Generative AI is changing mystery shopping processes globally by increasing accuracy, efficiency, and reliability of data collection. By integrating automation of image analysis and human-in-the-loop (HITL), market researchers and their customers (brands, retailers) can now convert unstructured images into actionable insights, while reducing issues related to missing observations, subjective biases from multiple shoppers, and inaccuracies from manual data entry.
Accurate Data Collection
Generative AI helps with the product identification and analysis of in-store items by automating the manual data entry and analysis. Images collected through mystery shoppers (or even robotic cameras) can now be analysed for product and pricing details, placement, promotions, etc. at a much faster rate. In parallel, HITL systems allow for human intervention to validate flagged anomalies and to handle edge cases (such as, new products, planogram changes, etc.). Vision AI with HITL helps reduce errors in the collected data for better results.
Consistent Reporting
Mystery shopping processes often rely on the observations of the shoppers. These subjective observations often lead to biased data varying from shopper to shopper. Generative AI solves this subjective bias by standardising the shopper data and providing objective visual evidence. In addition, HITL enhances this process through human intervention in order to interpret edge cases that AI may struggle with. Such cases can be the assessment of a poorly placed product or identification of subtle compliance violations. This two-layered approach ensures higher consistency in the reported data.
Real-Time Monitoring
With Vision AI, mystery shopping operations can now achieve real-time monitoring. Cameras powered by computer vision can instantly identify some of the operational issues like misplaced products and empty shelves. In the event a system flags an ambiguous situation—such as improper stocking—human reviewers can jump in to provide the required clarity. This real-time feedback improves the efficiency and optimises the process for timely corrections.
Addressing Edge Cases
Multiple factors such as promotions and seasonal inventories make the retail environment highly dynamic. These factors affect the store layouts, product categories, prices, and thus customer behavior. Generative AI assisted by HITL enables analysts to generate insights in real-time. A human expert can solve an edge case by providing valuable feedback on unclear and rare scenarios. For instance, human experts can help make the process smooth by filling in the gaps of the data collected by the AI during a rush event such as a holiday sale.
Reducing Operational Costs
Generative AI helps reduce the reliance on manual labor in mystery shopping programs. By automating tasks such as data entry, image annotation, and reporting, AI helps in scaling the mystery shopping operations. In concurrence, HITL makes sure that the complex tasks which require human expertise are handled efficiently. It helps improve the accuracy and speed which enables the businesses to scale their operations.
Integrating Generative AI and HITL can transform the mystery shopping operations into a much more accurate and scalable process. This two pronged approach can help market researchers and their customers (brands, retailers) optimize their operations and enhance customer experiences effectively.
