The Mystery of 'Word of Mouth Equity'

Recently, McKinsey published an article on the value of Word of Mouth Marketing. They promote a concept labeled 'WOM Equity' ("an index of a brand’s power to generate messages that influence the consumer’s decision to purchase") At its heart, the writers acknowledges the emerging strengths of word of mouth marketing even going so far as to suggest that brands who want to make a leap ahead of competitors have a snowball's chance in hell of doing it via traditional advertising and a better-than-decent shot at it via the trusted platform of WOM:

"...the incremental gain from outperforming competitors with superior television ads, for example, is relatively small. That’s because all companies actively manage their traditional marketing activities and all have similar knowledge. With so few companies actively managing word of mouth—the most powerful form of marketing—the potential upside is exponentially greater."

Show a Case
The WOM Equity premise is quite straightforward. They distinguish between high influence and low influence relationships. They form a bit of a binary 'use-case' which will undoubtedly get us all to a conceptually sound way to rate the actual impact a WOM mention has on us. But the actual data and data collection needed for the model is to shrouded in mystery. Here is how they put it:

"To assess the impact of these different kinds of recommendations, we developed a way to calculate what we call word-of-mouth equity. It represents the average sales impact of a brand message multiplied by the number of word-of-mouth messages. By looking at the impact—as well as the volume—of these messages, this metric lets a marketer accurately test their effect on sales and market share for brands, individual campaigns, and companies as a whole (Exhibit 2). That impact—in other words, the ability of any one word-of-mouth recommendation or dissuasion to change behavior—reflects what is said, who says it, and where it is said. It also varies by product category."

The question for most of us applying WOMM today is not how to form a rigorous measurement hypothesis. Look at any of the past four volumes of WOMMA's measurement summit or the recently published WOMMA Metrics Guidebook, and you will find some of the best thinking on WOMM measurement almost all of which is backed up by real cases. The problem has always been coming up with a model that can not only be tested or put into practice on rare occasion but can be applied on every program and still be stronger that the traditional marketing metrics we have all grown to accept. The theory here will be strengthened when the authors show us a case.

Strong claims
There are some powerful quotes throughout the article. I love them all and only wish the data and source for each was highlighted alongside. The danger now is that WOM marketers will lift these claims and put the "McKinsey" name as a credit and hope savvy marketers won's ask, "hey, wait. Where did the data come from..."

But that won't keep me from rounding up some of my favorites here:

• "In the mobile-phone market, for example, we have observed that the pass-on rates for key positive and negative messages can increase a company’s market share by as much as 10 percent or reduce it by 20 percent over a two-year period, all other things being equal."
• "The impact of those messages (marketing messages passed along by consumers) on consumers is often stronger than the direct effect of advertisements, because marketing campaigns that trigger positive word of mouth have comparatively higher campaign reach and influence."
• "Marketers tend to build campaigns around emotional positioning, yet we found that consumers actually tend to talk—and generate buzz—about functional messages."
• "About 8 to 10 percent of consumers are what we call influentials, whose common factor is trust and competence. Influentials typically generate three times more word-of-mouth messages than noninfluentials do, and each message has four times more impact on a recipient’s purchasing decision. About 1 percent of these people are digital influentials—most notably, bloggers—with disproportionate power."
• "In fact, McKinsey research shows that marketing-induced consumer-to-consumer word of mouth generates more than twice the sales of paid advertising in categories as diverse as skincare and mobile phones."

Each of  these quotes is compelling. Each begs the question of where the data comes from. I fear that much of it is pulled together and out of context of existing studies and POVs. The last quote is clearly some reference to the Ed Keller book, The Influentials, which remains a valid POV to this day and yet has been joined by many other interesting and valid POVs on influence since then.

What's Missing?
Not only do we need to see the data behind some of these claims and the model they propose, we need to embrace the science behind influence from Cialdini to Watts to so many others to start to understand how we can use predictive models to impact sales through Word of Mouth Marketing. The authors seem to believe they have added the 'science' to the existing 'art' when, in fact, there is a ton of science out there already substantiating and refining their claims.

Comments

[Ted Wright]

NIce piece John. I look forward to a response from the McKinsey authors who seem to have gone silent since publication of their article.

Just a quick question - what valid science has Duncan Watts contributed to measuring WOMM? Before he "went on leave" from Columbia I remember the assumption filled computer models. Is there something else that I missed?

[Lucygriffiths]

Surely the game changed with Zuckerberg's Opengraph announcements at F8 last week...any thoughts John?

[Gigi DeVault]

Great post, John. I echo your reservations about the McKinsey article. Though you didn't say it outright (appreciate your professionalism) McKinsey "whitepapers" seem to suffer from a dearth of citations of other work from which they draw (your POV euphemism may suggest this). Perhaps it is an artifact of McKinsey's consultant over-reach. So I, too, am looking for a case / data to support what appears to be a promising construct.

I have just recently begun to write for About.com and do not have a budget that permits membership in WOMMA. Can you point to some WOM analytics resources that I can review & summarize in order to help my readers?

Thank you much.