by Gord Hotchkiss
What makes up buzz? And what determines how fast it travels? Last week, I talked about how important the opinions of others are in shaping our brand beliefs. Today, I want to look at one category of word of mouth, the juicy tidbit, recently christened “buzz”, and see what makes it leap from person to person.
Buzz is Nothing New
For some reason, we think buzz is a new thing that lives online. In fact, it’s as old as human behavior and has its roots in our very social fabric. We need to pass on information. We’re driven to do so. We gossip because it’s inherently satisfying, both to ourselves and to the recipient. But the spread of gossip through a social network is neither uniform nor consistent. In the 70’s Mark Granovetter discovered that, like many things, social networks are patchy, made up of tightly linked clusters of people who spend a lot of time together (families, friends, coworkers) which are loosely connected to each other through “weak ties”, more distant social relationships. The survival potential of a viral piece of information (Richard Dawkins first coined the term “meme” as a cultural equivalent of a gene in his book, “The Selfish Gene”) lies in its ability to jump Granovetter’s weak ties. If the meme doesn’t jump out of a cluster, it ceases to propagate itself and can die an isolated death.
It’s Not Just the Network
In 1993 Jonathon Frenzen and Kent Nakamoto launched an interesting study that showed that the ability of a “meme” to spread through a social network depended not only on the structure of the network (the main point of Granovetter’s work) but also on the impact of the meme’s message on the carrier (akin to the idea of a phenotype in genetics) and the value of the meme itself.
Frenzen and Nakamoto worked with three different variables:
- First of all, they altered the value of the message. In the first variation, it was news of a 20% off sale, in the other variation; it was the more valuable news of 50 to 70% off.
- Secondly, they varied the amount of product available at the sale price. In one case, there was unlimited inventory. In another, the supply was very limited.
- Finally, they varied the structure of the network itself, in one case having a network of strong ties, and in another, strong tie clusters linked by Granovetter’s weak ties.
What they found was that the value of the message (20% off vs 50 to 70% off) has a significant impact on the rate in which the word spread, as did the availability of items at the sale price. The second factor introduced a moral hazard aspect. It made spreading the news a zero sum game; if I tell you, I might lose out.
Frenzen and Nakamoto also found that in strong tie clusters, word seemed to spread relatively quickly regardless of the nature of the news. There were variations, but in all cases, the majority of the strongly linked network came to know of the news fairly quickly.
Social Speed Traps
If the discount was fairly low, the news tended to get stuck within clusters and had difficulty jumping the weak ties. If the news was valuable (50 to 70% off) and supply was virtually unlimited, the news was much quicker to jump the weak ties, spreading through the network very quickly. But, if the discount was large and the supplies were limited, suddenly the news tended to get trapped within the strongly tied clusters. People were reluctant to spread the news because the more people that knew, the more it was likely that they and their close family and friends (the people within their strong tie clusters) would lose out on a great deal.
Weak Ties on the Web
In both the online and offline worlds, the speed with which buzz will spread depends on the value of the message (is the gossip juicy? Is the price unbelievable?) and how much we stand to gain or lose (does sharing reduce the chances of me and my close circle getting ahead?). Gossip’s primary purpose is to create social bonds and the sharing of intensely interesting information is something we’re programmed to do. Similarly, we’re programmed to share opportunity with those closest to us, e