Surprising Buzz Champions

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.

Buzz

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).

Rising to Stardom: What Makes Some User-Generated Content So Popular?

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”.

UGC Diffusion Factors

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.

Are You Targeting the Right People to Grow Your Community?

Last time I discussed a few research findings on what makes people pass on information to others.  This week, I would like to follow up on the topic and talk about a recent project done by Zsolt Katona (@UC Berkley) and his colleagues.  The research question Katona and colleagues set out to answer is what drives the growth of an online community. They surmised that the specific social network structure of the initial adopters affect the adoption likelihood of subsequent followers. To test their thinking, the researchers analyzed the first 3.5 years of data from a central-European social networking website, when no marketing activities had been engaged to promote the site and the network had been growing organically through word-of-mouth.  Here is the gist of what they found.

People do tend to follow the crowd but a more closely-knit crowd carries much more power

We all have hesitations when it comes to novel new things and may consider them risky. Depending on how risk averse we are, we may wait until some or a majority of other people have adopted the new thing before we jump onto the wagon. In my own research project documented in the last blog, we found the median adoption threshold to be 50%, incidentally supporting the “majority rules” mentality. But the threshold reported by our sample ranged across the whole spectrum from 0% to 100%. Consistent with this idea of an adoption threshold, Katona and colleagues found that more people in one’s social circle adopting a social network makes one more likely to join the network. In this context, perhaps an additional driver besides risk is the fact that the utility of a network increases when more of one’s friends belong to it. The story does not stop here, however. The researchers also found that a closely-knit (or high-density in network science terminology) network where everyone knows everyone else is much more influential. If the same number of individuals in a closely-knit network joins a social network, the remaining non-adopters are much more likely to follow suit than if it were a loose (low density) network of sorts.

Network

Social butterflies are not the most influential

In network science, the fact that some individuals have way more friends/connections than most others in the same network has often been compared to the rich get richer phenomenon.  But unlike the richer people who do have solid cash to spend, social butterflies who have tons of friends (think 1000+ or even 500+ Facebook friends) are actually quite weak when it comes to influencing other people’s opinions. Well, at least when it comes to the decision to join a social network any way. This may be surprising on first look. But not so when one thinks deeper about human psychology. We all have limited energy to build and maintain friendships. The more friends we accumulate on a regular basis, the less energy we have to develop a deep and meaningful relationship with each individual, and thus the less we are able to exert a strong influence.

Weak ties may be good for information travel but exert limited influence

The strength of the weak tie has been a well-known phenomenon for more than 20 years, referring to the fact that weak ties that link disconnected networks are critical to the spreading of information. However, for exactly the same reason, the central role played by these weak ties also makes a network formed around such ties more vulnerable.  Referring to these individuals as structural holes, Katona and colleagues found that the adoption of a social network by these structural holes has less of an impact on their friends, perhaps accurately reflecting the fact that these are “weak” ties.

Lessons learned

  • Many factors create counter effects when it comes to increasing awareness of a community vs. increasing participation in a community.
  • While sometimes it may be necessary to target loosely-knit networks (more weak ties) for increasing the awareness of your online community, closely knit networks are eventually critical to increasing actual participation in your community.
  • The same thing goes with highly-connected individuals. While those who have lots of friends may be good for getting the word out, individuals who have a more moderate friend circle may be more ideal for building the community.
  • For a business, how these counter effects should balance out will depend on the exact goal for the online community at each stage.

Reference

Zsolt Katona, Peter Pal Zubcsek, and Miklos Sarvary (2009), “Network Effects and Personal Influences: Diffusion of an Online Social Network“. The full paper can be downloaded from Katona’s website at http://www.cs.bme.hu/~zskatona/pdf/diff.pdf