Social Media Strategy For Today’s CMO

As the one-time Chief Marketing Officer of a large global wealth management business with severe reputational issues during the 2007 financial crisis, I struggled mightily with how to engage with and use social media to help my brand.

Five years later, you’d think things would be better. But almost all of the CMO’s and Marketing leaders I talk with are still struggling with Social. Everyone knows it’s important, they know they need to engage with it, yet they are still trying to answer fundamental questions that are relevant to any Marketing activity:

1) How do I engage and use social media to benefit my brand and drive sales?

2) How do I measure social media and the impact it’s having on my brand—positive or otherwise?

These are challenging questions that almost all Marketers, no matter their sophistication, are struggling with. The question is, why?

Social Media as a Business Driver

In most Marketing activities, Marketers design marketing programs that they believe will increase basic Marketing measures like awareness, trial and consideration, and ultimately result in higher sales.

To do so, Marketers have created predictive tools that increase their confidence that these programs will ultimately work. Examples include:

• Copy testing to predict likely advertising success in building sales
• Purchase intent scores, which measure the likelihood of consumers buying your product
• Market mix modeling norms, which indicate how much sales $1 of investment will yield
• Etc.

A key issue with social marketing programs is that there are few of these measures, and even where they exist, they are highly suspect. Here’s why.

The Impact of Social – An Experiment

In his book “Everything is Obvious Once You Know the Answer,” Duncan J. Watts describes a web-based experiment designed to emulate a “market” for music, as a means of measuring the impact of social media. He designed the experiment for consumers to listen to, rate, and potentially download songs.

First, he created two groups. Both groups were given the same set of songs to listen to and rate. Each group was further divided into sub-groups so it would be possible to see how multiple groups responded to the same environmental conditions.

But, there was one key difference between the two groups. Group A consumers were not able to see how many of their compatriots were downloading the songs they were listening to. Group B, in contrast, was able to see the downloads and rank order popularity of the music based on other participants behavior. Said differently, group B had a “social layer” of potential influence while Group A did not. The experiment was repeated” multiple times given that each of the sub-groups had different participants.

Group A results with different participant groups were remarkably consistent — the rank order of songs was similar across groups. If song A was number one in the first group, it was also number 1 in the 2^nd group, 3^rd group, etc.

This tells us not only that consumer music popularity without a “social layer” is consistent and relatively unchanged across groups, but that it is highly predictable.

Group B results were quite different. The most popular songs, and the less popular ones, were typically even more popular or less popular in Group B than Group A — e.g. the “social layer” of seeing how other participants downloaded songs sharply accentuated the way the degree to which consumers in Group B rated and downloaded songs.

But, equally important, results across different participant groups showed large variations in popularity. So, if song A was most popular in the first group, it might only have middle popularity in the 2^nd group, and so forth.

Social Makes Results More Extreme and Unpredictable

Duncan Watts had this to say about the results:

“In all the ‘social influence’ worlds…popular songs were more popular (and unpopular songs were less popular) than in the independent condition. At the same time, however, which particular songs turned out to be the most popular—the “hits”—were different in different worlds. Introducing social influence…increased not just inequality but unpredictability as well.”

Said differently, the “social layer” not only caused the ratings to be different, but also made the results much less predictable. This is the  challenge that Marketers face with social media:  social interaction and the impact it has on your brand is highly unpredictable.

In a world where CMO’s want to be sure that every dollar they are investing — in time, people and resources — is money that delivers a good return, a fundamental challenge with social is that it’s much less predictable than traditional Marketing programs in how it impacts your brand.

What to Do? Measure & React

What’s a CMO to do? Well, I believe that real time measurement of social — how well its reaching your consumers, how its changing their attitudes toward your brand (for better or worse), and what impact its having on your sales — is the right way to mitigate and take advantage of the unpredictable nature of social.

Watt addresses unpredictability as follows:

“Rather than predicting how people will behave and attempting to design ways to make consumers respond in a particular way…we can instead measure directly how they respond to a whole range of possibilities, and react accordingly.”

Real time “measure and react” is the best way that you can engage and respond to consumers in a social world when it’s so difficult to predict in advance how social engagement will play out.

That’s why CMO’s need to focus less on creating highly compelling (and perfect) social content and programs, and invest more in quickly understanding how consumers are responding to the various elements of their plan and then adjusting accordingly.

It’s obvious, CMO’s just need to continuously “measure and react,” because when they do so, they already know the answer — and how to respond.

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