Archive for June, 2010|Monthly archive page

Putting Your Money on Word of Mouth: Research on the social value of seeding programs

In Marketing, Technology on June 3, 2010 at 12:27 pm

This post was originally featured on, published on June 3rd, 2010. Since the website will be relaunched and the post removed, I have relocated the tutorial to my personal page so that the SNA and online marketing communities can continue to benefit from it.

When I attended the Marketing 2.0 Conference in Paris this March, I had the pleasure of seeing a presentation by Barak Libai, a marketing researcher from Tel Aviv University. Throughout the last decade, Libai his various researcher partners have been exploring the intersection of complex networks and marketing. His research includes topics such as innovation diffusion, word of mouth marketing, customer valuation & customer relationship management, the impact of “influence,” and the application of complex system methods in marketing research.

Last November, Libai and co-authors Eitan Muller and Renana Peres published “The Social Value of Word-of-Mouth Programs: Acceleration versus Acquisition.” Because of my experience with a company that used social network analysis to provide businesses with intelligence about their customer influencers and other aspects of their customer networks, I found the implications of this research fascinating, so I wanted to give a brief overview of the research findings and what they imply for the application of “customer network analysis”.

WOM FTW? (“Word of Mouth For the Win?”)

Word of mouth initiatives have long been a staple of the marketing diet, and in recent years so-called “influencer” targeting has become the go-to extension for the traditional WOM marketing models. Whether they’re labels as influentialsopinion leaders, or hubs, finding these influencers in a vast and complex network of customers is no small task; in light of the costs, managers often struggle to “achieve financial justification,” for this sort of marketing, though many are convinced of its benefits.

Prior to this paper, most of the research measuring the “social value” of influencers used soft metrics like message diffusion, conversation, or brand awareness; the monetary effects of WOM contagion have not been thoroughly defined in research. Libai and his partners are some of the first to attempt to answer the big question of ROI, to define the social value created by WOM in terms of customer equity. They define the impact of these WOM seeding programs based on the interaction of two factors: customer acquisition and customer acceleration (defined below).

Defining the Social Value of a Customer

In order to get the most accurate assessment of a customer’s potential WOM impact, the researchers define an individual’s “social value” based on the absence principle presented in the famous film “It’s a Wonderful Life”:

For those who might not have seen it, this classic story shows the effect on a town if one particular man had never been born (e.g., his wife is a never-married old maid). Libai et al. suggest that measuring a customer’s social value requires a similar method: measure the change in value over the entire customer network if this particular node were removed. To determine that impact, the researchers used “agent based models” (think Sims 3 for scientists) based on real-life social network structures.

So basically, based on this method, if Coca Cola’s marketing guy wants to determine how much social value I have within Coca Cola’s customer network, he’d make an “agent based model” of their customer network, then run a simulated WOM seeding program on the model, first WITH me in the network and then again WITHOUT me in the network. Then he’d determine the difference in how much money they’d make (for a given period of time) in each situation – that difference is my social value. It answers the question: how much are my connections worth to Coca Cola over the course of my time as a soda drinker?

If it isn’t already clear, this method of defining a customer’s social value has huge implications for companies trying to determine the value of their their WOM programs as well as social media marketing programs: finally, a method to determine which nodes (ahem, FOLKS) in their networks can have the biggest impact on their profitability, and just how big that impact can be.

Acquiring new customers, Accelerating adoption from would-be customers

There are two major ways that WOM campaigns can affect a product’s market share: acquisition and acceleration. Acquisition is how many new customers decide to adopt a product who would have otherwise adopted another brand or not adopted such a product at all without the WOM program. In the business world, time = money, so the sooner you a customer adopts your brand, the more time they have to spend money on your products – that’s the “acceleration” aspect. Acceleration is how much sooner people adopt a product due to the WOM program. Why does this matter? Adopting a product sooner gives a customer a higher lifetime value, so the sooner they adopt, the better! (For example, if I start buying Apple products at age 18, I am probably worth more to the company in the long run than if I start buying Apple for the first time at age 45).
The potential social value of a WOM program can then be determined in a similar manner — by comparing the “lifetime value” of the entire customer network with the program to the “lifetime value” of the network without the program (using agent-based models).

