Conclusive Proof That Social Media Data Predict Sales…Now What?

Social media data are quantitative and predictive. We must create research protocols to harness their full transformative power.

by Joel Rubinson
President at Rubinson Partners Inc

Editor’s Note: We’ve previously posted a few articles by Michael Wolfe on the progress in utilizing social media data to predict sales, and now thanks to an ambitious multi-sponsor study it appears that we have independent corroboration that when using the right tools and the right data that social data does indeed have a high correlation to predictive accuracy...

Last Tuesday, the results of a landmark study were made public proving that the quantity of social media conversations about a brand has a statistically significant relationship to changes in its sales.

“Researchers today announced the results of a landmark study that measured the impact of “consumer word of mouth” in six diverse categories, finding that online and offline consumer conversations and recommendations account for 13% of consumer sales, on average…About one-third of the sales impact is attributable to word of mouth acting as an 'amplifier' to paid media, such as television, with consumers spreading advertised messages...The study was based on sophisticated econometric modeling of sales and marketing data.” –Word of Mouth Marketing Association (WOMMA)

This industry learning comes on top of an academic paper by Prof. Wendy Moe at the University of Maryland that showed a correlation of .8 between social media listening data and brand equity metrics derived from survey questions.

So now that we know that social media data are truly DATA…with predictive value, how do we act on this?

First, research needs to take social media listening seriously

As I said in an earlier post, “...finding the prediction question,” research needs to become an equal opportunity employer. If the data has predictive value, it should be hired! Traditional survey researchers need to come to grips with the proven predictive value of social media data. We need to stop treating social media listening as a hobby and find its mainstream roles alongside surveys and other important data streams such as clickstream and transaction data.

Second, we should create new brand metrics from social media data.

In my last post about the marketing ATOM, I demonstrated how building brand audiences is the key to success in a digital age. People become part of an audience for a brand that is significant and relevant to them and audiences talk about the brands they join. Hence, it is no surprise to me that social media would provide an important set of brand metrics.

Third, we extend.

I plan to investigate if social media listening can replace continuous tracking of attributes. I am optimistic that we can do dipstick studies with attributes but track brand perceptions throughout the year via social data, creating a leaner, more agile, and more effective tracker program.

I would like to see us begin to partition social media conversation by client segment or audience. To illustrate, it is now possible to match social media profiles to customer lists using machine-based logic that matches on the name, e-mail, etc. As such, for example, Verizon could create a segment of customers called “On the bubble” who are more likely to defect and, as an aggregated segment, their social media conversation could be monitored. What are they saying about Verizon, competitors, life, TV programs, etc.? Where are the conversations occurring? This would be very powerful and the technology, in fact, does exist.

I urge research panel providers and brand websites to encourage social log-in so the power of Facebook and Twitter profiles can be harnessed. In this way, interest profiling and ad targeting merge into one thing.

Yes, the genie is out of the bottle but as you head into this world of integrative measurement, please be mindful of rigorous practice for social media listening. Different providers can actually produce very different data streams for the same brand, depending on whether they access the full Twitter firehose, including all social channels, how their semantic engine works, etc...

This is a significant stage in the journey the ARF started in 2008 when I was Chief Research Officer. We began to explore how social media listening could become a valuable partner or even partial replacement for surveys. Now that we know that social media data are quantitative and predictive, we must create research protocols to harness their full transformative power.