Hammerson, a European retail owner, manager, and developer, has recently debuted its new FindSimilar app to rave reviews from consumers who made use of it. The app, which was developed by tech company Cortexica, uses machine learning technology to recognize images of products and recommend similar items throughout the retail location in which the consumer is shopping.
Earlier this year, the FindSimilar app was piloted at one of Hammerson's London locations, the Brent Cross Shopping Center. 90 percent of consumers who used it there said they found the app useful, and 92 percent said that they would use it again. Further, 94 percent said the app would encourage them to visit different stores. With such sterling results, Hammerson has decided to extend the FindSimilar app to its complete portfolio of shopping centers across the UK and France.
Retail Comparison Apps
The FindSimilar App Helps Shoppers at Hammerson Stores Discover New Items
Trend Themes
1. Machine Learning-powered Retail Apps - Retailers can leverage machine learning technology to create personalized shopping experiences for customers, increasing brand loyalty and driving sales.
2. Visual Product Recommendations - Using image recognition technology, retailers can offer personalized product recommendations to customers, making the shopping experience easier and more convenient.
3. Customer Feedback Analysis - Retailers can use customer feedback from apps like FindSimilar to gain insights into preferences and adjust their product offerings accordingly.
Industry Implications
1. Retail - Retailers can enhance the in-store shopping experience and compete with e-commerce giants by leveraging technology like the FindSimilar app.
2. Tech - Tech companies can capitalize on the growing trend of machine-learning powered retail apps to create innovative products and services for retailers.
3. Data Analytics - The use of retail comparison apps presents an opportunity for data analytics companies to provide valuable insights to retailers based on customer behavior and preferences.