GMO Marketing Connect's AI Recommendation Engine Revamped for Stores, Personalized Delivery Target Expanded 18-fold [GMO Commerce]
GMO Commerce has revamped the AI recommendation engine for its store promotion DX tool, "GMO Marketing Connect." This enhancement expands the discovery of high-purchase-potential customers by up to 18 times, enabling more personalized information delivery. The AI analyzes customer purchase history and behavioral patterns to automatically execute optimal approaches based on the situation.
📋 Article Processing Timeline
- 📰 Published: May 7, 2026 at 20:00
- 🔍 Collected: May 7, 2026 at 11:31
- 🤖 AI Analyzed: May 8, 2026 at 01:57 (14h 25m after Collected)
GMO Commerce Inc. (Representative Director and President: Masato Yamana, hereinafter "GMO Commerce"), a member of the GMO Internet Group, has completely revamped the recommendation engine of "GMO Marketing Connect" with development support from the GMO Internet Group AI Research and Development Office.
What changes with this new feature is the accuracy and breadth of "who to deliver to." By combining purchase history and behavioral patterns, for example, for a customer who purchased product A, it becomes possible to more accurately analyze their preferences, such as "this person seems to like product B more than A." As a result, the number of "customers with high purchase potential" who were previously missed has expanded 18 times (※1). Furthermore, the AI classifies customers by situation and identifies customer segments that were previously unseen, enabling diverse approaches from customer nurturing to new purchases and churn prevention.
The broader the delivery target, the more accurate information can be delivered to more customers, leading to the maximization of promotional effects for stores. Through this feature, GMO Commerce will further accelerate store marketing DX.
(※1) Verified by 4 companies, the number of target recipients increased up to 18 times (according to our verification using a development system from the GMO Internet Group AI Research and Development Office).
[Background]
In business categories with physical stores such as retail, food service, and entertainment, 57-61% (※2) of consumers want to receive information tailored to them. On the other hand, 53.7% (※3) feel uncomfortable when products not aligned with their lifestyle are proposed, and there is persistent resistance to irrelevant deliveries. In other words, consumers continue to be in a situation where they "seek personalized information but receive irrelevant information," and the background to this was the challenge of not being able to sufficiently identify the customers to whom information should be delivered.
Furthermore, an era is approaching where AI agents analyze "what kind of things this person likes" and, on behalf of the user, handle everything from shopping suggestions to purchases with "this is recommended for you" (Agentic Commerce). In such a scenario, whether an AI agent judges "this store delivers information tailored to the user" will become a new decisive factor in attracting customers.
GMO Commerce possesses unique customer data that can effectively utilize AI. To further leverage this data, we have incorporated the technological capabilities of the GMO Internet Group AI Research and Development Office, leading to a complete revamp of the recommendation engine that identifies more customers to be targeted for delivery and automatically sorts them by situation.
(※2) (※3) According to Macromill, Inc. as of September 2025 https://www.macromill.com/service/knowledge-blog/marketer-column-027/
[Features of the New Function]
■ Number of "customers with high purchase potential" expanded 18 times, and prediction accuracy improved
Through this development, it is now possible to analyze not only customers' purchase history but also their behavioral patterns. This allows us to discover customer segments that could not be approached before as delivery targets, and the number of "customers with high purchase potential" has expanded 18 times. Furthermore, the accuracy in more precisely judging the purchase probability of each individual has improved.
■ Customers automatically classified by situation, allowing immediate understanding of who to deliver to
AI automatically sorts customers by situation based on their purchase experience and purchase probability. The person in charge only needs to select the group they want to approach and create delivery content suitable for that group, thereby realizing personalized delivery for each individual.
For example, for a customer group that has purchased in the past but has recently shown low engagement, a natural trigger is more effective than directly pushing products. In such cases, approaches tailored to the customer's situation, such as special offers timed with the approaching expiration date of points, can be realized without specialized knowledge.
