Last updated on June 13th, 2024
by Katie Hotze, CEO of Grocery Shopii
Predictive analytics has exploded on the scene in grocery retail, with Amazon delivering a master class on the power of predictive technology that motivates shoppers to buy more product more often when the suggestive pairings are done for them.
Late last year, McKinsey & Company reported that more than one-third of Amazon’s revenue was generated by its proprietary recommendation engine that feeds suggestions to busy people. This both improves their experience and expedites the shopping journey.
Furthermore, Amazon’s digital orders skyrocket 369 percent when customers engage with the site’s personalized recommendations. That data should send chills down our spines – this is where every grocer should be doubling down on their digital strategy.
Recommendations work because shoppers don’t want to think. They have kids, jobs, busy lives and – news flash – few consider the grocery store a wonderland for creativity. For most of us, it’s just a chore.
Consider every time you’ve walked into the store and forgot the grocery list or, more likely, never created one. So you’re left wandering the aisles aimlessly in search of dinner.
Shoppers adore automated suggestions for this reason, as they support the chaos of our busy lives by simplifying redundant responsibilities and reminding us when we’ve forgotten something.
At a minimum, supermarket E-commerce should drive suggestions based upon common pairings. So when adding taco shells to the online cart, we’re also offered salsa, ground beef and cheddar cheese.
This is predictive analytics in its simplest form. If 100 people in the past purchased this bundle, then you’ll also be offered the bundle. This engine has the power to expedite shopping journeys, please customers and earn more dinners simply by doing the thinking for the shopper.
Now take this example and go broad with its application and the opportunity to be a growth engine for grocery. Connecting like-items with the greatest statistical likelihood of conversion to a sale accomplishes a few essential needs simultaneously.
Personalized recommendations transform impersonal advertisements into appealing offers that grow bigger baskets and create happier shoppers simply by dialing up trend data and applying it to forecast future behavior.
Gone are the days when I had time to rip recipes out of a culinary magazine, jot down every item needed at the store, and have time to conduct the shopping trip. This process was all pre-digital and, for me, took at least three hours from start to finish.
That cooking magazine was my recommendation engine. However, the entire experience can be emulated digitally today. Thanks to data, science, speed and convenience find their places in the digital grocery shopping journey by driving thoughtful, data-driven suggestions.
This artform of tracking trend data, identifying correlations and testing offers on shoppers to build bigger baskets generates vast revenue potential for retail.
Beyond the obvious revenue lift, recommendations boost retail sales by intensifying that loyalty with the shopper. Delivering a more personalized approach that expedites shopping is a winning strategy to penetrate market share from shoppers with the greatest need for time-saving convenience.
Recommendation engines will motivate shoppers to purchase more products with greater frequency, plus create a trust and willingness to sample new products by feeding them through the recommendation system.
Get closer to what customers really want, how they shop and what offers convert their carts, and you will unlock a new tier of growth.
Recently named a Top Woman in Grocery, Katie Hotze is a digital marketer, entrepreneur and 20-year veteran of the tech consulting space. She is CEO of Grocery Shopii, a retail technology that allows shoppers to plan and purchase a week of home-cooked meals in less than five minutes from their grocer’s E-Commerce platform.
