Retrieving, integrating and summarizing descriptive data is important for retailers—and it’s old hat. Collecting data that summarizes what happened in the past is not a challenge for organizations, and the retail industry has been doing it for years. But as customer expectations change, the way retailers use data must follow suit.
Why now? You’ve heard this before, but it bears repeating: Customer expectations are evolving rapidly. What used to engage them no longer feels relevant. Shoppers want instant gratification and a seamless, continuous and contextual experience across all retail channels, including brick-and-mortar, e-commerce, store kiosks, call centers and mobile apps.
To make this happen, retailers are moving away from mass promotions that assume customers are interested only in what happens to be on sale. One size does not fit all—nor does one coupon, email or banner ad. What’s the point of offering a coupon for a type of cheese a customer would never eat? Or promoting a sale on Android phone cases when the customer has an iPhone?
In addition, retailers are implementing digital technologies to offer customers more meaningful information—whether that means learning about products, getting a relevant promotion or offer, checking stock availability or finding out how crowded a store is before they make the trip.
Less descriptive, more predictive
Offering a personalized consumer experience requires moving from descriptive data to predictive data. No longer can retailers look at the past to decide what to do next. Instead they must use smart data to mine patterns and present information in a visual way that provides actionable insight and anticipates outcomes.
Fortunately for retailers, there’s no shortage of data out there. Between point-of-sale (POS) captures, clickstreams, call centers and social listening, retailers have plenty to pull from. This data should be enriched with business rules that drive specific objectives and strategies, a combination that enables retailers to stitch together each shopper’s journey across channels using historical data, demographic data, preferences, attitudes and interests for a 360-degree view of the customer.
What would a customized retail experience built on predictive data look like? Imagine a grocery store that collects transactional customer data from all its touchpoints—POS, online commerce, social behavior, call center transcripts and other connected channels—then combines it with sensor data from smart-connected devices in the store (such as an iBeacon), analyzes it and pings the customer to recommend products they might like or a discount perfectly suited to their needs.
Using this predictive data, the store could identify when a customer is headed in to do some shopping, calculate that it’s been a while since that person made chili, then—through mobile and wearable devices—offer promotions for chili ingredients and even help the customer navigate the store.
For an added level of engagement, a store employee with a tablet, a smartwatch or another wearable device could extend the experience to the point of purchase by finding the customer at checkout with an ingredient they forgot to pick up.
By moving to a more predictive approach, the retail sector can use data to fuel marketing strategies and campaigns that drive conversions. To learn more about creating personalized omnichannel retail experiences, check out the Mindtree Phy-gital Shopper Survey.
To find out more about how to personalize the customer experience with digital initiatives, download the Mindtree e-book Get Personal to Make Digital Real.
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