A Modern Data Approach to Understanding Customer Loyalty at Renewal Time
By Jason Ling
Introduction
Retention is the heartbeat of profitable insurance growth. While acquiring new policyholders is important, retaining them is even more critical to long-term success. Yet, many insurance companies struggle to accurately calculate retention — and even fewer have the ability to predict who’s likely to churn before it happens.
With the right data foundation, insurers running core systems like Guidewire can use Snowflake to build a scalable, repeatable pipeline for calculating actual retention at renewal, and then develop a predictive model to identify high-risk policyholders and take proactive steps to retain them.
Step 1: Define and Calculate Retention at Renewal
Before predicting retention, you need a reliable way to calculate it. While the definition may vary slightly across organizations, here’s a common industry approach:
Retention Rate = (Number of Renewed Policies) / (Number of Renewal-Eligible Policies)
This typically involves:
- Identifying all policies with a prior term ending in a given period (e.g., Q2 2025)
- Checking whether those policies had a subsequent term issued within a renewal window (e.g., 60 days)
- Accounting for non-renewable policies, cancellations, and rewrites
Step 2: Enrich the Data
Once retention is calculated historically, the next step is to build a dataset for modeling. This requires joining policy and customer features available prior to renewal — not afterward — to avoid data leakage.
Typical features to include:
- Customer characteristics: tenure, age, multi-policy status, payment history
- Policy details: premium change, deductible, limit, coverage type
- Claims history: number, type, cost of prior claims
- Agent/channel: direct vs. broker, agency size
- Engagement: portal usage, renewal reminder opens
- Competitor pressure: inferred from quote behavior or market rate changes
All of this can be modeled in Snowflake using SQL views, dbt models, or Snowpark for Python-based transformations.
Step 3: Build a Predictive Retention Model
With a labeled dataset — i.e., past policyholders with known renewal outcomes — you can train a machine learning model to predict churn at renewal.
Options include:
- Logistic Regression: Simple and interpretable, good for identifying key drivers.
- Gradient Boosted Trees (e.g., XGBoost): High-performance, works well with tabular insurance data.
- Snowpark ML: Use Snowflake’s native Python environment to train and deploy models without moving data.
Example Workflow:
- Use Snowflake to prep data and export to a notebook (or use Snowpark ML).
- Train a binary classifier: 1 = non-renewal (churn), 0 = renewal.
- Evaluate with AUC, precision/recall, or lift at top deciles.
- Push scored data back into Snowflake to power dashboards or campaigns
Step 4: Operationalize and Act
Once trained and validated, your retention model becomes a proactive business tool:
Early Intervention: Flag at-risk policyholders 30–60 days ahead of renewal for outreach, discount review, or bundling offer.
Marketing Optimization: Prioritize retention campaigns toward high-churn risk but high-LTV (lifetime value) customers.
Underwriting Feedback Loop: Feed churn signals back to product and underwriting teams to refine pricing and product design.
Executive Dashboards: Track real-time renewal risk by region, segment, or book — directly from Snowflake to BI tools like Tableau, Power BI, or Sigma.
Guidewire + Snowflake = Retention Intelligence at Scale
For insurers on Guidewire systems (PolicyCenter, BillingCenter, ClaimCenter), Snowflake becomes the analytics brain that sits on top of your transactional backbone. By integrating with Guidewire’s APIs or backend tables, you can bring together the full customer and policy lifecycle into one platform for insight and prediction.
- Policy data → track premium changes, deductible shifts
- Claims data → assess claim frequency and satisfaction
- Billing data → incorporate lapses or failed payments
- Engagement data → measure digital behavior and preferences
This holistic view is the foundation for intelligent retention strategies that go far beyond simple reactive measures.
Retention Is Predictable — If You Have the Right Data Strategy
In today’s competitive insurance landscape, predicting churn isn’t a luxury — it’s a necessity. Snowflake enables insurers to calculate actual retention, understand the “why” behind attrition, and proactively intervene with customers at risk.
By combining Guidewire’s rich operational data with Snowflake’s analytical power, insurers can shift from reacting to churn to predicting and preventing it.
Want to build a predictive retention model for your insurance business? Let’s talk about how to unlock your Guidewire data with Snowflake and accelerate your retention strategy.
Let’s continue the conversation
If you’re exploring Insurance Retention or want to map a high-value use case, we’d love to talk.
About kipi.ai
Kipi.ai, a WNS Company, is a global leader in data modernization and democratization focused on the Snowflake platform. Headquartered in Houston, Texas, Kipi.ai enables enterprises to unlock the full value of their data through strategy, implementation and managed services across data engineering, AI-powered analytics and data science.
As a Snowflake Elite Partner, Kipi.ai has one of the world’s largest pools of Snowflake-certified talent—over 600 SnowPro certifications—and a portfolio of 250+ proprietary accelerators, applications and AI-driven solutions. These tools enable secure, scalable and actionable data insights across every level of the enterprise. Serving clients across banking and financial services, insurance, healthcare and life sciences, manufacturing, retail and CPG, and hi-tech and professional services, Kipi.ai combines deep domain excellence with AI innovation and human ingenuity to co-create smarter businesses. As a part of WNS, Kipi.ai brings global scale and execution strength to accelerate Snowflake-powered transformation world-wide.
For more information, visit www.kipi.ai.
