When I was growing up, one of my favorite television game shows was Name That Tune. Contestants challenged each other by claiming they could identify a song after hearing only a few notes. The game rewarded people who recognized patterns faster than everyone else. I never imagined I’d end up playing a version of that game decades later in the grocery industry.
After spending more than 15 years in category management, I started processing digital grocery orders at the beginning of 2026. Before long, I turned it into a personal challenge. After seeing only the first handful of items, I’d try to guess who the shopper was before scrolling through the rest of the basket. Was it a man or a woman? Were there children in the household? Was this a single shopper, a couple or a family? The more baskets I reviewed, the better I became at recognizing shopping patterns, and it sharpened the way I think about category management.
One order from The Fresh Market still stands out. The basket totaled about $150 but contained relatively few items: organic produce, tofu, lamb, salmon, Vital Farms eggs, David protein bars and oat milk. Before I reached the bottom of the order, I guessed it was likely a woman in her mid-to-late 20s who placed a premium on nutrition and was probably involved in athletics or fitness. Later, I spoke with the customer and learned she was an up-and-coming female race car driver. I wasn’t right because I knew who she was. I was right because her basket reflected her lifestyle. Experiences like that became more common over time. The more baskets I reviewed, the less I found myself guessing products and the more I found myself recognizing routines and lifestyles.
That experience completely changed my perspective. For much of my career, Circana and Nielsen shaped how I evaluated retail performance. Those datasets remain incredibly valuable because they explain what is happening across categories, brands and retailers. Digital grocery baskets taught me something equally important.
Circana and Nielsen showed me what sold. Digital baskets showed me why it sold.
Once I stopped looking at individual categories, I started seeing complete shopping missions. I wasn’t looking at cereal, yogurt and frozen pizza anymore. I was watching parents prepare for school mornings, families stock up before weekend baseball tournaments, busy professionals solve weeknight dinners and college students furnish a first apartment. Every basket had a purpose, and understanding that purpose changed how I viewed merchandising.
The patterns became remarkably consistent. Orders loaded with fresh produce and scratch-cooking ingredients were, in my experience, far more likely to be placed by female shoppers. Baskets dominated by frozen entrées, frozen pizza and convenience meals often pointed toward a single shopper and, more often than not, a male customer. Hamburger buns almost always appeared with ground beef, while rotisserie chicken frequently shared the basket with salad kits and bakery rolls. During back-to-school season, Sunday afternoon orders consistently included sandwich bread, lunch meat, snack packs and juice boxes. They weren’t random purchases. They reflected routines, and the basket told that story better than any sales report ever could.
That realization also changed how I think about grocery loyalty programs. Most retailers still judge success by enrollments, app downloads and coupon redemption rates. Those metrics matter, but they represent only a fraction of a loyalty program’s value. The greatest asset isn’t the discount. It’s the customer intelligence created every time a shopper fills a basket.
Every purchase provides context. A family buying diapers today may soon begin purchasing toddler snacks and children’s medicine. A shopper replacing traditional dairy with lactose-free products may be responding to a dietary need. Someone suddenly filling the basket with protein bars, electrolyte drinks and fresh fruit may be training for a race, coaching youth sports or simply making healthier choices. Those purchases aren’t random. They’re clues that explain not only what customers bought, but why.
That’s where many retailers still miss the opportunity. When cereal sales soften, another cereal coupon appears. When coffee sales slow, coffee goes on promotion. Loyalty data allows merchants to ask a better question. Instead of asking how to sell more cereal, they can ask how to better support a shopper trying to put breakfast on the table before school. Instead of promoting taco seasoning alone, they can merchandise an entire taco-night solution.
Retailers already possess an extraordinary amount of first-party data. The challenge isn’t collecting more information. It’s learning to interpret the stories hidden inside the baskets they already have and using those insights to make smarter merchandising, assortment and promotional decisions.
If your loyalty program disappeared tomorrow, what knowledge about your customers would disappear with it? If the answer is little more than coupon redemption rates and purchase history, you’re operating a rewards program. If the answer is that you’ve learned how families shop, how routines evolve and how needs change over time, you’ve built something far more valuable.
Looking back, I don’t think processing digital grocery orders simply made me better at reviewing baskets. It made me a better category manager. I still rely on Circana and Nielsen every day, but now I read them through a different lens because I’ve seen what happens inside individual baskets.
Categories tell us what sold. Baskets tell us why.