Every day customers generate massive amounts of data through POS transactions, social media interactions, mobile devices and more. Much of this data is filled with valuable insights retailers can use to compete in crowded markets and boost the bottom line---but how to go about it?
In a November 2014 article on Retailcustomerexperience.com, writer Melissa Amadeo---a specialist in retail marketing and analytics---discusses several strategies retailers can use to find real value in big data. In an effort to help retailers leverage existing customer data for insights they can use to make better decisions, increase profits, and drive desired consumer behaviors, a number of these strategies are outlined below:
Have an analytics end-game
“Before any data analysis begins,” says Amadeo, “it’s important for retailers to think about what they want to achieve.” Other big data experts concur with that advice, as all-too often companies seem to do data analysis for analysis’s sake, without implementing specific strategies that align with corporate priorities and business objectives. “If a retailer is looking to increase growth in convenience products”, Amadeo explains, “it may be best to focus on data rendered around assortment optimization, whereas another retailer hoping to raise the level of online sales might take a closer look at data surrounding the effectiveness of direct marketing.”
Take full advantage of existing data
In order to get the most out of existing data, the author calls for companies to go “beyond the traditional one lens view to get the biggest return.” Since much of today’s consumer data is unstructured and multi-faceted, Amadeo advises retailers to take a broad look across their consumer data in order to understand, not just what their customers want but how they want it. Amadeo also cautions that, “Taking advantage of existing data is so much more than relying on a large scale program.” As good a tool as big data analytics can be, Amadeo says that, “it’s really about tying valuable information on consumer behaviors from in-store POS systems, online and mobile purchases and loyalty programs to adjust store layout and product offerings to drive incremental growth.” In other words, taking full advantage of data means taking action on the insights the data provides about customer likes, dislikes, influences and loyalties to create a more targeted and effective marketing strategy.
As evidence of the value of leveraging shopper data for actionable insights, Amadeo says that targeted marketing results in consumer retention rates that are twenty-eight percent higher than mass promotions and untargeted discounts.
Don’t put too much weight on “Loyalty Program” data
Loyalty programs are everywhere these days, from the supermarket to the corner convenience store. These ongoing promotions appear to be win-win because, as Amadeo explains, “loyalty programs frequently reward both the retailer (data) and consumers (in-store discounts)….” Still, the author warns that loyalty program data does not tell the whole story, and she has some facts to back that claim up.
One study cited in the article and carried out by Colloquy, shows that in 2013, U.S loyalty programs grew by 26.7 percent. However, active membership in loyalty programs declined by 4.3 percent. Amadeo attributes the decline to the fact that “shoppers are slowly becoming desensitized to these programs.” Despite the coupons and rewards for shopping at a certain business, it appears that those promotions don’t always translate into lasting loyalty.
Amadeo suggests that the real value of a loyalty program lies in retailers using the data “to improve the overall shopping experience and to respond to trends and demands in real time.” Using data to generate specific offers that reward loyal customers in more meaningful and relevant ways is an effective strategy in retaining loyal customers. While not mentioned in the article, the relatively new practice of installing “beacons” on store shelves to monitor the time customer’s spend with specific products and to send them real-time coupons for those very same products while they are standing in front of them, is a great example of big data in action in a retail setting.
In closing, the author reminds retailers that big data is not a crystal ball solution for discovering key insights. But being that the data is already in place, taking the time and effort to carry out a successful big data strategy can yield big dividends.