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Inherent Optimism

Free Stuff by sparkrobot

Free Stuff by sparkrobot

When trying to predicting future behaviour (like purchase intent, or service uptake) it can be a very tricky exercise. You are asking people what they believe will happen in the future. And most people seem to put an accent on the positive.

Science Daily reports on a study that shows that people always put a rosy tint on their predicitons of outcomes that will happen tomorrow, and found “that people were consistently overly optimistic when asked to predict their own future behavior”.

I know myself – when estimating time it takes to complete a project phase when I am working out costings, I think – well, with no interruptions, hiccups or disasters, this  would take about 4 hours.  But in a real day – when the hiccups and disasters are a matter of course it will take 8 hours – at least.

This is really important to think about when using consumer data to predict an outcome. If the news seems too good, then we have to accept that maybe it is too good.  In an ideal world we may steal 50% market share in the first month, but this doesn’t take into account a whole bunch of stuff like product awareness, poor distribution, or intense competitor activity beyond what was assumed in the study.

So what to do?

  • be realisticif a result comes back that people prefer your product than the market leader, do not assume you will be in their position in 12 months time. Plan for a year or more of getting your message out there, cracking distribution, stealing share, but don’t expect to rule the world on launch. 
  • don’t oversell it, be truthful – Building in realistic expectations will also help your case internally. If you sell in a product in that it will take X% share in X months, and it doesn’t, chances are there will be calls for it to be deleted. You may actually have a pretty cool product or serve that needs more nurturing, so don’t build such high expectations that you may never be able to deliver to
  • use the right measurement tools – increased accuracy in results means using more sophisticated tools (and unfortunately increased cost). Using a choice modelling approach will increase your chances of getting a better picture of what may happen as you an include potential competitor activity in your market simulations. Asking straight up purchase intent will give you that overly optimistic response –  “Will I buy it? I’ll buy 10!”. And no – four focus groups will not give you the answer on the success of a launch.  If you use the wrong tools you have no right to “blame” research…especially the focus group!
  • build in realistic distribution and awareness estimates – I’ve seen some rocking concepts fail at launch as the marketing guys haven’t involved the sales guys enough, so while people may have heard about the product, no one can find in anywhere to actually buy it. Or a great product being marketed on a nearly non-existent budget so no one actually has heard about it. So, if you know either of these are likely to be low (no or low sales or marketing support post launch) make sure you build this into your predictions.  It will also sound like an excuse at the end of the day if you say you haven;t reached your target because you didn’t have  enough support – this should have been known beforehand.  

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