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If you’re like most companies, you’re using some form of traditional, survey-based market research or brand tracking in order to inform both your marketing and broader business strategies. But what if we told you that surveys alone aren’t all that accurate for some consumer demographics? And that those are the demographics that tend to be most sought-after?
Over the last few years, several members of our analytics team have been focused on predictive analytics. How do we help our clients better predict business results and market trends, so they have the information they need to make sound business decisions?
What we’ve found is that social and digital data provide meaningful signals into market trends that surveys sometimes miss. When we add digital and social data to more traditional models, our forecasts are generally 25-40% better, and we’re able to predict business outcomes for our customers with at or above 90% accuracy. Even more interesting, digital and social data is often more predictive of behavior than self-reported interest and intent for younger audiences, including Millennials.
Why are social and digital metrics so powerful?
There are a couple reasons this could be the case. First, there could be a spontaneity effect at play. We were dealing with a relatively low-consideration purchase, and younger people tend to have more flexibility with both their time and their finances when compared to older adults, who tend to have more commitments like young children.
The second effect we could be seeing is a social desirability one. We know that younger people are more susceptible to saying they’ll do things they don’t actually plan to do in order to be seen as ‘cool’ or otherwise on-trend. For instance, research has shown that millennials say they prefer chocolate brands with ethical sourcing in focus groups, but actually chose brands based on high-fat content and a relatively small number of pronounceable ingredients*.
To determine which of these was responsible for our results, we decided to take spontaneity out of the equation by looking at a high-consideration purchase: automobiles.
What we found is that for high-priced vehicles, we saw the same general trend as we did for our low-consideration purchase: self-reported intent was a better predictor than digital and social metrics for older audiences, whereas digital and social data better predicted purchases for younger groups. However for low-priced vehicles, we saw the opposite trend. Younger audiences’ tendency to report they were going to purchase a low-priced vehicles was a better predictor of their behavior, but for older audiences, digital and social metrics were a much better indicator.
What does this mean? The >35 crowd is find with saying they’re going to buy a BMW, but if they’re planning to buy a Kia, they’re less likely to tell you about it. This suggests not only that social desirability affects the predictive value of a survey, but also that any demographic group can fall victim to the bias.
What kinds of social and digital data matter?
While most marketers look at conversational metrics to calculate share of voice or get at voice of the consumer, it’s actually behavioral metrics that add the most predictive value. It’s the relatively anonymous nature of things like YouTube video views, website visits, and search that provides us a glimpse into what people are really interested in, especially when that thing is not particularly “cool.”
Social desirability still plays a role when you’re posting on Facebook to a group of 1,700 of your closest friends, but not when you’re one of the nearly 15 million people watching “A Goldmine of Blackhead & Whitehead Extractions” on YouTube at 1 am.
Insights in an echo chamber
Companies who rely solely on survey data run the risk of making critical decisions in an echo chamber. Consider a brand developing a new product. They put a survey in the field to understand which consumer segments may be interested in a premium product like theirs, and find that it’s more established consumers—those over 45—who show the highest definite interest and intent to purchase. They rely on this insight as they develop their creative and select their marketing placement, heavily investing in primetime television and less on digital channels.
The problem with this scenario is that the survey didn’t capture the whole picture. There is a group of millennials—affluent, urban, 25-34—who are interested in the product, but don’t say so because it’s not particularly environmentally-friendly. The result is a self-fulfilling prophecy: the younger audience didn’t say they were interested, so the brand didn’t effectively market to them, and, as a result, that high-value audience didn’t buy the product.
The brand looks at their purchase data, relieved that their insight was ‘proven,’ but they actually missed out on a large pool of potential revenue as well as an audience that could carry their business into the future.
Market research moving forward
We no longer live in a world where survey research is the best predictor of consumer behavior. Given our ability to overcome the limitations of traditional market research by bringing in new metrics, it’s irresponsible for brands not to leverage social and digital data in brand tracking and forecasting.
We’re so excited that our work was presented at Esomar’s 2016 Congress, because it shows that the broader Market Research community is coming around to the idea of fusing traditional metrics with digital and social signals, as well.
View this video for more information: https://www.youtube.com/watch?v=rVn8p4nHmIo
By: Kelley Sternhagen, Analytics Director at W2O Group &Seth Duncan, Chief Analytics Officer at W2O Group
