Learning From the Dynamics of Viral Marketing


In what context do viral marketing strategies work? How do on-line product recommendations develop, multiply, spread and ultimately, dwindle and die? And, can Marketers influence any of this?  

These were important questions posed by Leskovec, Adamic, and Huberman in their 2008 study “The Dynamics of Viral Marketing.” This is one of the few studies I’ve seen to actually study how on-line recommendations grow virally and how this growth impacts purchase behavior throughout the viral network.  

Viral Marketing -- When and Where Does it Work?

 

Admittedly, the study had limitations, notably that it was only four categories, just measured on-line viral activity, and included a discount incentive to help motivate purchase. But, even with these limitations, it uncovered deeper insights into the systematic patterns in knowledge sharing and persuasion online—all of which are of high interest to Marketers.  

What Did the Study Entail?

Lescovec et al. examined an online recommendation network composed of 4 million people who made 16 million recommendations for 0.5 million products. Each time a consumer purchased a book, music, or movie, he or she could  send e-mails recommending the item to friends. The first person to purchase the same item through a referral link received a 10% discount.  

What was Measured

  • When and at what price a product was purchased
  • If the product was recommended to others
  • Whether the recommendation resulted in a subsequent purchase and discount

They then modeled the effectiveness of recommendations as a function of the total number of previously exchanged recommendations.  

Recommendation Networks Grow Slowly Over Time.

 

Important Viral Network Learnings & Insights

Finding #1:  Consumers recommended a large number of products to the same group of people. As a result, recommendation networks became heavily locally-based. For example, in the DVD recommendation network there are 182,000 pairs that exchanged more than 10 recommendations.  

Consumers Tend to Recommend Products to the Same People

 

Finding #2: Recommendation networks centered on a specific product category. That is, the people tended to focus on recommending a particular product category and thus created a “community of interest.” Having said this, most all networks shared recommendations for all types of products.  

Finding #3: Trust, influence, and perception of “spam” affected purchase. As people exchanged more recommendations, the likelihood they would purchase the product increased due to a growing foundation of trust. However, purchase likelihood increased, peaked, and then fell as consumers received additional recommendations for a specific product. A few recommendations built credibility; too many appeared as “spam.”  

Finding #4:  Most recommendation chains didn’t grow very large. In fact, most terminated with the initial product purchase, and even the largest connected networks were very small as a percentage of the total population.  

Recommendation Chains Don’t Typically Grow Very Large

 

Finding #5: 20% of recommendations accounted for 50% of sales. This is not far from the usual 80-20 rule, where the top 20% of products account for 80% of sales.  

What are the ‘Viral’ Implications for Marketers?

1. Identify the “Amplifiers.” Given that 20% of recommendations generate 50% of sales, it’s key to figure out whom the amplifiers are and focus your efforts on them.  

2. Determine Where the “Amplifiers” Congregate. Where do they exchange product information? On what platforms do they consume media? Web behavior can be linked to off-line purchase panels to quantify the effectiveness of recommendations (see “What Really Drives Web Advertising ROI”).  

3. Take Online Recommendation Networks to the next level Through Social Media Marketing. Marketers should explore development of models to measure recommendation systems on Twitter, Facebook, Foursquare, and the larger online arena. Through broader web 2.0 outreach, marketers can quantify consumer engagement on recommendation networks by volume, reach, tone, and source.  

Marketers can optimize paid media and earned media with viral marketing.

 

4. Be Wary of Creating “Recommendation-Fatigue.” A fine line exists between trust and influence in recommending a product and what is widely considered “spam.” Consumer engagement via any online channel must be done with careful consideration of earned media and buzz promotion.  

Viral Marketing: Limitations…

What’s not yet so clear from the research is how to minimize transmission “breakdown” – e.g. how do you minimize the likelihood that a product recommendation is the last one. As the research showed, most viral networks don’t grow very large. Marketers will only invest significant money if they can truly scale viral marketing programs.  

…And Future Opportunities

With the right tools and metrics, marketers can diversify their marketing plans to incorporate viral marketing strategies. The research clearly shows that viral marketing can build unique and niche recommendation networks, bolster consumer engagement, and lift sales.  

And as consumers continue to favor a digitally-based, social network-centric world, it’s critical that Marketers become more expert at viral marketing. Key to this will be identifying amplifiers, focusing on congregation points, leveraging social media opportunities—all without overdoing it. As importantly, Marketers must discover new approaches to spread and scale viral marketing just as effectively as the flu seems to proliferate every flu season.  

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5 Responses to Learning From the Dynamics of Viral Marketing

  1. asit says:

    Randall, Great post. What are your views on effectiveness of offline WOM vs online WOM? Needless to say offline networks are more expensive to set up and track, but P&G ( where I also worked) started TREMOR & VOCALPOINT before the web revolution. Any learnings from that. Look forward to your insightful thoughts.Regards. asit

    • beardrs says:

      Asit — Thanks for reading the blog and your comments are much appreciated. I often remind people that there is much more off line word of mouth than on line. Keller Fay has a nice syndicated offline word of mouth tracking service. As you say, P&G’s Tremor has done a lot of work in understanding the dynamics of word of mouth. Generally, learnings are that: a) a relatively small group of consumers — typically 10-15% — in any category are the ones who really drive WOM as they have larger than normal social networks and they tend to derive part of their self-image from sharing new and insightful information with their network; b) they typically don’t talk primarily about the value proposition but rather things that they believe they have the inside perspective on. This is consistent with my own experience in using research to understand what people talk about when they talk–and it’s usually not the brand value proposition. c) it’s key to identify stimuli with this amplifier group that motivates them to talk about the brand. Interestingly, Keller-Fay WOM research generally shows that about 20% of WOM is about brand advertising. This is an under-researched area that needs to be understood further–e.g. how can ads be designed to drive both high purchase intent but also word of mouth.
      Randall

  2. asit says:

    Thanks Randall for the insightful response. The fact that the “influencers” talk mainly about what they themselves pick up while using the brand/product and not what is suggested as potential sound-bytes is really interesting. I will look up Keller-Fay. Thanks for the lead. Regards. Asit

  3. […] Read more analysis and the 4 Viral Implications for Marketers at Randall Beard’s blog [01-25-1… […]

  4. […] at scale. So, instead of your brand’s viral efforts petering out as most efforts do (see The Dynamics of Viral Marketing […]

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