Bearing Down On Difference

The biggest challenge brands face is standing out from the crowd.

Kantar’s BrandZ tracking of brands across all categories worldwide finds that year-in and year-out roughly eight-in-ten brands fall short on consumer perceptions of difference. Even more brands fall short on difference that is meaningful and relevant.

Difference is rare because difference is hard. The theory of good brand-building is straightforward; in practice, not so much. This challenge will become even harder in the future.

In a previous column I wrote about something I call the "paradox of quality." This is the tendency of quality to attract or to spark even more quality. When one brand introduces something better, competitors don’t sit idly by and cede advantage in the marketplace. Instead, competitors adopt the same improvements. This draws every brand to the same place, and in the process, eliminates the variety of ways in which brands were doing things before, albeit more poorly. Quality as a whole goes up because differences go away.

We’ve seen this with private label brands in grocery retail. In many categories, investments in the quality of private label brands have substantially narrowed the gap with name brands. The risk for name brands is that when people experiment or trade down, they will find that their experience is not diminished enough for them to switch back. This raises the ante for name brands. They must keep investing in the next big thing.

But finding the next big thing is harder and costlier than ever. Low-hanging fruit always goes first, so the next waves of innovation and improvements are inevitably more demanding. Research has shown this repeatedly, including two noteworthy studies published recently. One is an analysis of citation networks which found that disruptive scientific papers and patents have declined dramatically since the end of WW2. The other is an analysis of investment in intellectual property products by public companies, which found that it is 30 times costlier today to match the level of productivity in research and innovation that was true during the 1930s.

What brands need are affordable and better ways to identify differences that matter. Which has been championed by more than a few as one of the breakthrough advances that AI will provide for brands. But it won’t come automatically, if at all.

By and large, AI is a normative mechanism, particularly generative AI. Large language models sweep up all the intelligence inhabiting a given corpus of knowledge and experience. As a result, the best ways of doing things are no longer hidden or undervalued or misunderstood. What might have been barely discernible before is brought plainly into view. And such learning is sure to be put into practice.

The benefit and the risk of AI is that every brand will be privy to best practices. No brand will be able to forego what it learns about the best ways of doing things, if for no other reason than competitive threats and investor pressures. Hence, every brand will be higher quality than before, but every brand will also be channeled to the same place. Which means that the evolving mastery of AI across commercial enterprises will push brands toward consistency with a norm. This is the normative impact of AI—every brand of higher quality yet more alike than before.

This is great for operational processes. Efficiencies like these yield bottom-line benefits of no consequence to a brand’s perceptions among consumers. But when brands incorporate the same learnings about persuasion and promotion and pricing and packaging and all the other P’s of engagement with consumers, difference will be an even bigger challenge than now. With potential bottom line impact that could offset the savings realized through operational efficiencies.

This is why brands and agencies have become protective of their internal data and expertise. They don’t want AI to reveal what they know. Of course, the reality is that internal corpuses of knowledge and experience aren’t equally valuable. Some are sure to yield better learning than others. So, increasingly, we will see brands and agencies deploy AI for corporate reconnaissance to ferret out or to get good approximations of competitive insights. It will be an arms race of AI at another level of intensity, but still AI for normative purposes.

There is nothing new about the normative challenge facing brands in a future of AI. Every time something new comes along, every brand adopts it or does it, which raises the table stakes. Which is the paradox of quality.

We must keep AI in perspective. AI is not some fairy dust we can sprinkle on brands that makes difference easier to discover, deliver and sustain. AI is like every major advance in marketing, which in my career has included things like split-cable markets, A/B testing, conjoint analysis, grocery scanner data, marketing mix models, social listening, Big Data and digital advertising of many sorts. All were touted as the long-awaited big breakthrough in marketing. And brands have benefitted, but all brands not just a few.

So, too, will be the impact of AI. It is going to benefit brands immensely, but all brands not just a few. The challenge of standing out from the crowd is sure to be just as hard as ever.

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