Sow your (marketing) seeds in fertile ground – but which ground is most fertile?

The WOM program type used in this study is what is commonly known as a seeding program, in which a company offers some sort of promotion (free product, discounts, service trials, etc.) to a “seed” group of people with the intention that these people will market for them via WOM. (Why? Previous studies have shown that customer loyalty is higher for customers acquired via “recommendation from a friend” than for those acquired via marketing).

One of the key questions surrounding seeding programs over the last decade has been whether or not it’s more effective for companies to seed via influencers or random people their customers networks. (This Fast Company article from 2008 gives a nice overview of the great influencer debate.) To investigate this question as well as the impact of competition on a program’s effectiveness, the researchers measured the (simulated) change in a brand’s customer equity in five different scenarios:

  • no seeding program
  • “random” seeding program without simultaneous competitor seeding program
  • “random” seeding program with simultaneous competitor seeding program
  • “influencer” seeding program without simultaneous competitor seeding program
  • “influencer” seeding program with simultaneous competitor seeding program

The agent-based models were created based on 12 real-life social graphs from a variety of sources. The size of the seeding program (i.e., what percentage of the total network was chosen as “seeds” for a product) was determined based on industry standards, varying the size from 0.5% to 5% of the potential market. The “influencers” were defined as the top 10% of network with the most connections (highest “degree”), and the set percentage of “seeders” was chosen at random from this group. Conversely, for the “random seeding” program, the “seeders” were chosen at random from the entire network. Each of the five combinations of parameters listed above was run 20 times to combat the probability of an unlikely single run. These results were then averaged for each network to draw the final conclusions.

Research Results

At the end of it all, the research showed that the influencer seeding programs yielded higher customer equity gains in both the single brand and competitive scenarios, but  a competitive seeding program greatly reduced the overall effectiveness of a seeding campaign, whether the seeds were random or influential. Still, the results of the random programs were pretty good, so the decision about which program type to adopt depends on how much the company is willing to invest in discovering their influencers. Because it often requires complex, high-demand computing, customer network analysis can be time consuming and expensive, so some companies opt to let external vendors do their CNA.

One interesting point is that in a competitive scenario (e.g., when Pepsi and Coca Cola are both running simultaneous seeding programs), when the acquisition effect of the companies’ seeding programs is in essence “canceled” by the other program, BOTH companies can still benefit from the acceleration of their product in the market – getting customers sooner gives each of those customers a higher lifetime value, and therefore increases the lifetime value of the network overall.

The research offered in six other major results which we will summarize:

  1. “The competitive program effect”: The social value of a seeding program is “considerably higher” when the firm faces a competitor than when they are the “sole player in the market.” This can be explained by the fact that in a market with no competition, everyone in the potential market will eventually adopt the available product/brand, so the gain is only from acceleration instead of a combination of acceleration and acquisition.
  2. “The brand strength effect”: “Weaker” brands have more social value to gain from a WOM seeding program than “stronger” brands.
  3. There is a threshold “seed size” for both influencer and random programs before the program actually decreases the social value of a company instead of increasing it. So more seeds does not necessarily equal a more effective program!
  4. Influencer seeding programs reach that “threshold” much more quickly than random programs, so they reach their “peak” social value potential at a much smaller seed size.
  5. While about 75% of a seeding program’s WOM potential can be achieved using a random seeder program, “targeting influentials can increase social value considerably” (p. 31).
  6. The role of acceleration in increasing social value is more prominent in influencer seeding programs than in random seeding programs.

The downside

One things that the research (self-admittedly) does not take into account is the cost of implementing these seeding programs. Each company would have to determine on a case-by-case basis what their social value gain would be less the costs of implementing that program, and “such calculations may demand network-specific analysis” for each company network and product. Many companies have only just begun to realize the benefits of representing their customer networks for the purposes of customer network analysis. It’s a process that can be both complex and costly. But there are SaaS (software as a service) companies out there who can take your existing customer names and put them into a network framework to help you identify the influencers and analyze your network to determine the ideal WOM campaigns for your purpose, product, and people.

For a nice review, check out this presentation Libai gave at WOM UK conference in April: