Local grocery stores (chains) within close proximity now carry different inventories to suit their neighborhoods; limiting choices and driving up profits while better meeting consumer needs and demand.

We rely on algorithms to perform hundreds of millions of tasks: millions of instant stock trades per second; guiding an average 100,000 flights, bringing passengers safely to the ground; finding ideal mates using dating websites (a two-billion-dollar industry); and predicting terrorist activities around the world using pre-programmed feeds from global networks, i.e. connecting the dots.

The computer codes known as algorithms are used to perform tasks that stretch beyond human capabilities. Take this real world example–within nano seconds of a keyboard query, a programmed algorithm perhaps in tandem with other algorithms, is capable of searching across the world’s accessible data, billions of bytes on multiple servers thousands of miles apart on various networks using a variety of software platforms, and then ranking and sorting all the accumulated data into meaningful results based on the user’s query. And even with all these machinations, results can appear in seconds.

Grocery store chains have been accumulating reams of check-out data, and other information, for years, taken all together they constitute Big Data. For the longest time I wondered what the stores intended with all the effort and incentives to grab my data. In exchange for sharing my buying behavior, Giant Food offers me tens of cents off per gallon of gas, which the chain must reimburse to someone, given the narrow profit margins on gas here (factor in real estate costs, taxes, and competition in our local gas prices.)

Now we have a pretty good idea of how the data is used. We see the differences in inventory and promotions among the surrounding grocery stores within a three-mile radius in suburbia, three of them Giant Food and two Safeways.

Among the three Giant Foods–one is situated in a predominantly Hispanic neighborhood, the other, in a neighborhood one could categorized as Middle-to-Upper Middle Class based on the housing, and the third store is in an affluent area–made up of roughly half who are generational families-second and third generation Americans-and a half who are successful newly immigrated Russians, Indians (from the subcontinent) and South Koreans. The area served includes a range of real estate from higher-end-priced single family houses all the way up to mansions the size of palaces and horse-breeding farms.

By processing the data in algorithms, the stores are able to better accommodate the location’s primary shopping demographic. And by more carefully selecting inventory, products move off the shelves faster, labor costs are reduced by limiting brands and choices (now just three-to-five brands of cereal instead of, say, 50), and shelf space becomes less cluttered creating opportunities for charging a premium for shelf space to those CPGs (Consumer Product Goods) who want the advantage of narrow targeting,

Stores that have distinct segmentation, as do ours, offer up promotional opportunities and items that consumers desire during times of year, for instance religious days. For Passover, observed by the Jewish faith, matzoh and seder-restricted foods are stocked for a limited time, mostly at non-discounted prices due to demand.

To wrap up – the shift in grocery store inventory cropped up recently when our household discovered we could no longer rely on one store or another within the Giant Food chain to snag favorite staple products, including soda water and ordinary cereal brands. This became an irritant when we knew all the chain stores are supplied from the same warehouse. So we changed our buying habits to choose among the five stores based on what we intend to make for dinner.

Steven J. Slater is the Author of Be Relevant: How Brands Rise to the Top (A Practical Guide to Service Design) Available on Amazon – https://amzn.to/2HbcToJ

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