Retail is a fast-paced industry. Margins are tight and competition is steep. Customer retention is a major challenge, as consumers have more choice than ever—and more brands vying for their money and loyalty. Merchants today must figure out not only how to attract new customers, but how to keep their existing ones coming back for more.
Companies that want to gain the edge and stay ahead need to use all the tools at their disposal, like retail data analytics.
Turning Customer Data into “Gold”
Customer data is a natural byproduct of running a retail company. People are essentially leaving a trail of behavioral clues as they move through the sales funnel. But piles of customer data without the tools to analyze it are worthless.
Today’s retail analytics platforms like ThoughtSpot provide users with instant insights based on stored data. One important distinction from legacy systems of the past is that the best tools today are user-friendly for all—from merchandisers to marketers; brand managers to store associates. This means that instead of having to rely on IT and data specialists alone, employees throughout an entire company can ask questions about customer data and receive answers in seconds.
Data analytics platforms turn customer data into insights. Only then can employees turn these insights into “gold” by factoring them into intelligent decision-making.
Use Data to Segment Buyers—Then Personalize
Retailers stand to benefit from knowing everything they possibly can about groups of customers. The ability to use data to divide customers into segments, based on behavior and demographics, helps merchants target people with more relevant offers and interactions.
People crave, and expect, increasingly personalized shopping experiences these days. No matter what they’re buying and how, shoppers want to feel like more than just a number. One-size-fits-all, or even one-size-fits-many, approaches to marketing and selling are becoming obsolete.
Customers can smell generic messaging a mile away; they’re practically immune to it by now. The best way to cut through the noise and capture potential customers’ attention is to boost relevancy. How? By using data analytics to understand customer behavior and preferences.
Imagine a convenience store in a small town in the past—here, it would be commonplace for salespeople to know shoppers’ names, preferences and habits. So, as soon as someone entered the store, the associate would already have an idea of what they might want and need: “Hiya, Sam. Nice to see you. The usual today? We also just got in these new imported chocolates. I know you have a sweet tooth; you should try one!”
But today, more transactions are taking place online or in large retail settings without these close personal bonds. So, all retailers have to go on is how customers are likely to act based on attributes like gender, age, income level, geographic location and past interactions with the brand. The more granularly you’re able to segment company data, the more relevant and targeted you can make each customer journey—which in turn influences people to buy.
One large retailer in the U.S. used data analytics to segment customers, then figured out how to best understand and serve the buyers driving the most revenue. As Analytics Magazine reports, the retailer first defined population segments by characteristics, then used these segments “to guide decisions from store layouts to how staff interacts with customers.” Using data analytics to drive better decision making helped earn the store an 8.4 percent in sales and a 15 percent increase in total revenue.
Making sense of your customer data is a matter of having the right analytics tools and then using it to figure out what shoppers want most from your business.
