Consumer buying behavior: How grocery stores can tap big data to meet shopper demand
It's no secret that online shopping continues to redefine consumer buying behavior. While supermarkets have been late to the online party, that's now changing rapidly.
Last month, Target began testing a same-day grocery delivery service with Instacart. Meanwhile, Amazon has been piloting AmazonFresh groceries in select markets to gauge consumer buying behavior, while also testing consumers' appetite for innovations such as Amazon Dash, a Wi-Fi connected device that allows shoppers to order products with a click of a button.
By definition, groceries are a complex business, as food is perishable and storage logistics are complex. However, food is also a high-frequency purchase that's largely insulated from economic downturns. As competition heats up in the grocery aisle, big data is gaining traction as the tool supermarkets need to counter rivals and remain profitable.
These days, retailers can access data on product demand levels on a minute-to-minute basis across their fleet of stores. However, many grocers are still in the infancy stage when it comes to analyzing and monetizing the massive amounts of big data available. This leads to stocking shortfalls, such as assessing product demand based solely on historical data. It can also results in misguided marketing efforts: If a consumer bought ketchup on Saturday, an email coupon for it on Sunday is ill-timed and creates little value for the shopper.
This is where data from store loyalty programs and credit card purchases can come in handy. This information can be used to anticipate shoppers' needs ahead of time. For instance, grocers can use data analytics to determine how often shoppers buy milk, condiments or other products, and then send each household coupons based on their specific purchasing habits.
"You come up with a strong list of 20 items per household that you think they're likely to buy," Sylvain Perrier, president and CEO of Mercatus Technologies, told Marketing Magazine. "On the day the flyer comes out, you send those households a nice email listing those top 20 products, telling them they're on sale at their favorite store."
Enhancing in-store stock management
Perishable groceries such as meat, dairy and seafood call for accurate inventory management, often on an hourly basis. Customer analytics and forecasting tools can help grocers fine-tune their stock levels by evaluating consumer buying behavior and product demand from multiple perspectives and scenarios.
For instance, grocers may want to monitor cycles like when purchases spike for a particular food, buying patterns during sales events, when store traffic peaks or holiday-inspired purchases. According to a report from Manthan, this strategy worked for U.K. grocer Waitrose: Deeper insight into consumer buying behavior and demand patterns via advanced customer analytics and forecasting tools helped the supermarket chain "maintain optimized stocks, achieve faster turn-rates and reduce wastage on their perishables."
Concurrently, retailers can use these strategies to more nimbly adjust their inventory levels and maximize high-purchase items and hot sellers.
Leveraging predictive analytics
Amazon pioneered product recommendation engines: the "if you bought that, you might like this" innovation. This game-changing online shopping feature reflects the retailer's savvy analysis of consumers' shopping baskets.
Recommendation engines are designed to help shoppers discover products they weren't searching for but would be inclined to buy. Today, brick-and-mortar grocers are increasingly tapping the overarching technology behind recommendation engines: predictive analytics. This type of analysis forecasts future trends based on present and past data, and it can help supermarkets boost business. A data-driven, holistic evaluation of "buying triggers," such as seasonality, weather, inventory and promotions, is increasingly informing supermarkets' product mix, sales forecasts and marketing plans, according to a report from Express Analytics.
According to Progressive Grocer, supermarket Ahold USA used predictive analytics to guide its manufacturing and promotional strategies. The company banked on the steady growth of pedometers due to an increased interest in fitness and the emergence of wearable technology. Paul Scorza, CIO of the grocery chain, noted that the predictions to date had been very accurate.
Equipped with these data-driven tools, supermarkets can better identify what products shoppers want today and what they'll be searching for tomorrow, and this knowledge will help them remain competitive for years to come. Connect with big data professionals via IBM's Consumer Products Industry Solutions Page to learn how.