Propensity-to-Buy and Customer Segmentation Platform
About the Customer
Allianz Life Indonesia is a leading insurance provider serving a diverse customer base nationwide. The organization needed a scalable data science framework to unlock the value of its customer data and support aggressive growth targets.
Key Business Challenges
- Limited systematic use of predictive modeling in sales and marketing
- Difficulty identifying which customers were most likely to buy across multiple product lines
- Fragmented views of customers, making it hard to design targeted cross-sell and upsell actions
What We Automated
- Scoring customers and prospects with Propensity-to-Buy models across multiple business lines
- Segmenting customers based on behavior, value, and risk to drive targeted campaigns
- Generating actionable recommendations for next-best offers and follow-up actions
- Feeding model outputs into BI dashboards for sales, marketing, and management
How We Solved It
- 1Built and supervised Propensity-to-Buy models across several business lines
- 2Developed a customer segmentation model and analyzed segments to define tailored actions per group
- 3Embedded the models into an end-to-end data science framework with MLOps design and BI dashboards
- 4Created a repeatable, production-grade analytics engine that directly powers sales strategy and campaign design
"The analytics framework transformed how we prioritize customers. Sales teams understand where to focus and can justify decisions with data rather than intuition."
Key Results & Impact
Propensity-to-Buy models helped business units reach close to 150% of their annual sales targets
Customer segmentation insights improved average closing time by around 10%
Sales and marketing teams could prioritize high-potential leads instead of treating all customers equally
Teams can justify decisions with data rather than intuition
Ready to Optimize Your Sales?
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