Context and objectives

A leading electronics retailer was operating without a clear customer segmentation strategy, lacking visibility into purchasing patterns and customer value. Without a structured approach to understanding customer behavior, the retailer's marketing efforts were broadly targeted rather than personalized, resulting in inefficient campaign spending and missed revenue opportunities. Additionally, it made it harder for the retailer to have a systematized contact and engagement process.

The retailer called on Agilytic to develop a sophisticated RFM (Recency, Frequency, Monetary) model that would incorporate both online and in-store sales data. A tailored and optimized RFM required both a meticulous technical process and a good understanding of the business context to define actionable segments.

Approach

Our team implemented a structured approach to transform the retailer's customer analytics capabilities:

1. Data Integration and Cleaning: We aggregated data from multiple sources, including point-of-sale systems, e-commerce platforms, and customer databases to create a unified view of customer interactions across all channels.

2. Model Development: We designed a comprehensive RFM model that properly weighted and accounted for both online and in-store purchases, ensuring a complete picture of customer engagement and value.

3. Enhanced Data Collection: We introduced new data collection protocols that properly tracked returned merchandise and accurately tagged different sales types to improve data quality.

4. Efficient Segmentation: We developed a streamlined segmentation framework with only five strategic customer segments (rather than the nine segments commonly used in standard models), making the insights substantially more actionable for the sales and marketing teams.

5. Technical Implementation: We created a purpose-built technical module for the RFM model that integrated with the retailer's existing systems while providing improved performance and scalability.

6. In-depth Segment Analysis: We conducted comprehensive analysis of each segment, including:

   • Expected revenue per segment and per capita calculations

   • Annual transition matrices showing how customers move between segments over time

Results

The implementation of the comprehensive RFM model delivered significant, lasting benefits to the electronics retailer:

1. Data-driven marketing strategy: The improved segmentation enables targeted and proportionate actions on the clients based on their segment.

2. Holistic Customer Understanding: For the first time, the retailer gained visibility into their complete customer base, including previously “invisible” in-store shoppers who had never made online purchases.

3. Technical Foundation: The cloud implementation provided a scalable, future-proof foundation for ongoing analytics development and integration with other business systems.

4. High-Value Customer Identification: Our analysis identified a critical subset of customers responsible for 40% of future revenues, allowing for prioritized retention efforts.

5. Strategic Resource Allocation: Revenue forecasting per segment enabled the retailer to efficiently calibrate marketing investments according to expected returns from each customer group.

6. Customer Journey Mapping: The segment transition analysis provided insights into typical customer lifecycle patterns, helping predict and influence customer movement between segments.

The retailer now has a robust infrastructure for customer analysis that supports strategic decision-making across marketing, merchandising, and store operations. The more accurate understanding of customer value has enabled the company to allocate resources more effectively and build stronger relationships with their most valuable customers, positioning the business for sustained competitive advantage in the retail electronics market.