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NOTE: This blog is actually a position paper on brand equity metrics and modeling, critiquing the MASB metric and offering something better as an alternative.

Ad Age reported on Monday that MASB (Marketing Accountability Standards Board) has found the magic brand metric…actually, it’s an old metric where respondents are asked to choose which brand they would want if they were to win a lottery. Changes in the percent choice supposedly match changes in market share.

But if that’s all, that isn’t enough to make marketing better.

The MASB “magic question” is actually an over-simplified version of a much better question, the constant sum question where respondents are asked to allocate 10 chips across brands in their consideration set. This question was the cornerstone of a brand equity tool the team I led created in the 1990s, called BrandBuildertm (now owned by IPSOS) and applied to hundreds of brands while I was Chief Research Officer at The NPD group.

In some cases, we conducted BrandBuilder studies among IRI home scanning panelists and were able to prove the remarkable accuracy of the metric AT AN INDIVIDUAL LEVEL…and this is KEY in a digital age…predicting the actual recorded share of requirements (= probability of purchase) for each individual respondent. Furthermore, I had created a unique calibration method that was presented at ART forums that made the respondent level correlations even stronger.

Now why is the BrandBuilder constant sum approach more likely to make marketing better than the over-simplified “pick one” method?

There are four big reasons constant sum is better:

  1. Leads to better understanding of how shoppers decide
  2. Leads to contemporary precision targeting
  3. Leads to better modeling of attribute drivers
  4. …resulting in a more predictive brand equity metric that combines attitudes and behaviors

How shoppers decide

Shoppers have consideration sets that are ordered.  You might have a favorite brand on top of the list but over 50% of purchase decisions at the brand level in CPG are finalized at the point of purchase so clearly, shoppers often do NOT go into the store with a single choice in mind.  In a digital age, even for complex products like smart phones or financial services, brand consideration comes and goes as shoppers do their research online.  Finally, the pick one method just fails the logic test…if I could win one auto brand in a lottery I might pick a Bentley but I would never buy one in real life…I just can’t see myself actually spending $100K+ on a car.

Contemporary precision targeting

Numerous studies show that the consumers who are most likely to respond to your advertising and offers are those who are “switchable”…their probability of buying your brand vs. another is between 10-75%…not 0% and not 100%  Constant sum, when modeled up to scale via lookalike modeling and placed in your DMP, will allow you to target accordingly.  This targeting ability already exists via Datalogix, IRI proscores, and Nielsen Catalina; so why take a step back via over-simplification? If you use a pick one method, you get 0-s and 1-s but who are the switchables?  They could be in either group!  If your brand has a probability of purchase of say, 33% with a given consumer, you could be either.  If that consumer has a more preferred brand, your brand was not picked and you are a 0.  If 33% is the highest because there are a lot of 10 and 20% brands in the consideration set, you are a 1. The pick one method fails to make media targeting better in a digital, programmatic age.

A better model of attribute drivers

I have always been able to build very strong models, using chips as the dependent variable, of which attributes drive loyalty to one brand vs. another so any BrandBuilder study offered strong positioning guidance.  Recently, I was NOT able to build a good model against a “pick one” metric.  There was just too much loss of information.

A more predictive brand equity metric

If you cross tab predicted loyalty from attribute ratings by stated loyalty from constant sum, you get a 3X3 tic-tac-toe matrix.  The “upper right” box are the “real loyals”…behavior confirmed by perceptual differentiation.  In a JAR paper I co-authored in 1996 with Al Baldinger, “Brand Loyalty, The Link Between Attitudes and Behaviors” we proved that real loyals had a much higher probability of being retained one year later.

Your share of real loyals is the best darn brand equity metric because it: predicts future share, it operates at an individual level (and I believe can be modeled up to scale in a digital age), and integrates attitudes and behaviors so it helps marketers manage both the hard and soft parts of marketing…sales today, brand building for tomorrow.

Is there a possibility to simplify the constant sum question and still calculate real loyals?  Yes…I imagine that ranking brands in a consideration set might work nearly as well and still reflects shopper heuristics in an intelligent way. But pick one? Sorry that fails the Einstein test via his famous quote…”Everything should be made as simple as possible, but no simpler”.

(Note: for a copy of the JAR  article or to hear more about the advanced calibration method I created, please contact me at



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