Dabish Digital
Analytics

Five mistakes teams make with heatmaps

Every audit we run turns up some version of this. These are the ones we run into repeatedly when we audit heatmaps.

Data you do not trust is worse than no data, because it gets quoted anyway. It is the sort of thing that looks like polish right up until it costs you an enquiry.

Warning signs

  • Treating it as a launch task rather than an ongoing one
  • Assuming someone else already owns it
  • They show where attention goes, not why
  • Best used to generate hypotheses
  • Never checking whether the fix actually worked

Combine with recordings for context. That sounds obvious written down. It is still the thing most often skipped. Write the reasoning down alongside the decision, because the reasoning is what changes first.

Where to go from here

How to tell if yours is fine

More dashboards rarely produce more decisions. Three things worth confirming about heatmaps before you move on:

  • Someone can say what the current setup is without going to look
  • Best used to generate hypotheses — and you know whether that is true here
  • There is a way to tell whether the last change to this helped

None of this needs a rewrite. Most of it is a morning's work once someone decides to do it.