HSAD Uses AI on Customer Data to Double Purchase Conversions

Average Purchase Amount Also Rises 18%

Finance|
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By Kim Kyung-taektaek@sedaily.com
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null - Seoul Economic Daily Finance News from South Korea

HSAD said on the 25th that it has demonstrated results such as identifying potential customers through a solution that uses artificial intelligence to analyze brands' customer relationship management (CRM) data.

The solution HSAD adopted analyzes behavioral data with AI, including a customer's purchase history, purchase cycle, coupon usage and wish lists. It identifies new potential customers and then serves them tailored advertising.

HSAD worked with Markettap, an AI solutions company, and applied the approach to marketing for a health supplement brand over the past three months. The purchase conversion rate among customers selected by AI based on CRM data was 2.1 times higher than that of general ad viewers. The average purchase amount, tallied from the company's own purchase data, was about 18% higher. This means that not only did more customers who saw the ads go on to make purchases, but the amount each customer spent also increased.

The results were even more pronounced in certain customer segments. Potential customers identified by AI based on the behavioral patterns of existing customers who had not made a purchase for a certain period showed a purchase conversion rate about seven times higher than that of general ad viewers. HSAD said the case shows that customer segments can be analyzed and marketing efficiency improved based not only on short-term performance but also on long-term customer lifetime value (LTV).

An HSAD official said, "This project went beyond simply using CRM data for advertising, advancing to the point where AI analyzes actual customer behavior to determine which customer segments to use as the basis for ad targeting."

Original reporting by Kim Kyung-taek for Seoul Economic Daily.

AI-translated from Korean. Quotes from foreign sources are based on Korean-language reports and may not reflect exact original wording.

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