How to monetize the fuzzy narratives of social listening
Marketing professionals, such as yours truly, use social-listening analytics tools in the hope that they reveal whether customers are likely to stay loyal, buy more stuff, and say nice things about our companies and products. What these tools reveal is how people might or might not be leaning in the aggregate, under the questionable assumption that social media users are a cross-section of the target population you’re trying to engage.
Even if your entire target market were on social media, you’d be ill-advised to accept social intelligence as an indicator of how individuals truly feel about your brand. As I’ve stated, few customers declare their feelings in the form of tweets or Facebook updates that represent their semiofficial opinion on the topic. Even if people aren’t lying, everyday speech is full of ambiguity, vagueness, situational context, sarcasm, elliptical speech, and other linguistic complexities that may obscure the full truth of what they’re trying to say.
What we truly want from social listening is what we simply aren’t getting. What we’re actually getting is a blizzard of aggregated social metric data that measure any or all of the following:
- Social buzz: Many listening tools specialize in measuring aggregated social buzz by keywords, topics, hashtags, and conversations. The metrics might also show how the buzz shakes out into sentiment and “share of voice” by brand. It might even show difference in the buzz by social channels, geographies, demographics, influencers, day of week, and other such dimensions.
- Social reach: Listening tools might help you assess the followership of your specific social channels and impressions of your social postings across geographies, demographics, influencers, and so on.
- Social engagement: The tools might indicate the extent to which your social postings have driven shares, likes, replies, clickthroughs, and other indicators of customer involvement and sentiment with your brands, campaigns, and products.
When presented individually or in various visually compelling formats, those numbers can tell a wide range of stories. However, what social listening tools rarely present is a statistically validated causal narrative that we can use to predictively recalibrate our social marketing tactics. In the abstract, such a narrative might be structured as follows: “Social listening metric A showed that marketing tactic B created conditions C under which customer D expressed positive sentiments about, actually purchased, or recommended that others purchase product E under circumstances F and are highly likely to cause them or customers like them do so again under similar circumstances.”
If we can’t have strictly causal narratives of this sort, strong correlations would be an acceptable second-best approach for most marketing uses. However, most social listening tools can’t even deliver statistically predictive narratives at a level that might be actionable to social marketing professionals planning a campaign. We often have to resort to either hard numbers that don’t fit into a coherent story of what’s going on in customers’ hearts and minds or to soft customer-propensity narratives that are tangentially illustrated by whatever metrics we’ve been able to grab from our tools.
As Chris Atwood stated in his recent blog, “social listening has a nasty habit of being completely soft — all about the words without the context of volume and velocity of conversation — or completely quantitative with little information about what’s actually being said.”
Or as screenwriter William Goldman stated in his classic observation on the Hollywood hotshots who bankroll and market major motion pictures: “Nobody knows anything.” Even with predictive analytics and social listening engines at their disposal, most industries must deal with the difficulty of marketing-lift attribution analysis. This refers to statistical techniques for identifying which ads, promos, and other marketing tactics had the biggest impact on the success of a campaign. Attribution analysis is an inexact art at best, and it can’t assess the efficacy of social engagement any more precisely than it can with any other marketing tactic.
As I’ve stated elsewhere, I’m a little skeptical of any assertion that marketing-lift attribution can be boiled down to an exact science. If we were able to isolate the definitive factors, such as social engagement, driving any specific person to take any specific action, the social sciences would simply become social engineering. We may know the online pathways through which customers visit your online store, browse your products, and make purchases, but can we truly know the decision path that they follow in their heads and hearts?
Truth be told, the only social metrics that matter to businesses are those they can monetize. How well do your social tactics correlate with sales-funnel metrics? In other words, how effective has your social marketing been in encouraging potential customers to visit your website, product pages, and other properties where sufficient contact information might be collected to drive the sales funnel? What percentage of the contact information collected from social marketing tactics are subsequently verified, qualified, and validated as hot leads? And what percentage of validated leads are ultimately converted in to paying customers?
If you can consistently show that a strong social push translates to the ka-ching of new-customer conversions, that’s the best narrative you can present to the senior execs who fund your campaigns.
Source: InfoWorld Big Data
