If there is one pattern that shows up again and again in B2B technology marketing, it’s this:
Companies talk about what their product does, not what the customer achieves.
It’s a well documented, almost perennial issue, particularly in businesses led by technical sales, engineers or product teams. Years are spent building something complex, differentiated, and technically impressive. When it comes time to explain it, the safest thing feels like listing features, specifications, and capabilities.
In practice, this creates the opposite effect. Instead of differentiation, it produces noise. Buyers are left to translate features into outcomes, justify decisions internally, and explain value to non-technical stakeholders who were never part of the original conversation. That gap slows decisions, increases risk, and makes even strong products feel interchangeable.
People don’t buy products or services for their own sake. They buy them to make progress.
That idea has been proven repeatedly across decades of customer research. Customers “hire” solutions to solve problems, reduce friction, or move themselves from an unsatisfactory situation to a better one. Sometimes those problems are obvious. Sometimes they’re felt but hard to articulate. Often, customers aren’t fully aware of what’s holding them back at all.
The most effective marketing has always helped people recognise the problem first, then see a credible way forward. Feature led messaging asks buyers to do that work themselves.
What’s changed isn’t how people buy. It’s how unforgiving discovery has become.
Problems come before products
Customer problems exist at different levels.
There are problems people can clearly name. There are problems they feel but struggle to describe. And there are deeper sources of friction that customers have learned to tolerate, even though those issues quietly cost time, money, or momentum.
Feature led messaging mainly speaks to the first group, and even then it often assumes the buyer already understands what matters. Problem led messaging works earlier and harder. It helps customers recognise themselves in the situation being described and understand why change is worth considering.
This approach has always been more effective. The difference now is that buyers are increasingly encountering brands in environments that reward this way of thinking and filter out the rest.
A catalyst: ChatGPT advertising
This week, OpenAI announced it would begin testing advertising inside ChatGPT for free and lower tier users in the US.
This matters because ChatGPT isn’t a browsing environment. It’s a problem-solving environment.
Ads will appear in response to real questions, at the end of conversations where someone is already trying to understand an issue or make a decision. As Forbes explains in its analysis of the announcement, this represents a shift toward conversational advertising, where relevance and usefulness matter more than volume or repetition.
This doesn’t change buying behaviour. It removes the tolerance for messaging that never really aligned with buying behaviour in the first place.
Why feature-led messaging loses in conversational discovery
In a conversational interface, everything starts with a question.
The user isn’t asking what you sell. They’re asking how to solve a problem, reduce risk, or move forward.
Feature led messaging assumes interest. Conversational systems respond to need.
If your messaging can’t clearly connect a problem to an outcome, it struggles to justify its place in the conversation. Even when it’s technically relevant, it feels disconnected from the moment. This is why feature lists perform so poorly in conversational contexts. They don’t help someone think.
Answer Engine Optimisation and Generative Engine Optimisation don’t introduce new rules so much as formalise what already works. Clear answers to real questions perform better. Content that AI systems can easily interpret, trust and reuse performs better. And content that explains problems, context, and outcomes performs better than content that solely promotes solutions.
This aligns closely with how people actually make decisions. Context comes first, friction matters, and outcomes drive choice. Solutions only make sense once those things are clear.
ChatGPT advertising could sit right at the intersection of this shift. It doesn’t fix unclear positioning, it amplifies it. The same applies to supporting signals such as structured content, FAQs, and schema markup, which can reinforce clarity but only when the underlying message already makes sense.
Value-focused positioning means leading with the antidote
Value led positioning doesn’t mean hiding what you sell. It means getting the order right.
Strong positioning starts with the problem the customer is trying to solve, shows an understanding of the consequences of leaving it unresolved, helps the buyer see why change matters, and only then introduces the product or service as the antidote.
Most businesses already have this insight. It lives in sales conversations and client experience. The challenge is expressing it clearly and consistently.
That is where many brands are about to feel pressure.
Feature-led messaging will not survive this shift
Customers have always bought progress, not products. ChatGPT advertising simply enforces that truth at scale.
In a world where discovery happens through problem led conversations, feature led messaging won’t just underperform. It will disappear.
ChatGPT advertising won’t be a shortcut to better marketing (even if well implemented). If anything, it raises the bar. Poor positioning will not be fixed by appearing in a conversation, and unclear value will not become clearer because it is delivered by an AI system. In many cases, the result will be harsher, not kinder, exposure of weak messaging.
The brands that win will be the ones that truly help customers recognise the problem, understand what’s at stake, and trust the antidote.
Next move
If you’re unsure if your messaging clearly communicates the problem you solve and why you’re the antidote, now is the time to fix it. Contact Cernago today
