n",
    "# Printing the header with top 15 rows \n",
    "RFM_Segment.head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12373e7f-7919-4e71-b401-f6ffda5eeecd",
   "metadata": {},
   "source": [
    "## **10.1 Insights from Customer Segmentation**\n",
    "---------------------------------------------------\n",
    "\n",
    "This segmentation allows for targeted marketing strategies by identifying customers who are the most valuable and engaged, as well as those who may need additional incentives to increase their value to the business.\n",
    "\n",
    "1. **Top-Performing Customers:**\n",
    "\n",
    "    Customers with high RFM scores (e.g., `CustomerIDs 12748.0`, `12747.0`, and `12826.0`) are classified as **\"Can't Lose Them.\"** These customers have a high recency, frequency, and monetary value, indicating that they are highly engaged and generate significant revenue. Retaining these customers should be a `top priority`, and personalized marketing strategies or loyalty programs could be beneficial to maintain their high level of engagement.\n",
    "\n",
    "  \n",
    "2. **Champions:**\n",
    "\n",
    "    The customer with `CustomerID 12822.0` falls into the **\"Champions\"** category. These customers have high frequency and monetary values but slightly lower recency scores compared to the `\"Can't Lose Them\"` group. They are valuable customers but may need strategies to keep them engaged more consistently.\n",
    "\n",
    "3. **Promising and Loyal Customers:**\n",
    "\n",
    "    Customers such as `12824.0 (Promising)` and `12827.0 (Loyal/Committed)` have decent frequency and monetary values but lower recency scores. This suggests that while these customers engage and spend a good amount, their recent activity is `less frequent`. **Special promotions or re-engagement campaigns** could help improve their recency scores and potentially move them into the higher-value segments.\n",
    "\n",
    "  \n",
    "4. **At-Risk Customers:**\n",
    "\n",
    "    Customers with low RFM scores, such as `CustomerID 12821.0 (Demands Activation)`, show low values across recency, frequency, and monetary metrics. These customers have not engaged recently and have `minimal transactions and spending`. Strategies for **reactivation or targeted offer**s could help recover these customers or at least understand why their engagement has declined.\n",
    "\n",
    "  \n",
    "5. **Revenue Insights:**\n",
    "\n",
    "    The high monetary value of some customers (e.g., `CustomerIDs 12748.0` and `12749.0`) emphasizes the importance of focusing on `high-spending customers` for maximizing revenue. Analyzing the purchasing patterns of these customers can provide insights into which products or services drive their high spending.\n",
    "\n",
    "  \n",
    "6. **Customer Segmentation Strategy:**\n",
    "\n",
    "    The segmentation into distinct RFM levels provides actionable insights for different customer groups. `Tailoring marketing efforts based on these segments` can optimize resource allocation and enhance customer satisfaction and loyalty. For example, creating **exclusive offers** for the \"Can't Lose Them\" group and special incentives for \"Loyal/Committed\" customers can improve overall engagement and sales.\n",
    "\n",
    "\n",
    "By leveraging these insights, businesses can develop targeted marketing strategies to enhance customer relationships, optimize spending, and improve overall business performance.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8cdd17f-fd07-4274-83ad-d98a08fef1dc",
   "metadata": {},
   "source": [
    "# **11.0 Analyzing RFM Segments**\n",
    "--------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43e96df9-60de-4d6e-ae7e-601fc15288c1",
   "metadata": {},
   "source": [
    "\n",
    "Calculate the average values for each **RFM_Level** and determine the size of each segment.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "cf06e659-3273-420c-be09-d7488514c3eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   recency frequency monetary_value      \n",
      "                      mean      mean           mean count\n",
      "RFM_Level                                                \n",
      "Can't Loose Them      25.7      11.0         1869.0  1420\n",
      "Champions             66.2       8.1          598.4   358\n",
      "Demands Activation   257.8       3.0           87.1   373\n",
      "Loyal/Commited        80.1       6.8          457.7   401\n",
      "Potential             98.5       5.7          290.2   408\n",
      "Promising            123.1       4.8          219.1   429\n",
      "Requires Attention   169.1       4.0          148.1   378\n"
     ]
    }
   ],
   "source": [
    "rfm_level_agg = RFM_Segment.groupby('RFM_Level').agg({\n",
    "    'recency': 'mean',\n",
    "    'frequency': 'mean',\n",
    "    'monetary_value': ['mean', 'count']\n",
    "}).round(1)\n",
    "\n",
    "# Print the aggregated dataset\n",
    "print(rfm_level_agg)"
   ]
  },
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    "---------------------------------------------------\n",
    "## **10.1 Insights from RFM Segmentation Analysis**\n",
    "---------------------------------------------------\n",
    "\n",
    "This segmentation allows for targeted marketing strategies by identifying customers who are the most valuable and engaged, as well as those who may need additional incentives to increase their value to the business.\n",
    "\n",
    "1. **High-Value Customers:**\n",
    "\n",
    "   - **Can't Loose** Them segment is the most valuable, with the highest average 1monetary value of $18691. They also have the `lowest recency (25.7 days)` and `high frequency (11.0)`. This indicates they are frequent, recent, and high-spending customers, representing **1420 individuals**.\n",
    "<br>\n",
    "\n",
    "2. **Top-Performing Segments:**\n",
    "\n",
    "    - **Champions** have a decent average `monetary value ($598.4)` and relatively `high recency (66.2 days)` and `frequency (8.1)`. This segment comprises **358 customers** and, while not as valuable as the \"Can't Loose Them\" segment, they are still strong performers.\n",
    "\n",
    "<br>\n",
    "\n",
    "3. **Low-Engagement Segments:**\n",
    "\n",
    "    - **Demands Activation** has the `highest recency (257.8 days)` and `lowest frequency (3.0)`, along with the lowest average `monetary value ($87.1)`. With **373 customers**, this segment is less engaged and less profitable.\n",
    "\n",
    "<br>\n",
    "\n",
    "4. **Moderate Performers**\n",
    "\n",
    "   -  **Loyal/Committed and Potential** segments show moderate average monetary values (`$457.7` and `$290.2`, respectively) and `frequency rates (6.8 and 5.7)`. They have higher recency compared to the \"Can't Loose Them\" segment but are still significant in terms of revenue and engagement, with **401 and 408 customers** respectively.\n",
    "\n",
    "<br>\n",
    "  \n",
    "5. **Lower Value and Engagement:**\n",
    "\n",
    "    - **Promising and Requires Attention** segments have lower average monetary values (`$219.1` and `$148.1`, respectively) and frequencies (`4.8 and 4.0`). Their recency is also higher, indicating these customers are less frequent and less recent, with **429 and 378 individuals** in these segments.\n",
    "\n",
    "<br>\n",
    "  \n",
    "\n",
    "\n",
    "\n"
   ]
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