Not all big data is created equal, warns marketing scientist

Marketing scientist from Ehrenberg-Bass Institute warns marketers to better understand where data is coming from in the new frontier of analytics

The new frontier of data is full of possibilities, but marketers need to be aware that not all data is created equal, a leading marketing scientist warns.

Associate professor and director of the not-for-profit Ehrenberg-Bass Institute, Dr Rachel Kennedy, told attendees at the Data Strategy Symposium in the Hunter Valley that the scale and range of data sets available to marketers to understand customer behaviour is “awesome”, but that it can’t simply be taken as face value.

The institute, which is based out of the University of South Australia, researches marketing as a discipline as well as brand and buyer behaviour. Kennedy’s recent examples of work included studies on the accuracy and value of biometrics, neuroscience and virtual shopping.

“People used to think the world was flat until they saw enough data points to know that actually it is round. We’re at that point in marketing where there is a completely new way of thinking about it,” she said.

“We have an enormous capacity to store information that we didn’t in the past, and new ways of searching it that didn’t exist before. It is an exciting world, but it’s very easy to drown in this data. You can have years stored, but still not get the knowledge out of that data. I see a lot of that around the world – people are looking at the magician’s hands and not seeing the big picture because they don’t know what to look for.

“Even the best data doesn’t lead to the right decisions if you don’t know how to look at it.”

Just because it’s big data, it doesn’t mean it’s not biased information or makes you focus on things that aren’t as important to look at, Kennedy said.

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As an example, she pointed out that ‘likes’ on a Facebook site can offer a skewed sample of consumers to brands because they consist of individuals that are largely heavy users of a brand. The way to grow your market is in fact to target those light to moderate buyers or users who are not present in that group.

“All brands have most of their customer group as light buyers, another group that are moderate, and the smallest percentage are heavy buyers,” she explained. “The route to growth is not the heavy users, because you can’t lift them that much higher and there are not very many of them. You want to budge light and moderate buyers, and that will lead you to do different things.”

In addition, new studies using virtual shopping experiences to try and determine how consumers behave in a physical supermarket environment don’t necessarily reflect how individual shops in real life.

Kennedy pointed out global market research shows the most common number of items purchased by consumers going into supermarkets worldwide is one, meaning many don’t even use a basket or trolley.

Yet virtual reality experiences give consumers the time and opportunity to browse with a trolley, distorting sample information, she said. “This means you’re missing the most popular choices in-store,” she claimed.

“You need to understand the fundamental patterns in how people buy, and have prior knowledge in the principles of how advertising works, otherwise you’ll see things in your data that are just not there.”

According to Kennedy, key ways to determine whether data sources are trustworthy or not are whether it is repeatable, transparent, and relates or is predictive of in-market behaviours within your area or industry of interest in a way that can be proved.

“If I have two different teams measuring things at the same time, I’d want them to give me the right answer. Seems obvious, but in the this new frontier space it’s not necessarily the case,” she claimed.

“You get much bigger wins by looking at what was the same last year and previous year as this year. What’s the same for big and small brands, and what is the same across data sets in different countries? Where you find sameness, that’s the knowledge that’s useful for the predictions you’re going to make for the future.”

And just because new data sets are exciting, it doesn’t mean it’s time to throw out the old methods. For example, A/B testing is still critical, even though we’re working with much better data, Kennedy said.

“We need to get to the point where we know what data we need, old or new, to make the right decision,” she added.

- CMO was the media partner for the inaugural Data Strategy Symposium, organised by Ashton Media, in the Hunter Valley on 25-27 November.

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Comments

Tim Nicholas

1

The example of "likes’ on a Facebook" totally misses the point of Likes. They are intended to spread the brand message to the network/friends of the liker. Not count the popularity of the brand. Who is this supposed 'expert'?

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