When trying to create buzz about your brand, do you choose your loyal followers or do you choose people who don’t even know much about your brand? You might be surprised by the answer. A recent research by Professor David Godes and Professor Dina Mayzlin suggests that you should choose the latter group.

Why?

• We tend to know or come across people who are similar to us. Therefore, the friends your loyal followers have are likely to already be your loyal follower too or at least user.
• Loyal followers are likely to buzz about you any way. So the incremental gain from a buzz campaign based on these consumers is limited.

Run the Numbers

These researchers conducted a field study and two lab experiments, which showed significant gain by choosing non-customers as buzz agents. In fact, in the case of Rock Bottom Brewery (a restaurant), they estimated an average of $192 gain in sales brought in by each interaction from non-customer buzz agents. Pretty sizable, huh?

Before You Run Away With It

I see two potential caveats that you should consider before you take the results and apply them to your business:

• Intuitively, it would take more to get people unfamiliar with you to buzz about you. So cost is definitely a consideration. In the Rock Bottom Brewery field study, the non-customer sample came from the BzzAgent company panel. Although both the customer group and the non-customer group were offered potential prizes from the campaign, the non-customer group may have received (or expected) additional incentive from the BzzAgent network (although it’s not explicitly stated in the article).
• People who don’t know a brand well enough may have low credibility when spreading words about the brand. Those who consistently buzz about something they don’t really know (for their own personal gain) may eventually lose the trust of those around them.

More Information

You can hear Professor Godes and Professor Mayzlin talk about their research in this Science of Better podcast. Or if you want to read the article yourself, you can find it in the July/August 2009 issue of Marketing Science (subscription or pay-per-view required).

For the longest time, I’ve wondered what brings the extraordinary success of some user-generated content. Consider, for example, the top ten most popular YouTube videos of all time. The #1 video on the list is a simple one-minute clip of a little baby biting his British English-accented brother’s finger. But it has received a whopping 155+ million views, while your average YouTube video probably doesn’t get much more than a dozen passerby’s attention. Why such a huge difference?  I asked.  When I spoke with my friend Michelle Rogerson, she expressed the same curiosity.  So we decided to set out to answer our question.

To do this, we collected a random sample of slightly more than 100 videos from YouTube over the course of a week. These are all fresh new videos just uploaded onto YouTube, so that we can study their rise to popularity from scratch. We traced each video for a period of two months, recording the number of views and the average user ratings each day. We also collected a large number of characteristics for each video (see the figure below), including those related to the video content, to the video author, and to the network of users connected to the video author. We further recruited a group of individuals to rate each video on its production quality, educational value, and entertainment value, which are the three components of what we call “innate content quality”.

Equipped with all these data, we then used a technique called recurrent events analysis to see how these video characteristics affect the popularity of a video. Below are some of the main things we found:

• Authors with a large number of subscribers who each has only a handful of friends are in a better position than authors with a small number of subscribers who in turn may have a large number of friends.
• Once past an author’s direct network of subscribers, influence rather than simple reach becomes critical. We attribute this to the large amount of user-generated content (UGC) being passed around everyday and as a result our tendency to ignore most sharing unless they come from someone we are really close to or someone whose opinion we respect (opinion leadership is not dead!)
• What proportion of an author’s subscribers know each other also matters. A totally segregated set of subscribers does not help get the words out, but a group of subscribers where everyone knows everyone else is not optimal either. We found that the ideal connection ratio (termed connectivity or density in network analysis) was about 38%. Below this ratio, diffusion rate increases with connectivity up to the maximum, and then decreases after this threshold.
• Of the three innate quality components, entertainment value and educational value are equally important in determining a video’s popularity. Production quality, on the other hand, did not matter.
• But the biggest impact did not come from innate quality but from what we call “manifested quality”, which is quality information publicly available through other users’ ratings (i.e., the little stars underneath each YouTube video). Increasing the average rating by 1 star can lead to as much as 13.5% gain in diffusion rate.
• Age has a negative effect on diffusion rate, meaning that younger users’ contributions are more likely to be popular.
• An author’s past experience and success also carry over to the new content. More prolific authors and authors whose past contributions were more popular are more likely to see their new content popular.

