Prepaid Product Recommendation Engine
Telecommunications Sector
15% Increase in Prepaid Package Penetration + 20% Increase in Package Sales through Personalized ML Recommendations
Prepaid Package Penetration: +15%
Package Sales: +20%
Personalized Recommendations via Association Rules & Clustering
Introduction
As part of the strategic evolution from DataSphere to Simov Labs, this case demonstrates a machine learning-powered recommendation engine that optimized prepaid product offerings for a leading telecommunications company—transforming a broad, low-perceived-value catalog into personalized, high-relevance suggestions that boosted penetration, sales, and customer experience.
Business Challenge
The telecom company managed an extensive prepaid product catalog that overwhelmed customers, leading to low perceived value and poor adoption rates. Prepaid customers struggled to identify suitable packages, resulting in limited penetration, underutilized products, and missed revenue opportunities. There was no systematic way to personalize offerings based on customer behavior, preferences, or complementary product combinations.
Implemented Solution
- 1Conducted association rules analysis to discover high-performing product combinations and customer clustering to segment users based on purchase and usage patterns.
- 2Built a recommendation engine using low-complexity machine learning techniques (association rules + clustering) to generate personalized, dynamic suggestions aligned with segment behaviors and valued bundles.
- 3Integrated the model outputs into the company's CRM, campaign management platform, and USSD menu for seamless delivery at key customer touchpoints.
- 4Leveraged internal datamarts as the data source to ensure accuracy, relevance, and no need for external data acquisition.
Obtained Results
Increased prepaid package penetration by 15%, demonstrating improved value perception and customer adoption.
Boosted prepaid package sales by 20% through targeted, behavior-aligned recommendations.
Enhanced customer experience by delivering relevant, personalized offers that strengthened loyalty and satisfaction.
Achieved significant business impact using low-complexity ML techniques, avoiding costly or overly complex models.
Optimized the product catalog and offerings, improving profitability and overall value proposition in a competitive market.
Evolution in Simov Labs
This recommendation engine project is now fully integrated into our portfolio at Simov Labs. We evolve these association rules and clustering approaches into modern, cloud-native implementations (Snowflake, Databricks, BigQuery) with production-ready AI/ML for real-time personalization, collaborative filtering, deep learning-based next-best-offer, multi-channel delivery (app, USSD, SMS), continuous retraining, and explicit ROI measurement.
Aligned with DAMA and TDWI governance frameworks, our current solutions emphasize business-first analysis, radical honesty in opportunity assessment, and long-term partnership—ensuring recommendation systems deliver sustainable uplift in adoption, revenue, customer loyalty, and competitive differentiation in telecommunications. No promises. Just results.
Ready to deploy personalized recommendations that drive higher adoption, sales, and customer satisfaction in your product offerings?
Book a free 30-minute consultation. We'll discuss your product catalog and where a recommendation engine can deliver the most impact.