AI-Driven Lead Profiling: How InXiteOut Drove a 30% Conversion Lift for a Leading Insurer    

Client Context  

A leading Japan-based insurance provider, offering life and property insurance products, relied heavily on telemarketing for customer acquisition. 

However, telemarketing conversion rates had plateaued at 0.18%, well below industry benchmarks. This resulted in rising acquisition costs and significant pressure on profitability. 

To sustain growth and improve profitability, the client sought a data-driven approach to sharpen targeting and strengthen telemarketing efficiency.  

The InXiteOut Approach 

We conducted a comprehensive assessment of the client’s existing data assets to uncover actionable insights from historical telemarketing outcomes. Despite the low baseline conversion rate, we found that hidden data patterns revealed significant opportunities for precision targeting. 

Our deep-dive analysis focused on three key areas: 

• Identifying High-Propensity Customers: We sifted through demographic and behavioral data to find the standard signals of a likely buyer.  

We identified a cohort of families who had recently purchased a home; this group was 3x more likely to convert on a new life insurance policy. 

• Matching Products to Profiles: We then mapped who was buying what. This revealed clear, actionable patterns between specific profiles and the products they purchased.   

A key finding was that certain postcodes with higher seismic activity showed a 4x propensity to purchase supplemental property insurance. 

• Analyzing Call Outcomes: Finally, we mined call logs and campaign metadata to understand what worked.  

We discovered that calls made on weekday evenings were 2.5x more likely to convert than calls during standard business hours. 

These insights were then validated and used as the foundation for a new modelling framework to operationalize this intelligence for the client's daily decision-making. 

Model framework  

After extensive experimentation, we deployed a two-layered AI/ML framework that balances predictive accuracy with the client's need for interpretable results. 

• Layer 1 – Conversion Prediction: We built a predictive engine to identify customers with the highest likelihood of purchase. This ensured telemarketing agents invested their time in leads with genuine potential, significantly improving efficiency. 
• Layer 2 – Value Segmentation: Building on the first layer, the second model segmented these likely buyers by their potential policy value. This allowed the client to prioritize high-value opportunities, enabling sharper effort allocation and maximizing revenue impact.  

Tech stack used 

• AWS Data Stack (Glue, S3, Redshift) 
• AWS SageMaker 
• Tableau  

Benefits Delivered  

The project delivered measurable improvements across key performance metrics: 

• +30% lift in conversion rates: Our AI-driven targeting immediately boosted the client's telemarketing conversions by 30% over their previous 0.18% baseline, marking a significant enhancement in effectiveness. 
• Lower cost of acquisition: By focusing agent effort on high-propensity leads, the client reduced wasted calls and optimized bandwidth, which directly drove down the cost-per-policyholder 
• Stronger telemarketing ROI: By channeling resources toward high-propensity, high-value segments, telemarketing became a scalable, profitable acquisition engine. 

By leveraging advanced AI & analytics, InXiteOut helped the client streamline telemarketing into a high-impact sales channel.