Of course, with only one study, we are far from completely answering our initial question.  But what we found here suggest that there are indeed systematic differences among videos and authors that can help predict the success of future content. Carrying this over to other types of user-generated content such as tweets and consumer blogs, these findings and findings from future studies should help companies pour through the overwhelming amount of user-generated content available online and selectively invest effort in the ones that are most likely to become popular.

What do you think?  I’d love to hear your thoughts.  Is there anything important that we are missing? If you are interested in more details about our study, you can download our working paper at http://www.yupingliu.com/files/papers/liu_rogerson_ugc_diffusion.pdf.

Some people may disagree with what I am about to say here: online social networks bring people closer to each other. At least that is the personal impact that they have had on me.  But what does this mean for marketing?  One answer is that word-of-mouth between consumers is carrying more weight in how we choose and consume products. Whether we love or hate a product, now it is so easy to make it known to the public that we are essentially affecting the opinions of other consumers (from total strangers to close friends) every day.

Managers are often hesitant to invest in encouraging word-of-mouth, however, as its effects are notoriously difficult to measure.  This is because word-of-mouth behavior is often unobserved, and it is difficult to tease out the concurrent impact of traditional marketing.  These are the exact problems a recent article by Michael Trusov and colleagues in Journal of Marketing tried to tackle.  Entitled “Effects of Word-of-Mouth Versus Traditional Marketing: Findings from
an Internet Social Networking Site”, this article offers a clear answer to the relative effectiveness of word-of-mouth vs. traditional PR and marketing.

What did they look at?
The impact of word-of-mouth, event marketing, and media appearance on the sign-ups for an undisclosed online social network.

Some intuitive findings:
More new sign-ups resulted in more word-of-mouth; event marketing led to more media appearance, and vice versa;  word-of-mouth was not affected by previous event marketing or media appearance, however, suggesting consumers’ relatively independent opinions and actions.

Some not-so-intuitive and very important findings:
The 3-day elasticity of sign-ups with respect to word-of-mouth was .17. In layman’s terms, this means that doubling the amount of word-of-mouth increases sign-ups by 17%. The corresponding impact from event marketing and media appearance, in contrast, was only 1.7% and 2.2%. The gap became even bigger with regard to long-term effects.  In the long run, the effect of word-of-mouth is 20 times that of event marketing and 30 times that of media appearance.  While doubling event marketing or media exposure led to 1.7% and 2.6% respective increase in sign-ups in the long run, doubling word-of-mouth increases sign-ups by a full 53%. Financially, an outbound word-of-mouth referral translates into 75 cents/year increase in advertising revenue.

What does this mean for marketing practice?
Word-of-mouth is a powerful tool for customer acquisition.  With the help of more powerful tracking tools provided by social networks and websites, it is possible for managers to measure the return from word-of-mouth activities. The mathematical approach used in this article (vector autoregressive modeling) further helps tease out the impact of other marketing and PR activities so that the true effect of word-of-mouth can be accurately measured. Together, this should reduce the hesitation to incorporate word-of-mouth into a company’s overall marketing strategy. The findings from this article also provide a strong motivation to better utilize word-of-mouth channel of communication.

Cautions
Readers should be cautioned from taking the results from the above research too literally.  Two things should especially be taken into consideration.  First, the data came from an online social network.  Customers on such websites are usually highly motivated to invite their friends, and those invited by their friends are also very likely to sign up.  If we were to change the context to, say, online banking, both the level of referral and the impact of referral are likely to be lower.  Second, the word-of-mouth activities studied in this article are all organic referrals initiated by consumers themselves. If the word-of-mouth had been stimulated by the company (say, with financial incentives), the referrals may not have been considered as genuine to other consumers and therefore may not have created as strong of an effect as reported in this study.  Although these are real limitations, the findings from this study are still quite powerful indicators of word-of-mouth effect. It is a tool managers should not ignore.

Reference
Michael Trusov, Randolph E. Bucklin, and Koen Pauwels (2009), “Effects of Word-of-Mouth Versus Traditional Marketing: Findings from an Internet Social Networking Site,” Journal of Marketing, Vol. 73 (September), p.90-102.