Quantium_Virtual_Internship

Data Analytics

Introduction:

Quantium has had a data partnership with a large supermarket brand for the last few years who provide transactional and customer data. You are an analyst within the Quantium analytics team and are responsible for delivering highly valued data analytics and insights to help the business make strategic decisions.

Supermarkets will regularly change their store layouts, product selections, prices and promotions. This is to satisfy their customer’s changing needs and preferences, keep up with the increasing competition in the market or to capitalise on new opportunities. The Quantium analytics team are engaged in these processes to evaluate and analyse the performance of change and recommend whether it has been successful.

In this program you will learn key analytics skills such as:

• Data wrangling
• Data visualization
• Programming skills
• Statistics
• Critical thinking
• Commercial thinking

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.

Here is your task

We need to present a strategic recommendation to Julia that is supported by data which she can then use for the upcoming category review however to do so we need to analyse the data to understand the current purchasing trends and behaviours. The client is particularly interested in customer segments and their chip purchasing behaviour. Consider what metrics would help describe the customers’ purchasing behaviour.

To get started, download the resource csv data files below and begin performing high level data checks such as:

• Creating and interpreting high level summaries of the data
• Finding outliers and removing these (if applicable)
• Checking data formats and correcting (if applicable)

You will also want to derive extra features such as pack size and brand name from the data and define metrics of interest to enable you to draw insights on who spends on chips and what drives spends for each customer segment. Remember our end goal is to form a strategy based on the findings to provide a clear recommendation to Julia the Category Manager so make sure your insights can have a commercial application.

LIFESTAGE: Customer attribute that identifies whether a customer has a family or not and what point in life they are at e.g. are their children in pre-school/primary/secondary school.

PREMIUM_CUSTOMER: Customer segmentation used to differentiate shoppers by the price point of products they buy and the types of products they buy. It is used to identify whether customers may spend more for quality or brand or whether they will purchase the cheapest options.

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.

Here is your task

Julia has asked us to evaluate the performance of a store trial which was performed in stores 77, 86 and 88.

To get started use the QVI_data dataset below or your output from task 1 and consider the monthly sales experience of each store.

This can be broken down by:

• total sales revenue
• total number of customers
• average number of transactions per customer

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.

Here is your task

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.

Keep in mind the key considerations for a presentation:

• Data literacy level of your audience
• Table of contents / agenda
• Problem statement / purpose
• Overview and context
• Content balance
• Layout and content display
• Summary / next steps