A recent Forbes article describes the alternative fee problem, how to reasonably price legal fees using something other than an hourly basis, as a potential application of big data analysis.
Conveniently, we said this over three years ago in describing the data needed to create alternative fee structures.
Some corporate clients have enough legal liability of their own that they can do the analysis internally: an auto manufacturer’s finance arm can address auto loan defaults, for example, as can almost any company with a large volume of litigation, such as an insurance company. There’s likely to be enough data that reasonable connections could be drawn between various attributes of each case (amount at stake, jurisdiction, age of account, and similar items, many of which are related to the underwriting process in the first instance) and the ultimate legal fees and result (productivity: comparing input and output).
But for many corporate clients, they are only infrequent participants in litigation, and thus they turn to outside law firms on the cost question for the same reason they turn to those firms on the legal specialty question: volume of work for an outside lawyer is almost always higher than for a comparable in-house lawyer. I worked on more IPOs in two years as a corporate associate than most GCs would work on in a lifetime at a company (most companies only go public once!).
So law firms have the possibility of interpreting their data to find the answers, but there are a few major hurdles.
First, their incentives are not clearly aligned with doing the work to overcome the other hurdles. When all firms have the same problems, no one sees a pressing need to move first and invest in figuring out what they know.
Second, lawyers don’t like to be pinned down — it’s inherent in the hourly fee model: the risk of there being more work falls on the client. Since it’s the client’s lawsuit, lawyers see that as fair, and it pretty much is when the amount of work can be dictated by someone else, such as the other litigant!
Third, virtually all firms don’t collect the sort of data that might be useful to divine the cost drivers of litigation. (Insurance claims adjuster files might, but those companies can already do their own analyses.) With the need for either an upfront investment in collecting the absent data or in adopting a new process going forward, firms find it easier to use the excuse of “no data” to stick with the “I’ll give you an estimate that is really just a guess based on my own anecdotal approach.” Anecdotal evidence isn’t — and firms recognize that because they’re unwilling to adopt their own estimates created this way.
Fourth, law firms aren’t really sure yet about the risk transfer inherent in an alternative fee structure. The connection, in terms of ultimate outcome, between facts, law (which while knowable is seldom exactly known early in any given case), and lawyer skill, is fuzzy. Law firms have historically benefited from that fuzziness because excuses abound from the underlying facts (“you were always going to lose on these facts”) to the law (“there are just too many unfavorable cases”) to the process (“with this judge and this jury, it turned out to be a loss; with a different judge and jury, …”) to the client (“by trying to be cost-effective, we didn’t have enough resources available”). The rationale of “the other lawyers were smarter than us” is non-existent. But that’s what performance fees are about: betting on yourself and trying to manage the other cost factors.
We’re always looking for legal services opportunities, for corporations and law firms, to apply our approach to this alternative fee problem.