Algorithms’ impact on the media marketplace is “more prevalent than people realize, although not nearly as prevalent as it will be soon,” according to J. Walker Smith, Kantar’s chief knowledge officer.
And he should know. He is in the business of monitoring “the shifting dynamics of the marketplace context within which consumers live and shop.”
Charlene Weisler: How prevalent is the impact of algorithms and AI on the marketplace?
J. Walker Smith: It is important to distinguish algorithms and AI. Algorithms are structured decision rules that, nowadays, are executed through software used to run automated marketing programs or consumer apps.
AI consists of learning systems that modify and (hopefully) improve algorithms or decision rules over time.
AI is still being tested and assessed, although we hear a lot about many of its big early successes. Algorithms are the beating heart of programmatic marketing and real-time targeting, and they are the way in which all kinds of apps work...
Looking ahead, predictive software systems will get better at profiling consumers (within regulatory restrictions about data) and matching inventory to individual profiles. Personalization or customization is a process driven by algorithms.
Weisler: What are the positive and negative aspects of this?
Smith: An enormous amount of time and money is wasted on bad matches. Nobody wants to spend money on a product that isn't a good fit or waste time learning something that is a bad fit. Nobody wants to get entangled in a bad relationship that could have been avoided with an algorithm.
Economists care about this because they study efficiencies, and bad matches are a drag on the economy. It's hard to measure, but economists can point to things like people finding jobs faster and being better matched as direct improvements to macroeconomic welfare.
The negative aspects are twofold. First, people like to choose and to be involved in deciding. An algorithm that does everything for you takes away the pleasure of discovery. However, this can be solved with future software that builds in discovery….
The second thing to protect against is data security. Algorithms require data about consumers, so considerations of data privacy are front and center. Activists and regulators are more worried about this than the typical consumer, though. I believe we will eventually reach an equilibrium point, and then from there, consumers will still want algorithms that can predict or aid in decision-making.
Weisler: How fast will this trend grow?
Smith: It's growing fast, and is very prevalent already. There are no good metrics to track algorithms per se, but the take-off rate of the Internet of Things is the best proxy, because algorithms will be embedded in all of these devices.
They are in every consumer app, too, and that has become the new interface for content and products.
Weisler: Can algorithms be “gamed" — and if so, how?
Smith: Everything can be gamed. You game algorithms by figuring out the inputs and the relative value an equation places on each input. This is how people have tried to game Google, and it's why Google keeps upgrading its algorithm.
Indeed, gaming algorithms will become a large part of tomorrow's marketing landscape — not to do anything inappropriate or illegal, but to increase exposure to consumers and to boost the likelihood of consumers choosing one brand over another.
If this sounds familiar, it's because this is what marketers have always tried to do — not necessarily by gaming something, but just by putting more effort and money into the things that are known to work best in getting consumers to choose one brand over another.
Weisler: Algorithms can be wrong — what can a consumer do in that case?
Smith: In fact, algorithms are wrong a lot. Marketers play the odds, looking to increase the likelihood of consumers choosing their brand—and they want to do so affordably. Structured decision rules to accomplish this have always been embedded in media buying equations and marketing rules-of-thumb. Algorithms are just the 21st century version of that.
So being wrong per se is not bad for marketers, as long as they are being wrong less frequently by using an algorithm….
Consumers also know that many decisions will be less than optimal, so a good algorithm is one that minimizes, not eliminates, bad choices. Algorithmic mistakes are not life-threatening, at least not yet, so for consumers, wrong outcomes are just an inconvenience.
Weisler: How do algorithms evolve?
Smith: Algorithms evolve through experience that improves the models embedded in the algorithmic system. This can be done periodically over time or it can be done in real time.
Validations are run to assess predictions and then once enough data has been accumulated, updates are made.
The latter is the AI future that is getting so much press. By the way, the next frontier of evolution is voice assistants. Algorithms will be a big part of voice-based systems, but this is just in its initial stages right now.
Weisler: Doesn’t this type of marketing lead to "fishing in the same pool" for consumers? What about potential customers who may not know about your product or service?
Smith: There is nothing new about this with algorithms. In fact, algorithms are about standing out in this crowded pool, and so are a new source of competitive advantage and a new kind of barrier to entry. Algorithms are not simply about marketing to past consumers or modeling past behavior. Going after existing customers is profitable when done well, but marketers are aware that they have to grow their franchise and the category itself.