Personalized Retail Interaction via Predicted Shopping Lists
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Solution Overview
Problem
The grocery industry faces challenges in effectively utilizing point-of-sale data to provide personalized shopping lists and promotions to customers, as existing data mining methods are imprecise and lack individualized channels for communication, leading to missed sales opportunities due to forgotten items and ineffective promotion delivery.
Innovation Solution
A system and method that creates customer models based on transaction data to predict shopping lists and provide personalized promotions, using a combination of customer identification, model creation, and communication components to deliver targeted content on mobile devices, optimizing promotion planning and inventory management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If clustering or segmentation strategies are used to profile customers based on similar customers, then data sparseness problem is overcome and variance in shopping behaviors is handled, but precision on any one customer is lost
Solution Approach 1:
The patent segments customers into different types based on their shopping behavior patterns (e.g., price-sensitive, brand-loyal, impulse buyers) and creates customized interaction strategies for each segment. This allows the system to maintain precision for individual customers while collectively handling data sparseness through segment-level patterns.
Solution Approach 2:
The patent applies different levels of personalization to different customers based on data availability and customer value. High-value customers with sufficient transaction history receive highly personalized interactions, while customers with sparse data receive segment-level personalization. This local quality approach optimizes precision where possible while maintaining reliability across the entire customer base.
2Loss of information
If direct mail is used for customer communication, then customers can be reached outside shopping times, but attention is not captured as customers are not actively thinking about needs
Solution Approach 1:
The patent uses transaction data to predict what customers will need before they shop, and delivers personalized promotions and shopping lists to mobile devices during the shopping trip. This preliminary action ensures customers see relevant information at the moment of need, capturing attention effectively.
Solution Approach 2:
The system allows customers to opt-in to receive personalized communications on their own mobile devices, giving them control over when and how they receive information. This self-service approach increases engagement as customers actively choose to receive relevant content rather than being passively contacted.
3Loss of information
If coupon based initiatives are delivered at checkout-time, then customers receive promotions, but they are seen as irrelevant as delivery is after the point of sale
Solution Approach 1:
The patent delivers personalized promotions and coupon information to customers' mobile devices before they reach the checkout point, based on predicted shopping lists and real-time location tracking. This allows customers to see and act on promotions while still shopping, making the information relevant and actionable.
Solution Approach 2:
The system continuously monitors customer location, shopping progress, and interaction with promotions, adjusting the delivery of coupon information in real-time based on customer behavior feedback. This ensures promotions are delivered at the optimal moment when customers are most likely to be influenced.
4Ease of operation
If personalized information is delivered to customers at multiple points in the store using mobile devices, then customer engagement is improved, but system complexity increases
Solution Approach 1:
The patent uses a universal mobile device platform that customers already possess (smartphones) to deliver all personalized interactions, eliminating the need for specialized retail hardware. The system performs multiple functions including location tracking, promotion delivery, shopping list management, and purchase tracking through a single mobile application.
Solution Approach 2:
The mobile device acts as an intermediary between the customer and the retail system, handling all complex communications and data exchanges. This mediator approach simplifies the overall system architecture by centralizing the interaction interface on the customer's device rather than requiring multiple distributed retail system components.
Data Source
AI summary
A method and system for using individualized customer models when operating a retail establishment is provided. The individualized customer models may be generated using statistical analysis of transaction data for the customer, thereby generating sub-models and attributes tailored to customer. The individualized customer models may be used in any aspect of a retail establishment's operations, ranging from supply chain management issues, inventory control, promotion planning (such as selecting parameters for a promotion or simulating results of a promotion), to customer interaction (such as providing a shopping list or providing individualized promotions).