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These kinds of recommendations help online stores and e-commerce websites to upsell their products to shoppers, and as a result online shopping is getting more and more market share in the overall shopping universe year over year.
What is stopping businesses from bringing the same experience to their stores? What are the limitations and how can these limitations be resolved?
We’re entering the age of personalization and adaptivity where everything is tailored. Shoppers are looking for shopping experiences customized to their needs. This requires anticipating their needs and wants and providing offers and recommendations that are in line with these needs. This is the main differentiator between online and in-store shopping.
E-commerce systems collect and utilize data to generate recommendations and suggest more products which customers are also likely interested in. These recommendations are generated based on what customers already have in their shopping cart as well as what other customers with similar profiles bought.
The same data can be captured and utilized in stores while customers are shopping. Data, such as a customer’s buying sequence and in-store physical movement around the store can be captured and mined for more meaningful insights. In addition, this data can be used to generate recommendations and up-sell products to shoppers.
How can this functionality be delivered to shoppers in store? The answer is an adaptive in-store recommender system that suggests different items along with their store locations to shoppers in real-time based on what they currently have in their shopping carts, their order history and segmentation analytics. This approach also increases shopper satisfaction by providing them with faster checkouts.
This solution can be fully implemented for different sized stores, along with a loyalty program and a mobile app for stores that don’t currently have a loyalty program, or can be added to the existing mobile apps and loyalty program if the store already has one.
By using this in-store recommendation system, customers are provided with a better shopping experience and businesses increase their sales revenues, reduce their waste and track customer behavior. Best of all, the captured data can be utilized to increase the accuracy of promotional campaigns and store’s planogram, and to optimize store floor plans to either maximize or minimize customers walking distance based on different use cases and business strategies. This is the next generation of the in-store shopping experience. The retail transformation is here.
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