Project 3 - Customer Analytics and Retention

Project Overview

Customer analytics helps businesses understand purchasing behavior, customer value, and retention patterns.

In this project, you will create a report that analyzes customer activity and identifies groups that may require additional attention.

Dataset Requirements

Use data containing fields such as:

You may also include a customer table containing signup dates and other relevant attributes.

Step 1: Build the Model

Create appropriate relationships between the customer, sales, and date tables.

Use a customer dimension to organize customer attributes and a date dimension for time-based analysis.

Check that each order and customer is represented at the correct level of detail.

Step 2: Create Customer Measures

Calculate the number of distinct customers with sales:

Customers with Sales =

DISTINCTCOUNT(Sales[CustomerID])

Calculate total sales:

Total Sales =

SUM(Sales[SalesAmount])

Calculate average revenue per customer:

Revenue per Customer =

DIVIDE(

[Total Sales],

[Customers with Sales]

)

The last measure represents average revenue per distinct customer within the current filter context. It does not necessarily represent average order value or customer lifetime value.

Step 3: Create Customer Visuals

Build visuals showing:

• Customers with sales.
• Revenue per customer.
• Sales by customer segment.
• Monthly purchasing trends.
• Revenue by region.
• Customers grouped by purchase activity, if the data supports it.

Use suitable slicers to investigate specific customer segments and periods.

Step 4: Analyze Retention

Customer retention requires a clearly defined customer activity period and a consistent method for identifying returning customers.

For example, a business may measure how many customers who purchased in one month also purchased in the following month.

A retention analysis typically requires historical transaction data, a reliable date table, and carefully defined cohort and activity measures.

Do not label customers as inactive or lost merely because they have not purchased recently without considering the business's normal purchase cycle.

Expected outcome: A customer analytics report that helps businesses understand customer activity and investigate retention opportunities.