Sometimes a simple metric isn’t enough. It can’t fully describe a behavior or performance of a system. That’s when you need a Franken-measure: a made-up metric monster that creates a comprehensive composite to capture complex concepts.
Franken-measures go by many names—indexes, scales, ratings, composite or compound measures—and show up in all sorts of places:
Sports have embraced Franken-measures to evaluate player and team performance, e.g. passer ratings, Rating Percentage Index for college basketball, and judging of Olympic events like gymnastics, ski jumping, and ice dancing.
Why would I want a Franken-measure?
You are probably already up to here with measures, so why would you want another one—much less one that is going to need extra effort and explanation? Here are a few things Franken-measures can offer:
A short-hand way to communicate about a complex concept. For example, a concept like customer loyalty may encompass everything from share-of-wallet to frequency of interactions to average sales amount.
A mechanism to operationalize a complex concept. Systems can take action on a single number more easily than an array of variables.
A definitive weighting of factors. Rather than constantly bickering about the relative importance of various measures, a Franken-measure can lock down the weighting, avoiding individual biases (in exchange for a systematic bias).
A balance of components. By combining multiple measures, variation in one measure doesn’t unduly bias the results.
What does it take to design an useful Franken-measure?
Not all Franken-measures are effective at achieving these benefits. There are at least four elements that contribute to a good design: completeness, concision, measurability, and independence. These factors can be combined into the Franken-measure Effectiveness Index (FEI) using Juice’s proprietary weighting model.
Completeness. Modeling all relevant performance factors to provide a holistic measurement of the concept.
Concision. A calculation that is as simple and straightfoward as possible, making it understandable and logical to users.
Measurability. Using direct performance data rather than relying too heavily on proxies or subjective measures. And from a practical perspective, if you can’t reliably gather valid data, the exercise is futile.
Independence. The components of the measure need to be independent so that variation in one component doesn’t directly drive another.
What can go wrong?
Finally, here are a few of the pitfalls to avoid when setting out to create your perfect Franken-measure:
Complexity. A complex calculation can confuse and infuriate your audience because it is hard to understanding what is driving performance and why the measure is moving. Leigh Steinberg, famous NFL agent, said of the NFL passer rating: “Other than one attorney in our office, I am unaware of a single human being who has the capacity to figure a quarterback rating.” The formula isn’t quite as inpenetrable as that, but it isn’t for the weak of heart:
Changing the baseline. There will be inevitable pressure to change the franken-measure formula which automatically invalidates historical performance.
In search of comprehensiveness. A desire to be comprehensive can hamstring the effort. Take Eric T. Peterson’s Engagement Model. He is clearly striving for completeness but at the risk of feasibility, in my opinion.
Black box and credibility. For the people impacted by a Franken-measure, it is important to understand what is going on under the covers. And if it is impossible to share the algorithm or approach, credibility of the creator is all that remains. PageRank succeeds to the extend that people trust that Google has an objective, well-intentioned algorithm. A whiff of agenda or bias would undermine it in the eyes of the audience. Take the National Review’s “Liberal Rankings” which have managed to label the last two Democratic Presidential nominees as the “Most Liberal Senators.” Coincidences like that can undermine credibility.
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