Online retailers increasingly direct products to a user’s landing page, highlighting products that may be of interest to them based on purchasing history. But how can this approach be applied to real world shopping? US-based online grocery store uses customer purchase histories to predict when items will run out. Using similar data, PriceSwarm is an app offering tailored shopping lists to customers who upload their receipts.
The app analyzes users’ habits to offer real-time feedback on their budget, and let them build up a repertoire of frequently bought items. It will also suggest cheaper comparable products, as well as deals various stores are running. Buying routines are also learnt, and the app will notify users when they’re about to run out of a necessary product. PriceSwarm will even notify a user that they haven’t purchased an item that they usually would, saving repeat trips to the grocery store. The crowdsourced data from users can then be used by local retailers to time promotions or discounts.
Tailored shopping lists can change the way users approach the grocery run, making the process less time consuming. Could these apps also integrate with map and traffic data, factoring in the time taken to travel to and from stores?
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