Like most hardcore people, in the car I roll with my windows down and my radio cranked up to 11—tuned to 91.5, my local NPR station, where Terry Gross recently interviewed Joseph Turow, author of “The Aisles Have Eyes.” Turow reports that retailers are using data gathered from apps on your phone and other information to change prices on the fly.
Having worked in this field for a while, I can tell you that, yes, they’re gathering any data they can get. But the kind of direct manipulation Turow claims, where the price changes on the shelf before your eyes, isn’t yet happening on a wide scale. (Full disclosure: I’m employed by , which offers personalized/targeted search and machine-learning-assisted search as features in products we sell.)
Why not? I can think of a number of reasons.
1. Technology changes behavior slowly
Printers used to be a big deal. There were font and typesetting wars (TrueType, PostScript, and so on), and people printed out pages simply to read comfortably. After all, screen resolutions were low and interfaces were clunky; scanners were cumbersome and email was unreliable. Yet even after these obstacles were overcome, the old ways stuck around. There are still paper books (I ), and the government still makes me print things and even get them notarized sometimes.
charged to suppliers who want preferential product placement in the aisles.
Meanwhile, Target makes money no matter what soap I buy there. Unless incentivized, it’s not going to care which brand I choose. Effective targeting may require external data (like my past credit card purchases at other retailers) and getting that data may be expensive. The marketplace for data beyond credit card purchases is still relatively immature and fragmented.
5. Personalization is difficult at scale
For effective personalization, you must collect or buy data on everything I do everywhere and store it. You need to run algorithms against that data to model my behavior. You need to identify different means of influencing me. Some of this is best done for a large group (as in the case of product placement), but doing it for individuals requires lots of experimentation and tuning—and it needs to be done fast.
Plus, it needs to be done right. If you bug me too much, I’m totally disabling or uninstalling your app (or other means of contacting me). You need to make our relationship bidirecitonal. See yourself as my concierge, someone who finds me what I need and anticipates those needs rather than someone trying to sell me something. That gets you better data and stops you from getting on my nerves. (For the last time, Amazon, I’ve already purchased an Instant Pot, and it will be years before I buy another pressure cooker. Stop following me around the internet with that trash!)
6. Machine learning needs to mature
Machine learning is merely math; much of it isn’t even new. But applying it to large amounts of behavioral data—where you have to decide which algorithm to use, which optimizations to apply to that algorithm, and which behavioral data you need in order to apply it—is pretty new. Most retailers are used to buying out-of-the-box solutions. Beyond (ahem) search, some of these barely exist yet, so you’re stuck rolling your own. Hiring the right expertise is expensive and fraught with error.
To influence a specific, individual consumer who walks into a physical store, the cost is high and the effectiveness is low. That’s why most brick-and-mortar businesses tend to use advanced data—such as how much time people spend in which part of the store and what products influenced that decision—at a more statistical level to make systemic changes and affect ad and product placement.
Online retailers have a greater opportunity to influence people at a personal level, but most of that opportunity is in ad placement, feature improvements, and (ahem) search optimization. As for physical stores, eventually, you may well see a price drop before your eyes as some massive cloud determines the tipping point for you to buy on impulse. But don’t expect it to happen anytime soon.