I’ll Take Two: How Retailers are Selling, Upselling & Mega Selling with Data Analytics

on October 5th, 2016
Data analytics

Whatever you can do, retail can do better. Retailers are almost always among the earliest adopters of technology, as retail marketing is highly competitive. Such is the case with data analytics, where retailers are capitalizing on ever-increasing views and perspectives of their customers.

For instance, a consumer’s Google search history paints one picture of them, while their social media posts, likes, and shares present a whole new side. When pieces to the puzzle are added to this view — such as their purchase history, shopping history (what they’ve searched for and haven’t bought), their history of opening and responding to marketing emails, etc. — retailers can get an uncannily accurate picture of their customers.

Why is this relevant? Because this information tells them even more about who’s buying, who isn’t, why, and how to get them to buy. For instance, if they know a customer’s age and sex, they’re likely to know what method of payment she prefers. If they know what kind of car the customer drives, they may be able to determine whether he prefers jackets or sweaters when the weather turns cool. Armed with one body of information, retailers can derive a whole new set of information. From there, they can target customers with highly relevant messages at the most opportune times. This means more sells, plus the ability to upsell (sell the customer an upgrade or add-on product) like never before.

Data Analytics Personalizes the Marketing Experience

Retail marketers can also offer customers the pricing model they’re most likely to agree to. For instance, some buyers always go for the rock-bottom price, while others typically like to treat themselves to the luxury of the highest-end merchandise. But most consumers, when presented with a low, mid-range, and high-end pricing structure, will opt for the middle ground. They like to think they’re neither too cheap nor too extravagant. Similarly, retailers can predict whether a customer is likely to be the type of shopper who likes to stock up on the things they need, or the sort to wait until the last minute to order their favorite products. This information not only tells them what that customer is likely to buy, but when they’ll probably be ready to purchase again and how much they’re willing to spend on each purchase.

Data Analytics Isn’t Limited to Online Shopping

Data analytics is often associated with online shoppers and e-commerce, but it’s making its mark in the brick and mortar retail sector, as well. Data analytics can be used to improve the overall revenue of a store, to boost the total sales per customer, the number of customers who buy products during a given visit, and more. For example, some retail businesses have used sensors in their stores to determine the most effective layout for the merchandise. By making modifications to the store layout based on how customers enter, exit, and migrate through the aisles, retailers can assure that more customers purchase more merchandise during each visit.

Balancing Stellar Customer Service with the Creep Factor

While data analytics is responsible for the highly personalized experiences consumers adoringly relate to Amazon, Netflix, and even the good old Google search, retailers have learned that there are downsides to all of the personalization and customization that data analytics is capable of. For instance, Target missed the target big time when they used analytics to determine that a teenage girl was pregnant before her father was made privy to the information. Similarly, by missing the mark, the retailer can easily alienate, rather than endear, the customer. For example, by using the customer’s email address instead of their actual name in email marketing campaigns, retailers step out of the realm of “personalization” and well into the arena of “spam”. Email subject lines like, “Hey, Maverick1956! Do you want …” trigger instant deletes, assuming they escape the jaws of the spam filters to begin with.

While data analytics is one of the most powerful technologies to hit retail stores to date, it’s a double-edged sword. Those who don’t leverage it wisely do so at their own peril.

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