What Social Media Data Can’t Tell You

  • social data

Originally published by Marketing’s Russ Martin. Follow him on Twitter @russless and @Marketing_Mag

For all the insights social media data offers, there’s still plenty it can’t tell you. And if marketers fail to set social data in context, it can actually send them in the wrong direction, according to Alexandra Samuel, vice-president of social media at the Vancouver-based Vision Critical.

Samuel is set to present three case studies at a South By South West panel Friday about what social datacan’t tell brands. Ahead of the talk, Marketing spoke to Samuel about misconceptions social data can lead marketers to, why the most engaged social consumers aren’t the best to listen to and why all social media data needs to be set in context.

Here are four lessons from the interview.


There’s a “crisis of the week” phenomenon on social media. Whether it’s an offensive tweet from Kenneth Cole or an enragingly inappropriate rant from an executive, there’s always a gaffe for the social set to pounce on.

But brands in hot water may not be in as much trouble as they think. Samuel said the loudest protests often come from non-customers. If a brand doesn’t monitor or talk to its customers, it won’t know whether a “crisis” has any effect on its specific base.

She uses Qantas’ 2011 “fail” as an example. “Qantas had a huge social media uproar because they did a Twitter promo people regarded as tone-deaf and insensitive. There was a big backlash, but at the end of the day, a lot of observers said ‘For all the negative PR, it probably didn’t actually effect the customers they were targeting because it was a niche campaign.’”


On social media, there are lurkers and sharers. The sharers – consumers who express their opinions and help content reach a larger audience – however, don’t represent the general public or even a brand’s social audience.

Take a retailer launching a capsule collection with a fashion designer as an example. “You might notice it gets a lot of pickup from your Facebook audience and people are really excited about a new high fashion line,” Samuel said. “Interestingly, when you compare lurkers and sharers, it turns out that sharers – the people who are posting, tweeting and responding – are fashion-oriented. People who post less and are more likely to be quiet, are much less interested in fashion and much more interested in price”

Samuel said Vision Critical’s research has shown there are huge differences between loud and quiet users on social media, which has a big implication for brands. “The problem is that when you rely on social media monitoring to tell you what your social media audience cares about, you’re actually hearing from only a slice of that audience, and it’s a slice that does not represent your overall social media users well,” she said. “You end up with a social media marketing strategy that skews towards what sharers are interested in.”


According to Samuel, most social media users are lurkers, not sharers. Vision Critical studies have found about 70% of social media users are relatively quiet.

This means much of what marketers learn from social data needs to be set in the context of the larger group. “To the extent social media monitoring is getting incorporated more and more by companies as a source of market research, it actually starts to skew your overall understanding of your customers because you think that what you hear on social media represents [all] your customers,” Samuel said.


The solution, Samuel said, is to set data in context for a brand’s particular customer base. This may mean comparing data to other information a company has, such as in-store data, transaction history or outside data like polls, though Samuel cautioned against trying to directly compare different data sets.

At Vision Critical, social data is compared to data from the private communities the company runs on behalf of brands like John Deer, Molson and Virgin Mobile. Combined, Samuel said the two data sets can lead to powerful insights and give brands a more clear picture of the consumer’s desires.

“One of the virtues of this approach is that if you know how your customer community breaks down in terms of their social media usage, you can use what you are learning in your private customer community to contextualize what you’re hearing from social media users,” she said. “I think this represents the future of how people will use social media data, which is by contextualizing it and not just with two separate data sets, but by integrating two types of data together.”