Quantium-Virtual-Internship

This repository contains my solution to the Quantium Data Analytics Virtual Experience Program on Forage

Introduction

Quantium, which was established in 2002, has a long history of data science innovation in various spheres of the business. They are committed to building a team of aspirational, varied, and enjoyable people as a fast expanding worldwide leader in data science and AI.

For the past few years, Quantium has shared transactional and consumer data with a significant grocery brand. As a member of the Quantium analytics team, it is our duty to provide the business with valuable data analytics and insights to aid in strategic decision-making.

Task 1: Data preparation and customer analytics

Conduct analysis on your client's transaction dataset and identify customer purchasing behaviours to generate insights and provide commercial recommendations.

• Examine transaction data – look for inconsistencies, missing data across the data set, outliers, correctly identified category items, numeric data across all tables. If you determine any anomalies make the necessary changes in the dataset and save it. Having clean data will help when it comes to your analysis.

• Examine customer data – check for similar issues in the customer data, look for nulls and when you are happy merge the transaction and customer data together so it’s ready for the analysis ensuring you save your files along the way.

• Data analysis and customer segments – in your analysis make sure you define the metrics – look at total sales, drivers of sales, where the highest sales are coming from etc. Explore the data, create charts and graphs as well as noting any interesting trends and/or insights you find. These will all form part of our report to Julia.

• Deep dive into customer segments – define your recommendation from your insights, determine which segments we should be targeting, if packet sizes are relative and form an overall conclusion based on your analysis.

Task 2: Experimentation and uplift testing

Extend your analysis from Task 1 to help you identify benchmark stores that allow you to test the impact of the trial store layouts on customer sales.

• Select control stores – explore the data and define metrics for your control store selection – think about what would make them a control store. Look at the drivers and make sure you visualise these in a graph to better determine if they are suited. For this piece it may even be worth creating a function to help you.

• Assessment of the trial – this one should give you some interesting insights into each of the stores, check each trial store individually in comparison with the control store to get a clear view of its overall performance. We want to know if the trial stores were successful or not.

• Collate findings – summarise your findings for each store and provide an recommendation that we can share with Julia outlining the impact on sales during the trial period.

Task 3: Analytics and commercial application

Use your analytics and insights from Task 1 and 2 to prepare a report for your client, the Category Manager.

• With our project coming to an end its time for us to send a report to Julia, based on our analytics from the previous tasks. We want to provide her with insights and recommendations that she can use when developing the strategic plan for the next half year.

• As best practice at Quantium, we like to use the “Pyramid Principles” framework when putting together a report for our clients. If you are not already familiar with this framework you can find quick introductions on by searching form them on the internet.

• For this report, we need to include data visualisations, key callouts, insights as well as recommendations and/or next steps.