Company: Samsung Life Insurance Company
Company background: Samsung Life, established in 1957, has been a leader in Korea’s insurance industry, upholding the noble value of insurance. We walk alongside our customers as their life financial partner, offering products and services that provide genuine value. Samsung life is involved in various aspects of the insurance industry, including the development, sale, and management of life insurance products, as well as corporate pension and asset management services.
Contact: Sun-young Park
Title: Senior Manager
Country: Korea
Award Category: ROI Rock Star
What was the business need/presented problem you were faced with?
Samsung life has been using SAS since 2000.
With SAS, We analyze demographic data, insurance underwriting data, claim data, and contact data of company or agent of Samsung Life Insurance customers to identify target customers for our sales channels and implement effective marketing programs through customer segmentation, ultimately increasing customer value.
As Samsung Life Insurance relies heavily on face-to-face channels, providing timely and accurate customer data to insurance agents is a significant challenge.
To meet this challenge, we need to collect diverse data and explore accumulated data quickly. It is necessary to gain insight through various statistical analysis and modeling.
How did you use SAS to increase revenue/save money for your organization?
Samsung Life has 12 million customers, and customer management and upsell are very important businesses. Customer management and upselling are important in our business, but reaching out every12 million customer incurs lots of costs in terms of both time and money. Therefore, we use SAS to analyze customers in 2-track as follows.
First of all, we always strive to contribute more to improving the performance of sales channels by accurately predicting and targeting which customers will buy policy.
We also want to assigned new customers and markets by finding and notifying agents of ‘inactive’ customers that they did not know about.
In other words, out of 12 million customers, customers to be managed with the highest priority are selected and assigned to agents.
In reality, 65-70% of our sales come from customers, and analysis using SAS plays a major role in streamlining marketing costs and increasing the efficiency of agent’s activities.
What SAS products did you use and how did you use them?
Samsung Life uses SAS Enterprise Guide and E-Miner Product. 50% of the marketing team members have the ability to use SAS EG as a basis, and conduct pre-analysis to establish marketing strategy and post-performance analysis through SAS. In particular, the CRM part is a group of experienced SAS users who perform modeling through E-Miner as well as EG.
Basically, after analyzing customer attributes using EG, we target customers by combining sales know-how in the actual field.
In order to advance targeting, after modeling by E-Miner, we use scores for each customer.
Recently, an ensemble model combining logistic regression analysis and random forest was used to perform customer upsell modeling, and a marketing campaign was conducted targeting the top of the score.
In addition, through various clustering, inactive customer groups were discovered and reflected in the system to support the sales activities of agents.
What were your results? Please quantify.
The results of marketing using the two analysis themes mentioned above are as follows. In general, the upsell rate of retained customers is around 0.7% per month,
but the upsell rate of target customers is 2.6%, which is three times higher.
In the case of inactive customers, customers who did not generate sales showed an upsell rate of 2.1% during the marketing campaign period.
It is expected that the performance will be further expanded if the marketing that utilizes existing customers is expanded through future analysis.
