Granular merchandising

Most regular customers at major grocery stores have a loyalty card, which is linked to a profile with very basic information (age, gender…). By 1. using this data, 2. using demographics data freely available and 3. using machine learning to draw inferences from the products bought and sold in various areas, it becomes possible to adapt the inventory carried by each store to its surrounding (ie: ramen and EasyMac for the student quarters).

The key question shifts from “where” and “how many” to “who”, “when”, “how often”, “how long” and “for how many cookies?”.

Most grocery stores in large cities have unique layouts with space restrictions, but those can be taken into account. If the store has 40 meters of shelves, and “basics” (tomatoes, beer, ice-cream…) take 30 meters, then the remaining 10 meters can be filled with the “best-sellers” for the categories of people who most visit the store, as well as a few new products. The weight of each can then be adapted and re-adapted as time goes.

It’s also possible to go even further than loyalty cards: computer vision could potentially identify different demographics if given enough training footage, helping inform strategic decisions about inventory. It could also be used to identify what draws peoples’ eyes and what doesn’t, as well as create heat-maps, helping change and adapt store layouts. The cameras are already in most stores and are pretty useless as it is—why not make good use of them for once?

Think of it as the equivalent of 1000 interns per store writing down what people look like and what they’re looking at each day.

Those interns are arguably cheaper in the U.S. because there are no labor laws there, but for civilized countries, computer vision might very well be the way to go. Check out RetailNext for more info.

Cashierless Stores

As mentioned above, we might soon not even need those pesky loyalty cards to be recognized in stores, thanks to computer vision. The technology is already being used across the world, with varying levels of success. The most advanced for now is Amazon Go, which lets you enter its stores via mobile identification (which links your physical purchase to your online behaviour for more of that sweet, sweet data), and leave without taking out your wallet. Items are identified via computer vision and the client’s account is automatically billed through the app. Check out this pretty great non-promotional video to know more.

Yes , this man looks like he has a REALLY annoying voice, but it’s not so bad, promise

The use of mobile is, of course, a fantastic idea: if we throw privacy out the window, the store could track where we went before, where we go after, and link cookies to store behavior. There are many other implications to these types of stores becoming prevalent: theft prevention, customer loyalty, automation… The idea of identifying clients thanks to facial recognition, for example, would be particularly attractive to high fashion stores where high spenders could be identified, and their habits, past purchases and shipping address could appear on a screen for all sales associates to see. This is already the case in luxury stores like Louis Vuitton, but in a much more analogue way.

Check out AiFi, Standard Cognition, Zippin and Trigo Vision for more info on cashierless stores.

Stock Visibility

Yes, this once again will refer to computer vision. Turns out, the real world is not 1s and 0s and needs to be seen to be analyzed. Who knew?

There are a few options to optimize stock visibility, but all of them go through a phase of training an algorithm to recognize ranges of products based on their packaging, and knowing how to count how many there are, how many there aren’t and, more importantly, how many there should be.

The latter is important because the till may know how many products there are in stock and how many were sold, but it doesn’t know if theft or counting errors (ie: suppliers skimming off the top) led to fewer products in store, in reality.

Indeed, cameras could be used to drastically limit theft by identifying when and where they occur. If we can see that an item has been picked up, we can also see that it’s been hidden. We could also potentially check for suspicious behavior based on past data (as shown here by startup StopLift). The ROI is also very easy to calculate (stores know very precisely how much they lose to theft and errors), so this tech is likely to be one of the first implemented.

Stock visibility is also key to optimizing something many retailers are keen to automate: restocking. It’s indeed repetitive and involves products which tend to be of the same shape, which is ideal for a robot going through aisles (the robotics of it all is another story entirely). Employees could then be better used for higher-value tasks. Or so they say: them being fired is probably just as likely.

One of the most original such ideas comes from startup Focal Systems ($2.8M), which aims to equip existing shopping carts with computer tablets that use computer vision to monitor the shelves as the cart moves through the store, and also display digital ads to the shopper (1 stone, 2 birds).

Why use robotics when the customer is the cheapest robot out there?

Natural Language Processing

Speaking of robots, much in the same way that an algorithm can be taught to “see”, it can also be taught to “hear” and “speak.” This goes even further than computer vision, as language is full of subtleties and ultra-local differences that are hard to predict. This hasn’t stopped a few stores from replacing greeters with in-store conversational interfaces, which would suggest products and answer questions for customers.

I believe this is just a gimmick, but it will be added here for the sake of fairness. Whether the excitement wears off once retail robots become “the norm” remains to be determined.

Pepper is French, too, meaning he’s an unbearable ass-hat much like myself

This hasn’t stopped companies from investing in such ideas however. Beyond the example above, a startup called TwentyBN has mixed computer vision to analyze what shoppers are doing with some very basic conversational notions to create a bot that might push customers to spend more. Check it out in the video below.

Conclusion

Much of the above may sound like science fiction. And to most retailers, it is. Though there’s a lot of talk about the blurring between online and offline, most companies first need to concentrate on optimizing physical retail as it is (workforce, supply chain, pricing…). God knows there’s a lot of work.

Beyond building healthy foundations, none of the above is possible without vast amounts of data. This data exists for retailers, but knowing how to gather it, clean it, and use it is far from easy!

Privacy issues are also likely to abound: tracking every movement in a store and compiling data, which could be mixed with online behavior, is not the most ethical thing one can do.

Nevertheless, and despite all hurdles, there are hope that the technology will be implemented far and wide, in one form or another, within the next 10 years. Not because it will increase profit (well, that too), but because, perhaps counter-intuitively, it will make retailing a more human experience.