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
- Low customer retention rate
- Lost revenue
- Low satisfaction level in new product
- High cost of customer acquisition
- Negative ROI
Apply Effective Cross-selling to reduce risks
- High cost of customer acquisition
- Negative ROI
- Low customer retention rate
- Low satisfaction level in new product
- Lost revenue
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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