Intergamma: Growing card holder loyalty with RFM segmentation and a new customer view

The Challenge

Intergamma faced the challenge of effectively monitoring and growing customer loyalty among their extensive cardholder base. With millions of customers and complex transaction data, it was difficult to gain insights into customer behaviors, preferences, and engagement patterns. They needed a solution that would provide a comprehensive view of each customer, enabling them to create targeted marketing activations and personalized loyalty programs.

The Goal 

1. Monitor Loyalty Among Cardholders: Intergamma wanted to establish a comprehensive and data-driven approach to monitor the loyalty of their cardholders. This involved understanding customer behaviors, preferences, and engagement patterns to identify loyal customers and those at risk of churn.

2. Grow Loyalty with Targeted Marketing Activations: Intergamma sought to leverage customer insights to create targeted marketing activations. The aim was to deliver personalized offers, promotions, and rewards to increase customer engagement, retention, and overall loyalty to the brand.

The Process

Preparation phase:

• Meet to discuss goals and present alternative solutions
• Write backlog items with business owners
• Refine backlog to get all necessary details
• Prioritise backlog by business importance
• Estimation of work for the selected user stories

Implementation phase:

• Define card holder definition and scope of transactions for RFM model
• Describe behaviour and loyalty of Intergamma card holders based on the RFM model
• Refine RFM segments and align with the business
• Develop strategic loyalty groups based on RFM segments
• Combine and load data to a new contact actual table
• Calculate RFM scores and appoint loyalty segments to card holders frequently
• Create customer view combining loyalty scores, additional customer profiles, demographics in one table
• Build reports to provide easy access to key information
• Document tables and views

The Solution

As part of the solution, the implementation of Google Cloud Platform played a crucial role in handling and processing the vast amount of customer data at Intergamma. Scheduled queries were set up to load the most up-to-date customer information into Big Query tables, ensuring that the data used for analysis and reporting was accurate and current.

To effectively identify cardholders and their associated transactions, at Crystalloids we designed a process to determine cardholders from the customer and transaction data. This information was then loaded into a new contact table, creating a consolidated and organized view of all cardholders.

The next step involved calculating RFM scores and loyalty segments for each customer. The RFM model, based on Recency, Frequency, and Monetary metrics, was utilized to categorize customers based on their recent purchase activity, frequency of transactions, and monetary value. The resulting RFM scores and loyalty segments were then loaded into a table, creating a comprehensive customer view that incorporated loyalty-related insights.

To make the obtained insights easily accessible and understandable, Google Data Studio reports were generated. These reports provided valuable insights into customer loyalty status, cardholder profiles, and loyalty trends. The visual representation of data in Data Studio allowed Intergamma's teams to quickly grasp key information and make data-driven decisions to enhance customer loyalty.

The integration of Google Data Studio with the loyalty segments and customer view offered an added advantage for marketing activations. By exporting cardholders directly from Data Studio reports, Intergamma could efficiently target and engage specific customer segments with personalized marketing initiatives, tailored to their individual preferences and behaviors.