Analytics

Melissa and I had lunch with a long time client recently during which he brought up a “new” thing he hears from his clients. That is, give us the “analytics” on the case. Analytics is a concept that gets lots of airplay in various contexts. The business world has long focused on numbers to indicate performance on various measures. These are analytics. Polls, like political polls, can also be counted as analytics of success. Marketing research on a variety of products and services can indicate likely success and profit (though not always; anyone remember “New Coke”?) We have written 2 prior blogs on “Benchmarks for Lawyers,” (referenced as “part 1 or part 2″). What I understood from the client with whom we were lunching is that the clients were asking him for probability of case outcome – based on his experience. Combining this with what the likely trial verdict would, supposedly, create some measure of a good settlement. (Something like, the plaintiff wants $2 million; if we have a 50% chance of winning a defense verdict, then the case is worth $1 million.) I found it shocking that the client (insurance adjuster) would be so naive as to think this “experience based probability” could be considered analytics. I know there are jury verdict reports that provide some information wherein a certain type of injury has resulted in certain reported (and that is a critical factor) verdicts. But, that cannot tell you, nor can an attorney’s past experience, what will happen with “this case,” “at this time,” in “this venue.” Benchmarks for Lawyers, part 1, addressed the use of mock jury research to create a “benchmark” or “analytic” by which to judge “this case in this venue at this time.” There is no other reasonable analytic to answer those questions. And, one of the main reasons there is not is that there are no control groups in litigation. One can never compute the outcome which would have been achieved by a different lawyer, with a different jury or judge. The only thing that is possible is to test the variables at hand, in as unbiased and scientific a fashion as possible, to come up with an evaluation of likely outcomes. Attorneys are sometimes put in a very difficult position by unsophisticated clients who do not understand that litigation outcomes can’t be forecast, even as well as the weather. Mock jury research provides considerable direction both to outcomes and ways to influence them, however. That analytic is not cost free, but it is extremely valuable in managing expectations, risk, and litigation.

It is interesting to me that what is old to me is new to other people. And, it is also interesting that words have different meanings to different people. I have been analyzing scientific data since 1977. Analytics are, therefore, what I do; it is what I did in college and grad. school and what I have done my entire professional life. As a social scientist, analytics involve the application of mathematical and statistical principles to the interpretation of data. Analytics never, ever, involve a “gut feeling,” an educated guess about the way something, for example, a trial, will turn out based on past experience, or a prediction of a specific outcome that is based on a generalization. I was surprised to hear my long time client say some insurance adjusters believe analytics involve nothing more than his opinion about the possibility their case will turn out this way or that way. My client, who happens to be one of the most intelligent among all of the clients with whom I have ever worked, appeared as surprised by this new trend among his clients as I was when hearing about it. He knows the meaning of analytics is far different than “guesstimating” the outcome of litigation. It was slightly humorous to me to hear that some people are using the word “analytics” as if it is a new discovery or trend in litigation. Although many things have changed in the decades I have been scientifically analyzing data, the proper ways of doing so have remained the same. In my career as a jury/trial consultant, analytics require strong adherence to the scientific principles about which I was trained. There is nothing new, trendy, or faddish about doing things the right way.

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