A New Metric to Measure Word of Mouth Marketing

[tweetmeme source=”pricingright” single_only=”false”] Effect of  Word of Mouth (WoM) marketing is intuitively accepted by most and a few peer reviewed research in the journals of Marketing Science and Marketing Research found evidence supporting the predictability (somewhat weak however).  There are two key shortcomings I see with many of the popular Word of Mouth Marketing metrics:

  1. One that is most acknowledged in any marketing research study is the gap between customer’s stated attitude and their actual behavior. When the customer answers the question on paper (or screen) they are asked to answer about their expected behavior but the answer is still rooted in the attitude. For example, when you ask a customer about likelihood of purchase, paying for services or recommend the product/service to others the customer states their current attitude and not what they will do when presented with the exact situation in the real world.
  2. Two is trying to compute a net metric based on the stated attitude level. They result is data loss (because some responses are thrown away) and the need for extremely large number of samples to get statistically significant results. According to research published in Marketing Research, (Summer2007,Vol. 19 Issue 2) up to 26 times more samples are needed for net metrics.

I propose here a newer and actionable metric – iTRM  (iterative Total Recommendation Metric). This addresses both the shortcomings listed above. First, it gets closer to actual customer behavior when it comes to recommendations than any of the metrics. Second there is no data loss, all responses are counted.  Here is how the metric is calculated.

Customers are specifically asked about their actions, how many times they will recommend. The scale is -5 to 5. Negative values mean customers will be “de-recommending” your products. iTRM offers multiple advantages over existing metrics:

  1. The time horizon is specific and  limited, 1 month. The time limit however does not imply that the measurement must be repeated every month but if the metric is as a predictor of sales it requires monthly measurements.
  2. The customers do not have to make subjective judgments about rating scales. For example a 8 for one could be 6 for another in many ordinal scales measuring likelihoods. Here it is the exact number of actual recommendations.
  3. This is simple to administer and measure. It can be either a simple sum or can be continuously improved to be a weighted sum that is different for different industries and even for specific brands based on past data. (The word iterative stands for both my brand and the iterative process by which the metric evolves).
  4. The limit of 5 is not a shortcoming given the specific action and the time horizon. If for a particular brand we find the need for higher scale, it can easily “iteratively” improved upon and still be consistent with past results.
  5. It is easy to validate its predictability and improve on the weighted calculation.

Overall, I believe iTRM provides a better measure of WoM with potential to use and easily validate as a predictor of growth.

How do you measure your WoM?

There is Nothing New in Marketing Except Catch Phrases

Marketing is about segmentation and targeting there is nothing more to it. Segmentation is recognizing that different people buy different products for different reasons and  finding those reasons, occasions, usage scenarios and hence what the customer is willing to pay for. Targeting is delivering versions that meets those reasons and customer’s willingness to pay. I started a new series of articles on what I called “Fidelity Trap“. As I said in the introductory post, this is  concept I based off of the concept of Fidelity, Convenience and trade-off  as used by author Kevin Maney in his book Trade-Off: Why Some Things Catch On, and Others Don’t and on the concept of congruence and traps used in a different context by  professor and author Henry Chesbrough (I did one course with him while at Haas School of Business).

If you take Maney’s book, the core of it is not new or groundbreaking – somethings catch on and others fail when the marketer fails to fully understand their segments and/or fail to target them with the right versions. By definition, products fail in the market place because of marketing failure. What Maney’s framework of  Fidelity and Convenience does is to  frame and group together different reasons, occasions and usage scenarios of the customers.

What about the Traps meme ? It is just another framework or my hope for getting a catchphrase. Traps result when a firm’s marketing strategy is inwardly focused, ignoring market evolution, or due to differences in customer’s stated preferences and behaviors.

In the Trade-off continuum, it is oversimplification to say Fidelity and Convenience are the only two factors involved in customer decision making. It should also be noted that customer utilities from these factors are functions of other variables independent variables. For the analytically inclined there are methods and tools like conjoint analysis that allow the marketer to find how much customers value the different features (be it a fidelity feature or a convenience feature), all other factors  and how to define versions and at what prices.

There is absolutely nothing new in marketing – only restatements, new mental shortcuts and of course catchphrases.

 

Implying Causation – Predictive Analytics Slippery Slope

Imagine, if you will, a child eating broccoli for the very first time. While eating broccoli, let us say the child sneezes a few times in succession and then proudly declares, “I think I am allergic to broccoli”. As a parent or simply as a grown-up it is not difficult for you to see the fallacy in child’s case. One does need an advanced degree in econometrics or statistics to  reply back, “eat your broccoli – correlation does not imply causation”.  Consider the following real cases:

  1. From The Times Economix Blog:
  2. There’s a very strong positive correlation between income and test scores. (For the math geeks out there, the R2 for each test average/income range chart is about 0.95.)

  3. From The WSJ opinion column:
  4. Study after study reveals that there are long-term career benefits to working as a teenager and that these benefits go well beyond the pay that these youths receive. A study by researchers at Stanford found that those who do not work as teenagers have lower long-term wages and employability even after 10 years.

  5. From WSJ half-page Ads targeting parents
  6. Students who read The Journal are 76% more likely to have a GPA of 36% or higher

  7. From a research paper on subscription to library resources by universities
  8. Working with Dr. Carol Tenopir of the University of Tennessee and consultant Judy Luther of Information Strategies, this single-case study demonstrates a $4.38 grant income for each $1.00 invested by the university in the library (ROI Value). The white paper External link University Investments in Information: What’s the Return? is posted on Library Connect. The results articulate the relationship between the value of research information and its impact on the funding of an institute.

  9. From a research paper from the London School of Economics
  10. In terms of percentage growth, a 7 point increase in word of mouth advocacy (net-promoter score)
    correlated with a 1% increase in growth (1 point increase = .147% more growth). The measurement was done through telephone survey in 2005 and the revenue growth numbers are for 2003-2004.

Can you spot the fallacies in these claims?  Are these seemingly erudite and well researched claims any different from the claims of a smart child that wants to avoid broccoli? Why do we want to see correlation when none exist or take correlation for causation? Why do we suspend our critical thinking when the results are presented by big brands, big universities and packed with tonnes of data and graphs?

Of all these cases I listed above, the last one is the winner. Suppose in the chronology of events,  event-2 follows event-1 in time. It is pardonable and a ubiquitous mistake when someone says event-1 might have caused event-2. This is the garden variety correlation causation confusion. But this example I quote says, “event-2 caused event-1”.

I do not know a word for this!