■ Utilize existing channels as they are, completed in 4 steps
It is completed in 4 steps: "① Product selection → ② Appeal axis selection → ③ Group selection → ④ Delivery." Since existing channels such as LINE, Instagram, official store apps, and email can be used as they are, no new costs or effort are required. By automatically executing the optimal approach according to customer situations, which was difficult to find and deliver manually, not only is operational efficiency improved, but personalized delivery of even higher quality is realized.
[About "GMO Marketing Connect"]
URL: https://www.gmo-c.jp/lp/marketing-connect/
What changes with this new feature is the accuracy and breadth of "who to deliver to." By combining purchase history and behavioral patterns, for example, for a customer who purchased product A, it becomes possible to more accurately analyze their preferences, such as "this person seems to like product B more than A." As a result, the number of "customers with high purchase potential" who were previously missed has expanded 18 times (※1). Furthermore, the AI classifies customers by situation and identifies customer segments that were previously unseen, enabling diverse approaches from customer nurturing to new purchases and churn prevention.
The broader the delivery target, the more accurate information can be delivered to more customers, leading to the maximization of promotional effects for stores. Through this feature, GMO Commerce will further accelerate store marketing DX.
(※1) Verified by 4 companies, the number of target recipients increased up to 18 times (according to our verification using a development system from the GMO Internet Group AI Research and Development Office).
[Background]
In business categories with physical stores such as retail, food service, and entertainment, 57-61% (※2) of consumers want to receive information tailored to them. On the other hand, 53.7% (※3) feel uncomfortable when products not aligned with their lifestyle are proposed, and there is persistent resistance to irrelevant deliveries. In other words, consumers continue to be in a situation where they "seek personalized information but receive irrelevant information," and the background to this was the challenge of not being able to sufficiently identify the customers to whom information should be delivered.
Furthermore, an era is approaching where AI agents analyze "what kind of things this person likes" and, on behalf of the user, handle everything from shopping suggestions to purchases with "this is recommended for you" (Agentic Commerce). In such a scenario, whether an AI agent judges "this store delivers information tailored to the user" will become a new decisive factor in attracting customers.
GMO Commerce possesses unique customer data that can effectively utilize AI. To further leverage this data, we have incorporated the technological capabilities of the GMO Internet Group AI Research and Development Office, leading to a complete revamp of the recommendation engine that identifies more customers to be targeted for delivery and automatically sorts them by situation.
(※2) (※3) According to Macromill, Inc. as of September 2025 https://www.macromill.com/service/knowledge-blog/marketer-column-027/
[Features of the New Function]
■ Number of "customers with high purchase potential" expanded 18 times, and prediction accuracy improved
Through this development, it is now possible to analyze not only customers' purchase history but also their behavioral patterns. This allows us to discover customer segments that could not be approached before as delivery targets, and the number of "customers with high purchase potential" has expanded 18 times. Furthermore, the accuracy in more precisely judging the purchase probability of each individual has improved.
■ Customers automatically classified by situation, allowing immediate understanding of who to deliver to
AI automatically sorts customers by situation based on their purchase experience and purchase probability. The person in charge only needs to select the group they want to approach and create delivery content suitable for that group, thereby realizing personalized delivery for each individual.
For example, for a customer group that has purchased in the past but has recently shown low engagement, a natural trigger is more effective than directly pushing products. In such cases, approaches tailored to the customer's situation, such as special offers timed with the approaching expiration date of points, can be realized without specialized knowledge.
■ Utilize existing channels as they are, completed in 4 steps
It is completed in 4 steps: "① Product selection → ② Appeal axis selection → ③ Group selection → ④ Delivery." Since existing channels such as LINE, Instagram, official store apps, and email can be used as they are, no new costs or effort are required. By automatically executing the optimal approach according to customer situations, which was difficult to find and deliver manually, not only is operational efficiency improved, but personalized delivery of even higher quality is realized.
[About "GMO Marketing Connect"]
URL: https://www.gmo-c.jp/lp/marketing-connect/