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After the Interactive Advertising Bureau (IAB) guidelines on social media ad metrics, the Word of Mouth Marketing Association (WOMMA) has come out with a draft of its guidebook on measurement and metrics for word of mouth marketing (PDF).

The guidebook seeks to “offer a broad overview of the types of metrics available, key considerations for their use, and specific examples of their application.” WOMMA also cautions that “the guidebook is not intended to offer industry standards or a definitive statement on the one right way to measure word of mouth”.

The first draft of the guidebook looks at seven different types of metrics –

- Advocacy: Measures the intent and/ or behavior of making recommendations using approaches offline surveys or online network and content analysis.

- Conversation Share: Measures the volume and share of conversation using ongoing online buzz monitoring and offline syndicated research, and campaign specific custom research.

- Cost Per Conversion: Measures the cost of getting one person (prospect) to perform the desired action (purchase), after factoring in conversion value, conversion attribution and incremental conversions.

- Conversational Reach: Measures the cumulative penetration of a brand message within a given target audience through conversations, by using a multi-generational approach.

- The Influencer Factor: Identifies influencers and measure their word of mouth activities via self-report surveys, online buzz monitoring and sociometric network analysis.

- Cost Deflection: Measures the decrease in R&D, time to market and customer support costs through customer feedback and peer-to-peer support.

- Value of a Conversation: Measures how much a positive or negative conversation is worth to the brand’s bottom line by using customer lifetime value, WOM referral value and media mix models.

The draft says that sections on Sentiment Analysis, Overall ROI, Media Reference and Ratings & Reviews will be added to the final paper.

I think the WOMMA guidebook has the “potential” to become an important resource for word of mouth measurement. I like that it not only describes a metric but also explains what it means and how to measure it. Also, the focus is more on broad measurement approaches than narrow metrics. Finally, the guidebook includes both online and offline measurement of word of mouth, and sometimes even describes their relative merits and demerits.

At the same time, I see two tendencies that are worrisome.

First, there’s a strong bias in the draft towards the services offered by the people writing the report. Perhaps, this bias is innocuous, a result of the writers’ familiarity with their own solutions, but the draft sometimes reads like a sales brochure. I think a better approach will be to focus on the measurement approaches and metrics without mentioning the specific tools and services, or at least underplaying them.

The other problem, which is a direct result of the first bias, is an extremely high focus on offline word of mouth research, so much so that less than a quarter of the report is about social media analytics. Perhaps, the authors are so invested in their own offline word of mouth research approaches that they are unable to appreciate how fundamentally social media has changed word of mouth marketing and measurement.

Overall, I am hopeful that WOMMA will be able to overcome these biases and produce a useful guidebook that doesn’t read like a sales brochure written by a committee.

Do share your views on the WOMMA measurement guidebook here and help them develop a truly useful industry resource.

Cross-posted at The 20:20 Social Media Analytics Blog.

DigiActive co-founder Mary Joyce and I are delighted to announce our new co-authored blog Netfluence.org, which is an investigation into whether and how networked technologies influence political power structures.

The debate on whether internet and mobile technologies are transforming traditional power structures is dominated by three divergent narratives.

According to the first, utopian, narrative, internet and mobile technologies enable individuals to publish and distribute content, self-organize into communities of interest and participate in collective action. As a result, they can create new types of media outlets, build new types of civil society organizations, and monitor, protest against and even bring down governments. Even though these new degrees of freedom are far from universal, they are fundamentally changing political power structures. The future has already arrived, this narrative insists, it’s just not evenly distributed yet.

According to the second, status quo, narrative, power structures are ingrained into our society’s institutions, and internet and mobile technologies don’t really change these institutions, or create new ones. The case studies compiled by the utopians constitute anecdotal evidence, at best, and the influence of networked technologies will always be limited because of issues related to access or ability. So, internet and mobile technologies are a minor influence on political power structures, at best.

According to the third, dystopian, narrative, internet and mobile technologies are, in fact, enabling traditional institutions to further consolidate their power through censorship, surveillance and propaganda. So, even though they give us the illusion of greater power, they have, indeed, compromised our ability to protect our privacy, have access to diverse views, and build real institutions.

Both of us have roots in the digital activism community, so our natural bias is towards the first narrative. However, we have seen enough evidence for and against all three narratives that we felt the need to objectively investigate their relative merits.

We will look at the interplay between networked technologies and political power structure through different lenses. We will explore if the power dynamics between individuals and institutions is changing. We will ask if power is shifting from states to non-state actors. We will also investigate if these technologies are leading to the formation of new types of (non-commercial) (non-)institutions.

By delving into books, academic papers, and news articles, by engaging in formal and informal conversations with thinkers and practitioners, and through first hand involvement in projects that seek to subvert political power through the use of internet and mobile technologies, we will compile a collage of perspectives that will hopefully result in a book worthy of your attention.

Our first big question, and the topic of our next post: how are internet and mobile technologies changing diplomacy?

Cross-posted on Netfluence.

Social network users may have a large number of Friends or Followers or Followees, but they regularly interact with a much smaller number of people in their inner circle who matter to them and reciprocate their attention.

On Facebook, users only poke and message a small number of people while they have a large number of declared friends.

Mobile phone users regularly call or text message only a small percentage of the contacts stored in their phone.

Now, Bernardo A. Huberman, Daniel M. Romero and Fang Wu from the Social Computing Labs at HP have shown that, irrespective of how many followers and followees they have, most Twitter users send @usename messages only to a few other users.

The researchers studies 309,740 users, who on average posted 255 posts, had 85 followers, and followed 80 other users. Only 211,024, or 68%, of these were active users who posted at least twice and the average time between their first and last tweets was 206 days. Also, around 25.4% of all tweets included an @username message.

The research has some interesting analysis on the nature of Twitter Friendship based on @username messages. It defines user A as user B Friend, if user B has included @A in at least two tweets. It is independent of whether only user A is following user B, or only use B is following user A, or both are following each other. So, if a user has very few followers and followees, but uses @username messages very often, the number of his Friends can be higher than the number of his followers and followees.

However, the Friends/ followees ratio is less than 1 for 98.8% of the users, and most users have a value less than 0.10. The average of the ratio is 0.13 and the median is 0.04.

The research also show that even though the number of Friends initially increases as the number of followees increases, after a while the number of friends starts to saturate and stays nearly constant.

This indicates that even though users declare that they follow many people using Twitter, they only keep in touch with a small number of them. Hence, while the social network created by the declared followers and followees appears to be very dense, in reality the more influential network of friends suggests that the social network is sparse.

This finding has significant implications for how influence on Twitter is measured. Specifically, it seems that @username messages and retweets are more important than the number of followers in estimating Twitter influence.

Finally, the research shows that the number of tweets initially increases as the number of followers increases but it eventually saturates. However, the number of tweets increases as the number of Friends increases, without saturating. This suggests that the number of Friends is a more accurate predictor of how active a Twitter user is than the number of his followers.

The research seems to suggest that Twitter users who use @username messages are more engaged in their Twitter network than users who don’t use them. So, if the first rule of Twitter is Robert Scoble’s “who you follow is more important that who follows you”, the second rule of Twitter may be: “how many times you mention others in an @username message is more important than how many times you are mentioned”.

Also see: Om Malik, Jeremish Owyang, Greg Verdino.