Insurance Cross-sell Prediction

USE CASES

Cross-Sell Prediction

The current customer base provides a company with the lowest hanging fruit to generate additional revenue.

Effective cross-selling strategies can improve profitability and increase customer retention at the lowest cost of customer acquisition.  The challenge is for the insurer to deliver the right cross-sell product to the right policyholder and at the right price and time.

Cross-sell strategies need to take into account past customer behavior, change in customer mix, a combination of offerings that have the best chance of success, and a host of other such considerations to make them as effective as possible.

All such strategies need to be data-driven business decisions.

USE CASES

Insurance Cross-Sell Prediction

The current customer base provides a company with the lowest hanging fruit to generate additional revenue.

Effective cross-selling strategies can improve profitability and increase customer retention at the lowest cost of customer acquisition.  The challenge is for the insurer to deliver the right cross-sell product to the right policyholder and at the right price and time.

Cross-sell strategies need to take into account past customer behavior, change in customer mix, a combination of offerings that have the best chance of success, and a host of other such considerations to make them as effective as possible.

All such strategies need to be data-driven business decisions.

Apply Effective Cross-selling to reduce risks

Apply Effective Cross-selling to reduce risks

Approach

NEMESIS provides actionable insights through easy but powerful data modeling, customizable dashboards, and immediate actions with case management system.

With NEMESIS, you can:

  • Profile and analyze a large volume of data with simple clicks and clean the data for better insights.
  • Segment the customers and create customer profiles with in-built feature engineering technology.
  • Predict the most effective cross-sell strategy using a rich model library in a drag-and-drop environment to get the most accurate results. Model technologies include, but are not limited to, Decision Tree, K-Nearest Neighbors, Naive Bayes, SVM, LightGBM, and XGBoost.
  • Customized dashboards to clearly show customer behavior, their preferences, buying patterns, and what strategies have worked best for each customer segment.
  • Take action through a case management system, and monitoring the performance of a set of sales strategists so that quick and timely adjustments can be made to ensure maximum effectiveness.

Analysis & Results

Exploratory data analysis Insights

  • Focus more on customers between 35-54 years old.
  • Pinpoint the strategies that work best with each segment.
  • Target marketing campaigns to some region codes and policy sales channels.

Business insights from the model prediction  

By applying the best-fit model recommended by NEMESIS, the insurance company resulted in earning more than $1M profit during the fiscal year.

Without the model built by NEMESIS, the company would do a marketing campaign resulting in a total profit of $9.3 million. However, NEMESIS’s model identified 33,000 customers who would be interested in auto insurance. With a $2.00 million marketing promotion and an estimated $0.73 million loss caused by missing potential customers, the net benefit was about $10 million – an increase of 10.62%. This translates into a $15 saving per customer.

Exploratory data analysis Insights

  • Focus more on customers between 35-54 years old.
  • Pinpoint the strategies that work best with each segment.
  • Target marketing campaigns to some region codes and policy sales channels.

Business insights from the model prediction  

By applying the best-fit model recommended by NEMESIS, the insurance company resulted in earning more than $1M profit during the fiscal year.

Without the model built by NEMESIS, the company would do a marketing campaign resulting in a total profit of $9.3 million. However, NEMESIS’s model identified 33,000 customers who would be interested in auto insurance. With a $2.00 million marketing promotion and an estimated $0.73 million loss caused by missing potential customers, the net benefit was about $10 million – an increase of 10.62%. This translates into a $15 saving per customer.

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