Using mobile predictive analytics to improve customer experiences | Mobile Business Insights

Predict customers needs and trends
No one needs to tell retailers that they’re operating in a competitive landscape. Consumers now have more brands to buy from and more ways to buy than ever before. If a store doesn’t have the right item in stock, or the right price on that item, customers can find another retailer that does with just a few taps on their smartphone screens.

Predictive analytics helps retailers fine-tune inventory, get ahead of trends and avoid stock-outs so that customers can always find what they want. Retailers can also use predictive algorithms to optimize pricing based on what competitors are offering or to personalize pricing for valued customers.

As another example, imagine that a customer has viewed a particular refrigerator on a retailer’s mobile app several times. He’s also checked pricing on Amazon, and it’s cheaper there, but he wants to see it in person before buying. As soon as he enters the retail store, predictive data analysis surmises that he wants to see the fridge and triggers a mobile app alert offering to match any competitor’s price or waive the delivery charge if he buys today.

Speaking of delivery, instead of giving the customer a four-hour window in which he needs to be home to sign for the fridge, the retailer can use predictive analytics to determine what time would be most convenient for him (and everyone else scheduled for a delivery that day) based on mobile location data, traffic patterns and route-optimization algorithms.

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Using mobile predictive analytics to improve customer experiences.