The reason a percentile motivates better than a raw score

An energy company tested two report formats for household electricity usage. One showed kilowatt-hours consumed; the other showed the household’s percentile among neighbours. The percentile group reduced consumption by two percent more than the raw-number group. The data was identical; only the frame changed.

What everyone sees

A raw number should be more useful because it is precise and actionable: you know exactly how much you used and can set a specific reduction target. A percentile is vague — “better than sixty percent of neighbours” does not say how many kilowatt-hours to cut. The more informative format should drive more change.

What is actually happening

Schultz, Nolan, Cialdini, Goldstein, and Griskevicius demonstrated the power of descriptive social norms: telling people where they stand relative to neighbours changes behaviour more than providing raw usage data. Festinger’s social-comparison theory explains why: in the absence of objective standards, people evaluate themselves by comparing with similar others. A percentile provides exactly that comparison, while a raw number leaves the person to construct their own standard — which they rarely do.

Why it stays hidden

The motivational gap hides because both formats appear to offer the same information in different packaging. The raw number feels more scientific and therefore more credible, which leads designers to prefer it. But credibility and motivation are different outcomes, and the percentile wins on the dimension that drives behaviour.