Customer Segmentation Use Case Part 1:  Introduction

Introduction

Marketing departments utilize customer segmentation to categorize individuals into distinct groups based on specific criteria, aiming to more effectively target them in campaigns. Often, these criteria must be extracted or derived from various elements within the customer data. This data is frequently ‘dirty,’ containing missing or erroneous values, and can also evolve over time, necessitating continuous application of segmentation strategies.In this blog post series, we will showcase how Infoworks AI can accelerate a customer segmentation project. We will demonstrate how to use Infoworks AI to gather, cleanse, and analyze the relevant data, effectively segmenting the customer data set and operationalizing the segmentation process for ongoing analysis.

Use Case

Nexacore is a fictional electronics retailer that has experienced an increase in customer churn.  Nexacore’s CEO has launched a company-wide initiative to address this urgent threat to their growth.  The marketing team needs to understand customer buying behavior to design targeted marketing campaigns to reduce churn and increase sales.  The marketing data analytics team must move quickly to complete the analytics for a presentation to their CMO. 

The team has recommended a customer segmentation model called RFM, which stands for Recency, Frequency, and Monetary.  This model scores customers based on how recently a customer made a purchase, how frequently they purchase, and how much money they have spent with the organization.  Based on these three measures, customers are divided into quartiles and a composite score is generated.

The marketing data analytics team needs to access all of the company’s customer data, cleanse that data, apply the RFM scoring, and generate a report showing the results.

Value

With this segmentation, Nexacore can develop targeted marketing campaigns for each customer segment.  For example, customers who purchased recently and frequently, but are lower in spending (RFM scores of 113 and 114), could be categorized as loyal, low spending customers.  Further categories can include:

Category	RFM Scores	Campaign Strategy
Nexacore Loyal	111, 112, 113, 114	Weekly promotions
Churned Loyal Customers	411, 412, 421, 422	Loyal customer 25% discount
High Spending New Customers	141,142	Weekly promotions + 10% off on your next visit coupon
Seasonal Shoppers	131, 132, 231, 232, 331, 332, 431, 432	Seasonal catalog and 25% off coupon

Summary

Segmenting customers enables marketing departments to target specific groups with tailored campaign content. Nexacore is leveraging customer segmentation to address an increase in churn. The marketing data analytics team is working to explore, cleanse, and apply RFM scoring to the data for effective segmentation. This approach will allow Nexacore to market to each customer segment in a way that resonates more effectively.

In the upcoming blog posts in this series, we’ll explore how they leveraged Infoworks AI to expedite the completion of this customer segmentation use case.

In Part 2:  Getting Started with Infoworks AI, we’ll get connected to our data set, integrate business rules and definitions, and create a project.

In Part 3:  Data Exploration and Cleansing with Infoworks AI, we’ll see how Infoworks AI automates and accelerates data exploration through profiling data.  We’ll also see how Infoworks can be used for data cleansing.  All without having to write code.

Lastly, in Part 4:  Data Analysis and Reporting.  We’ll see how Infoworks AI enables data analysts to apply the RFM model to our data set and generate reporting and visualizations.