ML Targeted Engagement for Fuel Retailers
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Solution Overview
Problem
Fuel retailers struggle to effectively attract consumers into their stores during fuel purchases, as existing methods such as signage and targeted advertisements are not effective due to a lack of customer data and the annoyance caused by delayed fuel pump operations, resulting in lost revenue.
Innovation Solution
A data-driven machine-learning system that uses visual characteristics from transaction terminals, weather data, and transaction details to predict customer behavior and tailor promotions, determining whether to engage customers and what type of promotion would be most effective in enticing them into the store.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If text offers are displayed on the pump during fuel transactions, then store revenue may increase by attracting customers, but customer experience deteriorates due to delays in fuel pump initiation and payment
Solution Approach 1:
The system performs preliminary analysis of transaction data, visual characteristics, and weather conditions before the fuel transaction completes. The machine learning model pre-determines the optimal engagement strategy and promotion type, so that when the customer is ready to complete the transaction, the personalized offer can be instantly presented without delaying the fuel pump operation or payment process.
2Productivity
If targeted advertisements are attempted, then customer attraction may improve, but system complexity increases due to lack of customer data infrastructure
Solution Approach 1:
The system uses self-service by leveraging data that is already being collected during normal fuel transactions. Instead of requiring complex customer profiling infrastructure, the system analyzes transaction patterns, visual characteristics captured by existing cameras, and weather data to automatically generate personalized promotions. The infrastructure serves itself by repurposing existing data collection capabilities for targeted marketing without additional complexity.
3Productivity
If blast offers are provided to all customers, then some customers may be attracted to the store, but revenue is lost by discounting items for customers who would have purchased anyway
Solution Approach 1:
The system applies local quality by providing different promotion strategies to different customer segments based on their specific characteristics. Instead of uniform blast offers, the machine learning model analyzes individual transaction patterns, visual characteristics, and contextual factors to determine which customers would benefit from promotions and what type of promotions would be most effective. This localized approach ensures discounts are only given to customers who need the incentive to enter the store.
4Adaptability or versatility
If visual characteristics and transaction details are analyzed in real-time, then personalized engagement is achieved, but processing speed may be reduced
Solution Approach 1:
The system performs preliminary analysis of visual characteristics and transaction details as data is being collected during the fuel transaction. The machine learning model is pre-trained and configured to rapidly process these features, generating engagement decisions before the customer completes the fuel transaction. This preliminary processing ensures personalized engagement without adding noticeable delay to the transaction flow.
Data Source
AI summary
A machine-learning algorithm is trained with features relevant to a visual/video analysis performed on subjects conducting transaction at transaction terminals. The algorithm is also trained on weather data known at the time of the transactions and on selective details of the transactions. The algorithm produces as output predictions relevant to: whether a given subject for a current transaction is likely to enter a store, likely items that the given subject might purchase if the subject were to enter the store and likely amount of money that the subject would spend in the store, an effectiveness of providing an incentive for the subject to enter the store, and what type of incentive would most likely entice the subject to enter the store.