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

In a meeting that included Unilever, Procter, and General Mills - then a highly unlikely event, we all agreed that social media listening had tremendous potential for insights value creation. This became the big springboard into the ARF Research transformation super-council.

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. On a personal level, I have seen this first hand via my work with Decooda, which has also demonstrated this consistently in non-public research with several major CPG clients, as well as a deeper level of insight available through combining other frameworks such as the Censydiam model by Ipsos.

Joel Rubinson is THE thought leader in market research on developing a new model for utilizing a variety of data streams to produce game-changing insights, and in today’s post, he highlights recent public studies that pretty conclusively demonstrate that the long-awaited new reality is indeed upon us. he also gives some specific and very useful suggestions on how market researchers need to adapt.

Before we go into a lull for the Thanksgiving holiday here in the U.S. that kickoffs the busiest shopping period of the year, there is no more important message we can deliver to the industry than this one.

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), sponsors include AT&T, Discovery Communications, Intuit, PepsiCo, and Weight Watchers.

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.  Once researchers enrich brand KPIs beyond the venerable survey tracker with social (and other digital) data, they will become an agent of change for the enterprise. Social KPIs will encourage marketers to focus on building their audiences, creating content that is worth sharing, and tracking advertising and promotional campaigns through peoples’ willingness to talk about them and share them. In fact, turning social media data into must-have metrics has already been done via Social TV ratings that both Nielsen and Rentrak offer and it affects the pricing of TV spots.

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’d like to see research turn report card trackers into predictive engines built from time-series data that includes social media, digital, weather, survey tracking results, etc.  Our goal is to get ahead of future trends for a brand so we can influence these outcomes positively before they happen.

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.  To understand the complexity on the last point, consider social media listening for Target the retailer.  Extracting meaningful conversations on the retailer “Target” rather than Seeking Alpha talking about a company hitting its financial targets is not trivial. Also, some conversations map to brand preferences while others map to a hot promotion or topic.

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.  The first meeting included Unilever, Procter, and General Mills…a highly unlikely event…but we all agreed that social media listening had tremendous potential for insights value creation.  This then became the big springboard into the ARF Research transformation super-council.

Now that we know that social media data are quantitative and predictive, we must create research protocols to harness their full transformative power.

Note: For both studies, the social media data streams were provided by Converseon, to whom I am a strategic adviser.

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