Conversion Modeling
MOTIVATION
Predicting customers willingness to
purchase insurance policy at the quoted price
Predictive models must indicate the impact of major business decisions accurately. In the case of selling an insurance product to potential customers, it becomes all the more important. Today, many InsurTech institutions employ Conversion Modeling techniques for predictive whether an insurance quotation will lead to a successful purchase. They use customer and sales data to predict whether a quoted price is acceptable to customers. Accurate prediction of conversion helps companies understand the impact of proposed pricing options and retain a loyal customer portfolio.
The Problem Canvas
- No Pre-trained Model
- The dataset is common (but can be used for analysis)
The Data Question
- Evaluate the possible impact of a New Pricing Plan on the top-line growth
- Based on a quote’s rating characteristics and price, predict the prospect success rate
Solution Canvas
Approach:
InsurTech experts need to balance the impact of a proposed pricing plan on customer attraction with top-line growth. They must keep a keen eye for ensuring profitability of their institution without driving customers away.
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Toolset:
RubiStudio – Data Exploration, Data Joiner, Merger, Statistical Hypothesis, Code Fusion
RubiML – Clustering models, Silhouette Score – Code Fusion
Rubisight – Story and Dashboard.
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Skillset:
Business Reporting
Machine Learning
Domain Knowledge
Data wrangling
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Dataset:
The dataset throws light on customers’ interest in buying home policies from a particular site and includes information on specific coverage, sales, customers, property, and location.
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OUTCOMES
Business Impact
- Accurately identify customers who are more likely to buy the policy
- Determination of individual’s price based on consumer behaviour and historical data
- Aligning better with customer needs leading to a better conversion rate
- Evaluate the impact of proposed changes in pricing
- Maintain an ideal portfolio of various customer segments
