Enhancing retail experiences with synthetic training data

28 Apr 2023

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Retail companies are exploring the use of AI-driven solutions to improve customer behaviour and the overall customer experience with their Point of Sale (PoS) environments. The AI solutions aim to reduce losses, identify and reduce customer friction, resolve product issues, and improve associate efficiency in various ways. This enables them to make better recommendations, offer more personalised experiences, and provide seamless omnichannel shopping experiences. Synthetic training data is also transforming the retail industry by creating new and improved shopping experiences for customers. But how is it transforming these retail experiences and why does it matter?

Improved Personalisation

One of the ways in which synthetic training data is transforming retail experiences is by enabling retailers to improve personalisation. By leveraging this technology, retailers can create more accurate customer profiles, enabling them to make tailored product recommendations, targeted marketing campaigns, and personalised in-store experiences. Retailers can use synthetic data to train their algorithms to analyse customer behaviour and preferences to make better recommendations.

The Shopping Experience

With the future of shopping moving towards cashierless stores like Amazon Go, retailers are increasingly turning to synthetic training data to create seamless omnichannel shopping experiences. Synthetic data can benefit cashierless stores by helping to understand consumer trends, prevent theft, and improve product recommendations. By analysing customer behaviour, synthetic data can help optimise store layout, enhance inventory management, and provide personalised recommendations to customers. Additionally, it has the potential to assist with identifying shoplifting behaviour, reducing losses and in turn, improving profitability. Overall, it can assist such stores in making more informed decisions, enhancing the customer experience, and increasing profits.

Omnichannel shopping is all about providing customers with a consistent experience, regardless of how they choose to shop. By leveraging synthetic training data, retailers can train their AI-powered systems to understand the behaviour and preferences of customers across different channels. This can help retailers to offer a smooth experience by making sure that customers receive consistent recommendations, promotions, and other personalised experiences regardless of whether they are shopping in-store or online.

Improved Product Development

Another way in which synthetic training data is transforming retail experiences is by improving product development. By collecting and analysing vast amounts of data, retailers can identify trends and patterns in customer behaviour that can inform product development decisions. Retailers can use synthetic training data to identify which features customers prefer in a particular product category, such as clothing or electronics. This can help retailers to create products that are better suited to the needs and preferences of their customers.


In order to leverage synthetic training data effectively, retailers need to have access to high-quality, diverse datasets. Synthetic training data can complement real-world images by rapidly generating diverse product images required to train AI models. Mindtech has created a Green Screen Studio that can randomise floors, wall textures, camera positions, and lighting to create unusual angles/colours and lighting to test the AI network against.

We also offer generic models of checkout counters and assets such as baskets and supermarket trolleys to train AI networks. A broad range of scenes are available, and if the customer has a specific scene not already available, We have an Editorial Team that can work with the customer to create the required scene. Pixel-accurate annotations are available to support customers’ requirements, including 2D and 3D bounding boxes, semantic segmentation, and instance semantic segmentation. Depending on the use case, a customer may want to vary the parameters to create a dataset testing a range of key detection parameters for their network. The use of a conveyor belt moving objects under the camera may be beneficial for SKU identification AI networks as it delivers a range of differently positioned SKUs to the camera.

By leveraging this technology, retailers can create more accurate customer profiles, offer more personalised experiences, and improve product development decisions. This can help retailers to create a competitive edge in the market by offering experiences that customers are more likely to engage with. As technology continues to advance, we can expect synthetic training data to become an increasingly important tool for retailers looking to create better shopping experiences for their customers.

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