How Syren Boosted Retention & Revenue for a Beverage Leader 

Problem Statement

Without clear insights into
high-value customers, a leading beverage company struggled with a declining customer retention rate. Their marketing efforts lacked focus ultimately leading to missed opportunities and inefficient spending. 

Challenges 

  • The inability to identify and retain high-value customers resulted in frequent and high customer churn, reducing
long-term revenue potential.
  • Marketing teams lacked the necessary data-driven insights to allocate budgets effectively, leading to wasted resources.
  • Without clear lead
prioritisation, sales teams were unable to focus on the most promising prospects, impacting overall growth. 

Solution

Syren applied a multidimensional approach that included the advanced RFM-R solution, and growth hacking models. Going beyond the traditional recency, frequency, and monetary (RFM) framework, we incorporated range selling, allowing for a more comprehensive analysis of customer purchasing behavior.
Enabling segmentation not only based on transactional metrics but also by brand and category, our approach offered a more granular understanding of customer preferences. 

RFM-R Optimization & Recommendation Engine 

The RFM-R model segmented customers based on their purchasing behavior such as how recently they purchased (Recency), how often they buy (Frequency), and how much they spend (Monetary value). For an even more complete analysis, we incorporated range selling (R) segmentation like the variety of SKUs they purchase, the impact of promotions on their buying decisions, their response to new product introductions, and their preferences across different brands and categories.
These insights enabled precise customer segmentation, allowing targeted marketing campaigns. High-value customers received personalized messaging to encourage repeat purchases, ensuring sustained engagement and maximizing long-term customer value. 

Growth Hacking Models

Automated growth hacking models continuously generated real-time recommendations for the sales team. These insights helped prioritize RFM-R course corrections and identify high-growth and retention opportunities at both the segment and store level.  

Progressive Growth Model 

The Progressive Growth Model (PGM) follows a test-learn-scale approach, identifying high-impact strategies through data analysis. Using A/B testing, dynamic pricing, and AI-powered predictive retention models, PGM refined engagement strategies to proactively retain customers. Once these tactics were identified, they were scaled across channels to maximize ROI, making sure that every marketing dollar contributed to sustainable growth, higher customer lifetime value (CLV), and improved retention rates.

Dynamic Calling

Using AI-driven scheduling, dynamic calling analyzed customer history, demand patterns, and revenue potential to prioritize visits that were most likely to drive retention and sales. Battling limited resources, this approach eliminated wasted effort, sending representatives only where they were needed. By targeting the right customers at the right time, Dynamic Calling improved conversion rates, enhanced customer engagement, and turned each visit into a growth opportunity. 

Promo Optimization  

Promo Optimization ensures companies maximize revenue by strategically planning promotions based on budget constraints, inventory availability, and market impact. Instead of blanket discounts, AI-driven A/B testing identified the most effective promo structures. Linear and Mixed-Integer Programming optimized budget allocation, balancing discounts across products and regions while preventing issues like cannibalization (where one promo hurts another product’s sales). Scenario analysis simulated different strategies to help the customer choose the most profitable approach. By determining what to promote, to whom, and for how long, this model increased ROI, improved retention, and drove sustainable revenue growth. 

Measurable Business Impact

0 %

Increase in
Customer Retention

through tailored marketing for high-value segments identified by the RFM-R model.

0 %

Improvement in ROI

due to optimized marketing spend allocation based on machine learning-driven insights.

0 %

Revenue Growth

by targeting repeat buyers with scenario-based marketing adjustments.

0 %

Boost In CLV

for the top 20% of customers by prioritizing high-frequency, high-value buyers through real-time sales recommendations.

Ready to transform your retention strategy and maximize revenue?

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