The following article is being shared as background information for SMAC's upcoming work to create a common vocabulary for describing, industry standard metrics for measuring, and uniform methods of buying engagement campaigns.
Substantive comments are encouraged. Please note, comments will be moderated by the author to keep the discussion on topic.
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Paper Four in an Ongoing Series
September 08
In this paper:
- A WOM communication returns $0.87 in value
- Each WOM recommendation nets a $0.38 Communication Dividend
INTRODUCTION
The goal of this paper is to define and provide a model for calculating a "Communication Dividend," or the net result of the value of each word of mouth (WOM) communication less its cost.
In previous papers addressing the value of a conversation, BzzAgent compared the cost and impact of WOM to traditional media, such as television, print, interactive and radio advertising. This paper creates a model that assesses the value of a product-related conversation in the context of the "lifetime value" of a new customer (CLV). This perspective enables marketers to best understand the impact of a conversation on the bottom line.
COMMUNICATION VALUE USING CLV
The model for this paper is consistent with other recommendation-based CLV models, including a recent study published in the Harvard Business Review1 and a featured presentation at the Advertising Research Foundation Measurement 3.0 conference.2 In both
the study and presentation, researchers calculated the value of a communication by factoring the revenue generated by each customer's purchases as well as purchases resulting from the individual's recommendations.
The chart below shows that when historical and normative data (based on BzzAgents' 500 WOM campaigns) are applied in a one-year lifetime value model, a managed WOM campaign results in a per communication value of $0.87.

While each communication is likely to generate $0.87 in revenue, what is most revealing in this analysis is the Communication Dividend (or the per communication ROI).
A WOM campaign consisting of 10,000 participants creates an average of 616,800 individual communications over two generations of consumer dialogue.3 This reach results in an
average per communication cost of $0.49. The communication dividend nets $0.38 for each person reached via WOM, or nearly $235,000.
CLV INPUTS
There are four key inputs at play in a customer lifetime value model. Naturally, the variability of these inputs will impact the outcome. First, one must look at the per unit revenue and purchase cycle. Consumer packaged goods and packaged food tend to be the most popular verticals marketed through organized WOM.4 For this reason, we applied a $3.00 per product revenue and monthly purchase cycle, both reasonable assumptions for these product categories.5
Second, we applied purchase and retention rates of 20 percent and 10 percent, respectively.6 These are based on three points of reference: internal data, widely published
media influence studies citing WOM as the number one influencer of purchase decisions, and recent post-recommendation purchase intention rates of 50 percent.7
MAKING A WOM LIFETIME VALUE WORK FOR YOUR PRODUCT
This paper provides the principle constructs of valuing a communication using a CLV approach. Of course, every vertical and each product within that vertical will have different price points, purchase cycles, conversion and retention rates that can impact the value of a conversation. Inputting the retention rates and purchase behavior associated with your products into the model creates an easy method to estimate your potential return.
So, what's your products' conversation value? How does your other media compare when viewed through a CLV lens?
Footnotes:
1. Carl, W.J., (2008): Measuring the Value of Word of Mouth: ARF Measurement 3.0: New York: Advertising Research Foundation and at https://www.chatthreads.com/corp/pdf/CV_Exec_Summary_Web.pdf (retrieved 9/11/08)
2. Kumar, V., Leone, R.P., Peterson, A. J., (2007): How Valuable is Word of Mouth? In Harvard Business Review. Cambridge: Harvard Business Press
3. Carl, W.J., McGlinn, M (2007): Measuring the Ripple: Creating the G2X Relay Rate and an Industry Standard Methodology to Measure the Spread of Word-of-Mouth Conversations and Marketing Relevant Outcomes. In Measuring Word of Mouth Volume 3. Chicago: Word of Mouth Marketing Association.
4. In 2007, 70% of BzzCampaigns were either a consumer packaged good or packaged food
5. Based on internal analysis of (1997) Household Buying Habits in Drug Store News and (2008) Times & Trends A Snapshot of Trends Shaping the CPG Industry. At http://us.infores.com/portals/0/articlePdfs/TT_March_2008_NPP_Final.pdf: Chicago: Information Resources Inc. (retrieved 9/2/08)
6. Willke, J., www.bases.com/news/Secrets%20of%20New%20Product%20Success.pdf: (retrieved 9/11/08)
7. http://www.marketingcharts.com/direct/offline-wom-more-prevalent-positive-and-credible-than-online-buzz-5144/keller-fay-wom-credibility-offline-vs-onlinejpg/ (retrieved 9/2/08)


Comments: 4
It would be interesting to run the same model for industries outside CPG. Significant differences in price, margin, and conversion rates would yield different WOM dividends. It would help identify industries where WOM (and, once our work is done here, likely social engagement) opportunities are strongest. Have you looked beyond CPG at all?
Thanks for the question. I’m Matt McGlinn, Director of Business Intelligence at BzzAgent and I have been actively involved in this series of papers.
Our goal with these papers is to create an active dialog about potential methodologies in valuing a conversation, and your question is exactly the type of response we are seeking.
This paper presents a framework for others to apply their own data to in order to better understand the relative value of conversations about their products/services. We used CPG as the input because of our experience in the sector, but the research methodology is easily adaptable to other industries and product verticals. The degree of success and the variability in the model is best tested using data and benchmarks supplied by experts in a given field. To that end, we strongly encourage others (especially those outside CPG) to input own data into the model, share their results and offer recommendations for improvement. It’s in the collaboration of SMAC members where we can adjust and improve our efforts to best measure the value of a recommendation and social media engagement.