The Three Deadliest Mistakes 90% of Brands Make Every Day

They look like three different problems. They are one problem, showing up in three departments.

It dawned on me in the middle of a call last week, answering a question I have been asked a hundred times.

Why do smart companies, with real budgets and real talent, keep producing campaigns nobody reacts to and products nobody switches for?

Three mistakes. They are made every day, in almost every brand organisation I walk into. And they are not independent.

Mistake #1: Treating awareness as if it were relevance

Awareness is the most heavily managed number in marketing. It has a tracker, a budget line, a quarterly review, and a target.

Relevance usually has none of that.

So the machine runs on the number that has a dial. Reach goes up, prompted awareness goes up, the deck looks healthy, and share does not move. Everybody knows the brand. Nobody has a reason to choose it.

Here is the uncomfortable part: awareness is genuinely necessary. You cannot be chosen if you are unknown. That is exactly what makes the mistake so durable. It is not a false metric. It is a true metric doing a job it was never able to do, and it hides the failure underneath it. I have written about that specific trap in awareness is a vanity metric.

Being known is a permission slip. Relevance is the reason.

Mistake #2: Building strategy on a shallow read of what consumers care about

Ask a CMO whether they have customer understanding and they will say yes. There is a segmentation. There is a U&A study. There is a tracker with brand image attributes, rated 1 to 7.

The checkbox is ticked. So nobody looks again.

That is the blind spot I keep running into in top management advisory, and it is the most expensive one in the business, because everything downstream inherits it. Strategy is arithmetic on top of an assumption about why people buy. Get that assumption wrong and you do not get a slightly worse strategy. You get a confidently wrong one, executed with discipline, for three years. That is the argument in shallow customer understanding, and the mechanism behind it is the say-do gap: decisions happen subconsciously, and the explanation is assembled afterwards.

Attribute ratings do not fix this. They tell you what people are willing to say about a brand in a survey context. That is a real thing. It is just not the thing that moved their hand at the shelf.

Deeper does not mean longer. It means a different instrument. Not more questions.

Mistake #3: Engineering technically great products that miss what people subconsciously want

This one hurts the most, because the work is genuinely good.

The product team optimises the specification. Better materials, faster performance, more functions, fewer weaknesses, a cleaner comparison table. Every attribute improves. The launch underdelivers anyway.

Because people do not buy specifications. They buy what the product does to their situation: the status it signals, the effort it removes, the autonomy it grants, the anxiety it settles. Those motives rarely make it into the brief, for a boring reason. Nobody can answer them when asked. A buyer who wants reassurance does not say "I want reassurance." They say the packaging looks professional.

So the brief gets written from the answerable questions, and the product is engineered past the actual motive. That is also why so much pre-launch testing confirms a product that then fails, a pattern I unpacked in why customers can't tell you why they buy.

One root: Proxy Optimization

Look at the three side by side and the same move shows up in each.

Awareness stands in for relevance. Feature specifications stand in for desirability. Stated preference stands in for motivation. In all three cases the organisation manages the stand-in, because the stand-in is countable, budgetable, and improvable on a quarterly cycle. The real driver is none of those things.

Call it Proxy Optimization: managing the measurable stand-in for demand instead of demand itself.

It is not stupidity. It is what happens when the instrument decides the diagnosis. Whatever you can count becomes the thing you manage, and then, quietly, the thing you believe in. Ten years later the awareness curve is beautiful and nobody in the building can tell you why a customer picks you over the alternative.

Marketing has been optimising proxies for a long time. That is roughly why only a small minority of brands actually grow, which is the whole subject of THE TOP 5%.

What replaces the proxy

You cannot fix this by asking better questions. The information was never available to introspection in the first place.

What works is a different sequence. Frame the actual decision space with projective and implicit methods instead of direct questioning, so the motives that neither respondents nor internal experts would have named get on the table. Measure subconscious association with reaction-time-based tests rather than rating scales, because speed exposes what deliberation hides. Then use Causal AI to separate the drivers that cause behaviour from the ones that merely travel alongside it. That three-step approach is what we call Deep Implicit Research.

The output is not a more refined version of the same debate. It is usually a driver nobody in the room had on the list, with a size attached to it.

And once you have that, the three mistakes collapse into one question, answered: what do these people actually care about, and how much is it worth?

Of the three, which one do you see most often in your own organisation? I am reading the replies.

Brand mistakes and Proxy Optimization: frequently asked questions

What is the difference between awareness and relevance?

Awareness is whether a brand is known. Relevance is whether being known changes anything. A category leader can be recognised by almost everyone and still lose share, because recognition without a reason to prefer is simply a name people can recall. Awareness is countable and cheap to buy, which is why it gets managed. Relevance is the harder question: does this brand connect to something the buyer subconsciously cares about at the moment of choice?

What is Proxy Optimization in marketing?

Proxy Optimization is the habit of managing a measurable stand-in for demand rather than demand itself. Awareness stands in for relevance, feature counts stand in for desirability, stated preference stands in for motivation. The proxy improves quarter after quarter because it is easy to count and easy to buy, while the underlying driver of buying behaviour is never measured. It is the common root of the three most frequent brand mistakes. More coined terms are collected in the SUPRA glossary.

Why do technically excellent products still fail?

Because engineering optimises specifications, and specifications are not what people buy. A product team can improve every attribute on the sheet and still miss the implicit motive that makes someone switch: status, ease, autonomy, belonging, reassurance. Buyers cannot reliably name those motives when asked, so they never enter the brief. The result is a product that wins every comparison table and loses in the market. Testing for the motive before launch is a different exercise, see test if a product will sell before launch.

How do you find out what consumers subconsciously care about?

Not by asking directly, because the decision is made before the explanation is constructed. SUPRA uses Deep Implicit Research: framing the real decision space through projective and implicit methods, measuring subconscious association with reaction-time-based tests rather than rating scales, and then using Causal AI to separate what actually drives behaviour from what merely correlates with it.

Dr. Frank Buckler is the founder of SUPRA and a pioneer in Causal AI for marketing. He has applied implicit research methods across FMCG, pharma, financial services, and insurance for over 25 years. The full background is in his book THE TOP 5%.