Personalizing the shopping experience is increasingly crucial for retailers as they try to strengthen ties with their customers and, hopefully, retain them as lifelong patrons. In fact, many stores wish to gain a better understanding of what they call the "customer journey" – that is, the path the consumer takes from researching the item to finally purchasing it.
Your guide to buy
With so much information at their fingertips nowadays, customers can easily compare prices and read up on fellow shoppers' reviews of the products before even setting foot in a brick-and-mortar store. Guiding the potential buyer from clerk to cash register now seem oddly traditional and old-fashioned. While patrons have much more information to educate themselves with before handing over their money, retailers can also use big data predictive modeling software to better know their customers and create a more personalized shopping experience.
IBM's Big Data and Analytics Hub blog noted that using analytics to understand the journey of the customer can make businesses' future marketing approaches much more effective. A retailer gets a better perspective of which items to market to which patrons, when the best time is to cut prices and how to increase customer loyalty.
"Predictive analytics gives insight into the customer journey."
Kroger, one of the nation's largest grocery store chains, already uses analytics to try to walk a mile in each of its customers' shoes, IBM noted. The supermarket hopes to gain enough information about its shoppers so it can develop individualized profiles for each one, instead of grouping them into faceless segments. Predictive analytics can eventually give Kroger the insight it needs to create marketing pitches and coupons tailored to every one's particular preferences.
Crafting the store experience
Predictive analytics can also help retailers with fewer in-house IT resources compete with their larger companies. Forbes pointed out Stage Stores, a department store chain with locations in 40 states, uses big data solutions to meet customer demands and to stay head-to-head with larger stores such as Macy's.
While it used to be up to staff members to determine the best time to markdown prices, Stage Stores found using predictive analytics to pick the best time for a sale was much more profitable. The stores decided to implement the suggestions it received after analyzing the information and decided to reduce prices incrementally instead of making dramatic cuts at the end of the season.
Predictive analytics and big data have the power to give retailers insight into the customer journey, so they can give their patrons a better shopping experience.