By: David Phillips, president and COO, NLogic

Remember: Hilary Clinton was supposed to win. All the data supposedly said so, from The New York Times to Nate Silver’s FiveThirtyEight.com, which calculated the probability of Clinton winning the popular vote but losing the electoral college at just 10.5%.

There were a few lone voices calling in the wilderness, pointing in the other direction towards a reality TV host with an orange face and large hair. They too were basing their predictions on data, but coming to the opposite conclusion. Allan J. Lichtman, a political historian at American University in Washington said just two days before the election that Trump would win.

The expert consensus proved to be founded on faulty assumptions and faulty analysis. Even with what looked like the most accurate, up-to-date and comprehensive data available to them, the actual outcome came as a nasty surprise.

That election showed what happens when we look at the wrong type of data – specifically, the wrong-term data. There were in fact at least two distinct terms of data being used to predict voter behaviour: short-term (e.g. polls) and long-term (e.g. historical election trends).

In the marketing world, the same categories of short- and long-term data also exist. Short-term data is typically good at seeing what’s going on right now: what’s trending, what’s being purchased, how people currently feel. In the world of marketing and media, a lot of the digitally sourced data is short-term, providing a seemingly clear window into what’s being seen, heard or clicked on. Long-term data on the other hand typically looks wider and further: how people have behaved over time, how their feelings towards brands evolve, who they’ve actually voted for.

All too often, we fall into the same wrong-term data trap as those who called it for Clinton when looking at our brands and businesses. In seeking to embrace all the exciting-looking, short-term data available to us, we fail to ask the critical question of whether it’s the right kind of data. And to answer that, there is one criterion that is more important than anything else: is this data fit for purpose? Is it best suited for doing the job I need it to do? If I need to assess long-term behavioural trends (like voting preferences or brand sentiment), then I should be looking at long-term data, however tantalizing the short-term data may seem. Anything else is wrong-term data.

When it comes to marketing, all brands share the same common purpose: sustained increase of sales. This is inherently a multi-term purpose: short term (sales) and also long term (sustained increase). The analysis and data that feeds it should therefore also be multi-term. Any assessment of ROI needs to embrace a multi-term view: the return cannot only be seen in the short-term.

What smart marketers are doing is ensuring that the data they are looking at is aligned to the purpose they have in mind. Short-term data serves short-term purposes well. But for all those who are concerned with anything past the short-term, long-term data is essential. Because applying short-term data to long-term purposes is using wrong-term data, and that can lead to nasty surprises.

And I think we can all agree we’ve had quite enough of those recently.