In many organisations, the “average customer” still sits at the centre of decision-making. Average satisfaction scores, average likelihood to purchase, average usage levels are easy to report, easy to track and easy to agree on – even though they can give a misleading picture of how people actually behave.

From a market research perspective, the average customer is a statistical shortcut rather than a real person. Very few people behave like the mean of a dataset, yet strategies are often built as if they do. When that happens, decisions can look sensible on paper but fail to deliver in practice.

The issue is not that averages are inherently wrong. Used carefully, they can be helpful indicators. Problems arise when they are treated as representative of the market as a whole. In most categories, behaviour is uneven: different customers value different things, make different trade-offs and respond in very different ways to the same offer.

Averaging across those differences smooths away variation, which is precisely where both opportunity and risk tend to sit.

This tends to show up in a range of ways. A product test might return a respectable overall score, masking the fact that a smaller group finds it genuinely compelling while many others feel largely indifferent. A brand tracker may appear stable, even though one audience is disengaging while another is becoming more positive. A proposition may test well in theory but struggle to convert because it does not strongly resonate with any particular group.

In each of these cases, the headline result tells a reassuring story, while the underlying detail offers far more commercial value.

Over-reliance on averages can also push teams towards safer decisions. When insight is reduced to a single number, strategies often aim for broad acceptability rather than clear appeal. The result is often a series of small, safe changes that are easy to justify internally, but rarely move the needle in any meaningful way.

From a commercial point of view, this is a missed opportunity. Growth rarely comes from pleasing everyone a little. It comes from understanding which customers matter most, what motivates them, and how their needs differ from the rest of the market.

This is where segmentation becomes important. By examining how attitudes and behaviours group together, segmentation replaces the idea of an “average” customer with a more realistic picture of who is actually there. It helps teams see where demand is strongest, where resistance is likely, and where effort is best focused.

It also changes how results are interpreted. A modest overall score can look very different when viewed by segment. What appears weak on average may be highly relevant to a strategically important audience. Equally, a strong headline score may be driven by people who are unlikely to convert or deliver long-term value.

Recognising this difference is critical for effective planning and forecasting. When teams understand the spread of responses, rather than focusing only on the midpoint, they are better placed to anticipate uptake, identify sources of friction and avoid overestimating demand, leading to more realistic plans and more effective use of resources.

The aim is really to make sure the insight being used reflects how markets actually behave. Often, that comes down to a small shift in focus; moving away from what the average customer thinks, and instead towards understanding who thinks differently, and why.

Ultimately, the risk of relying on the “average customer” is not just analytical, but commercial. It leads to decisions that feel safe, but rarely deliver step-change impact. Organisations that understand variations in behaviour and motivation make clearer choices about where to focus, and are better placed to turn insight into growth.

Interested in learning more about segmentation? Get in touch to arrange a free consultation with us to discuss your business.