Online recommendation engines like Google have changed the way we make our choices. Simple searches for any product bring us relevant results, and searches within online retailers bring us even more tailored results, as these platforms can leverage on our collected data, such as browsing history and purchase patterns. This is not the same for the brick-and-mortar environment, where retailers find themselves with high-volume data and trends of products purchased. On the other hand, mall owners find themselves swamped with different types of data such as footfalls, dwell times, and patterns of browsing. Neither holds sufficient information to create a clear picture of the shoppers’ intent and fulfillment. Without a unified dataset and model, there is no effective way to provide targeted marketing and services to the customer; they often have to count on their industry sensing and intuition.
Have you wondered how good it would feel to enter a store that understands your preferences and gives you entirely personalized recommendations? You can enjoy the full experience of shopping at a physical retailer, feeling the texture, trying on the fit – only better, since it now has a uniquely personal touch. This is where technology can make a difference and supercharge your customer experience, with the power of an “Offline recommendation engine”.
By capturing intelligent video analytics and using machine learning models on customers’ interactions, the gaps in the knowledge of your customers can be effectively bridged. You will get brand-new insights into customer browsing history and purchase patterns, putting you in an equal stance as your online counterparts. This allows you to focus on your physical retail experiences, and offer personalized deals through micro-segmentation as shoppers and visitors enter your stores.
Are you ready to claim your competitive advantage with an offline recommendation engine? We are ready to empower you with our Artificial Intelligence capabilities.