How an Online Retail Giant Boosted Customer Loyalty by 40% Using Lifesight’s RFM Analysis

increase in customer loyalty levels

increase in engagement rates

uplift in overall revenue

decrease in customer churn rates

IN THIS STORY

About the brand
The Challenge
Primary Goals
The importance of RFM Analysis
The Solution

About the brand

Rooted in the digital age, a major online marketplace platform is an innovation-driven retail brand, headquartered in a tech-centric locale of California. They combine AI-enabled analytics and savvy customer service to redefine what customer-centricity means today. With an ad spend of $1.2 Mn, they used Lifesight’s RFM Analysis tool to segment their customers into four groups: loyal, at-risk, new, and churned. This helped them identify their most loyal customers and those who were at risk of churning. Take a look at how our strategic alliance with Lifesight’s RFM Analysis tool catapulted our customer loyalty metrics by an astounding 40%.

The Challenge

Their marketplace was losing customers and struggling to keep them engaged. Their personalized campaigns weren’t working as effectively. They realized that they needed to understand their customer segments better to create more effective marketing strategies.

Primary Goals

Enhanced customer experience and improved marketing ROI
Customer Lifecycle Management through optimized customer journey
Allocate marketing resources more efficiently by focusing on high-value segments and optimizing ad spend, channels, and messaging.
Recovered revenue and revitalized customer relationships.

What I love about the Lifesight is the scoring system, which rates customers on ranks. Our top 20% of customers, earned a recency score of 5. The following 20%, customers like received a score of 4, and so forth. This has given us a clear roadmap for personalized marketing and engagement strategies.

VP, Marketing

The importance of RFM Analysis

RFM (Recency, Frequency, Monetary) analysis is a model that segments a customer base into various loyalty levels based on the recency, frequency, and monetary value of their purchases. Applying RFM analysis, businesses can identify and target their most loyal customers with more personalized, effective marketing campaigns.

RFM analysis is a customer segmentation model that helps businesses identify and target their most loyal customers. It uses three key metrics to segment customers:

Businesses can use RFM analysis to create personalized marketing campaigns for each customer segment. For example, businesses can send special offers to their most loyal customers or offer them early access to new products. Here’s the methodical approach employed by the brand to leverage RFM analysis for refining its marketing initiatives:

The Solution

The brand turned to Lifesight’s Measure platform to gain insights into its customer base. Within the Measure dashboard under “Customers,” they could view their RFM Analysis report. This report broke down customer counts within various loyalty segments, such as

Champions: Recent, frequent, and high-spending customer
Potential Loyalists: Recent but less frequent or lower spending
At Risk: Formerly frequent customers who haven’t purchased recently
Can’t Lose Them: Customers who used to be regular and high-spending but have dropped off

Implementation Strategy & Methodology

The brand applied RFM analysis to categorize its customer base into three distinct segments:

Loyal Customers: Individuals who have engaged in recent transactions, boast a history of multiple purchases, and have exhibited substantial monetary investment with the retailer.
At-risk Customers: Customers who have recent engagements with the brand but lack a significant history of transactions or monetary investments.
New Customers: Entrants who have initiated purchases recently but do not have a diverse transaction history or substantial financial contributions with the retailer.

Post-segmentation, the brand conceived personalized marketing strategies tailored to each customer group. For instance, loyal customers received exclusive offers like a 20% discount on subsequent purchases, whereas at-risk customers were enticed with gifts accompanying their future acquisitions.

This application of RFM analysis empowered the brand to orchestrate marketing campaigns with enhanced efficacy and resonance, ensuring precise and impactful customer outreach.

Data Integration: The retailer giant integrated their customer data with Lifesight’s Measure.
RFM Analysis: The brand ran the RFM analysis, identifying the various customer segments.
Targeted Campaigns: Based on the insights from the RFM Analysis, the retailer developed targeted marketing campaigns for each segment.
Performance Tracking: They closely monitored the engagement and conversion rates to measure the effectiveness of their new approach.

Key Results

Improved every key marketing and business metrics YoY:

32 increase in customer loyalty levels
27% increase in engagement rates
20% uplift in overall revenue
18% decrease in customer churn rates

Conclusion

Understanding customer loyalty levels through RFM analysis has enabled the brand to develop highly targeted and effective marketing campaigns. The tool enables marketers to swiftly categorize users into similar groups and craft customized marketing approaches for each. This not only boosts user involvement but also enhances long-term retention. Using Lifesight’s Measure platform, they have successfully turned the tides on customer churn and significantly boosted customer loyalty and profitability.