How retailers use mobile predictive analytics to improve customer experiences
You don’t always have to be a fortune-teller to know what will happen next. Whether it’s knowing just when their kids will start arguing during a summer road trip, which employees will be late to work on the fifth of July or when they’re about to have car trouble, most people can predict some future events with near-perfect accuracy. It’s a matter of experience and access to information.
Predictive analytics enables retailers to gain similar insights into mobile customers. By using statistical techniques such as data mining, modeling and machine learning, these advanced analytics tools can identify the likelihood of future outcomes, including consumer behavior.
How can retailers use that information to improve the customer experience for mobile users?
Stop customer churn before it starts with predictive analytics
Mobile predictive analytics enables organizations to look at the customer experience in context, predict when someone is losing interest — and, to some degree, why they’re losing interest — and then launch targeted content to re-engage them. Say a fashion retailer has a mobile customer who hasn’t opened the app in months. She liked the brand enough to download the app, and she hasn’t deleted it from her phone — so why isn’t she using it? Is she a flight risk? And how much does the brand stand to lose if it loses her?
Predictive analytics can help answer these questions by examining her buying history (maybe she only uses the app around gift-giving holidays), her previous purchases (maybe this season’s trends aren’t really her style), her relationship with the brand (maybe she’s called in recently to complain or expressed negative views about the company on social media) and even her social data (maybe her friends have also been losing interest in the brand, and she wants to wear what they’re wearing). Incorporating social data is particularly important, considering consumers say social networks are their #1 source of online inspiration for purchases, according to PwC.
Advanced analytics tools can then calculate the lifetime value of the customer and trigger personalized special offers that address the customer’s pain point and reflect her value to the company. For example, if her friends have been churning, the brand could send her a special “friends and family” discount offer and suggest a shopping spree with her gal pals, or send marketing messages reminding her that her style makes her unique and that she wants to stand out. Different retargeting strategies might work, but brands don’t even get the chance to try unless they can predict when customers are losing interest.
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.
Improve the in-store customer service experience
Predicting customer behavior also comes in handy for brick-and-mortar retailers, helping stores streamline operations and personalize the customer service experience for individual shoppers. For example, store-traffic analysis helps managers ensure they have enough staff for each shift and direct those staffers to high-traffic areas based on real-time customer demand. This way, customers don’t have to go searching for help or stand in long checkout lines.
Stores that use beacons and other indoor mapping technology can also integrate predictive traffic analysis into mobile apps to help users plot the most efficient routes and avoid congested areas of the store during high-traffic days and times.
Convenience is an important aspect of customer service, but so is the actual service. By equipping store associates with tablets and analytics solutions, retailers can provide the customized recommendations that customers would get online, but in person. These solutions help associates predict what items customers will like based on their preferences and browsing history, social data, what other customers bought together and any other useful data. That combination of personalized insights and personal service creates a great customer service experience — the kind of experience that customers talk about, post about and come back for.