One of our clients—a nutritional platform—faced a challenge that some of the meal plans they were recommending were being rejected by customers. This was impacting sales and hurting their brand reputation. They were interested in addressing this by using an AI model that could predict if a customer was likely to reject a meal plan, and recommend alternatives in case they were.
We developed an ML-based quality control system within the platform's meal plan recommendation engine. The system leverages customer data, preferences, dietary requirements, and platform usage history to predict acceptance or rejection of proposed meal plans.
We analyzed customer demographics, preferences (likes and dislikes), dietary targets, and historical interactions with meal plans on the platform.
Utilizing machine learning algorithms, we created a predictive model that assesses the likelihood of customer acceptance for a given meal plan. The model factors in individual preferences, dietary restrictions, and past engagement with meal plans.
If the ML system predicts the potential rejection of a meal plan, it triggers an alternative plan recommendation. This iterative process ensures that customers receive meal plans aligned with their preferences and increases the likelihood of acceptance.
The system incorporates collaborative filtering techniques, considering similar customers' behaviors and preferences to improve recommendation accuracy.
The ML-based quality control system was deployed on AWS EC2 servers, ensuring reliability and scalability.
The ML-based quality control system correctly flagged 80% of recommended meal plans that would have been rejected by customers, leading to significantly higher acceptance rates and improved customer experience, directly protecting Customer Lifetime Value (CLV).
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Stop customer churn. Our predictive AI model identifies meal plans likely to be rejected, reducing rejection rates and protecting brand reputation.
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AxcelerateAI offers cutting-edge AI solutions, specialising in building and deploying custom machine learning models to help businesses optimise operations, enhance decision-making, and drive growth.