With the dawning of the Age of Big Data, increasingly, we’re seeing the rise of the data-driven match. Yesterday, I came across a primitive example of this concept (primitive because it’s based on user-input rather than analytics); a new matchmaking site, Same Plate.com where users can select dates based on food preferences. Likewise, some colleges now permit incoming students to choose their own roommates based on a series of questions.
The next iteration of big data will be more complex of course, with matches based on past patterns gleaned from user experience – which carries implications and potential opportunities for lawyers. One of my favorite futurists, Stephanie Kimbro recently described how Law Pivot (just acquired by Rocket Lawyer), oxymoronically billed as “crowdsourcing legal advice,” created an algorithm to analyze user trends on its site to match the most qualified lawyer to respond to a user’s legal question. Similarly, I’m certain that many of the “lawyer match sites” have their eyes on the promise of big data in the future, and with each transaction are gathering data in the hopes of selling for a pretty penny more refined, closely tailored leads to lawyers. And instead of coming up with ways to market to our potential ideal clients, we could let the data do it for us.
Sounds like Nirvana, doesn’t it? Yet, I’m skeptical. For starters, just how personal will we allow these data aggregators, er, lawyer matching sites, to get with client information? Harvesting data about clients’ problems is a point that Stephanie presciently addresses in her new book, Consumer Law Revolution (free chapter available here) and it’s a concern I’ve both written and ranted about.
But potential confidentiality issues aside (as there may be ways to sufficiently protect information), are data driven matches all they’re cracked up to be? Many times, we expand our horizons by working with those who don’t immediately seem to be a perfect match on paper. I remember back in the first year of law school, my writing professor randomly teamed us up with other classmates to brief and argue a case. I dreaded the assignment, having been paired with a guy who was in my large section who seemed haughty and aloof. Yet we worked well together (turned out our different personalities meshed well and we shared the same healthy skepticism about the legal profession) and he became a lifelong friend.
When it comes to clients, data and self-selection helps to some degree. As I’ve written, lawyers often get the clients they market for. In other words, if you label yourself as a “bargain basement lawyer,” don’t be surprised when most of your clients complain about your rates or haggle over price. If you bill yourself as a pit bull lawyer who doesn’t take no for an answer, you may attract strident clients who won’t take no for an answer when you try to explain why a particular strategy won’t work. Still, an enormous space lies between the ideal client and clients from hell — and we may find a way to effectively represent many of the clients in that middle space even though a computer program wouldn’t necessarily have matched us. After all, as lawyers, that’s what we do: find a way to work with people — from opposing counsel to judges who don’t necessarily share our life views, personality or skills — so that we can effectively represent our clients.
What draws certain clients to certain lawyers? What makes some relationships work and others falter? Do clients really know what they want? Sure, on a survey, today’s clients say they want lawyers who return phone calls, who are responsive, who can turn around a case in 24 hours and don’t charge much to boot. But if you were to asks client, after the fact, why they loved their lawyers, I suspect the answer would be far different; it would be because they found someone who stood by them when no one would, who worked doggedly even when there wasn’t any hope, who was honest, blunt and told them exactly what to expect even if they didn’t want to hear it. I fear that in the rush to exploit big data, we may wind up matching clients to lawyers who checked all the right boxes, instead of to the lawyers right for them. Data-driven matches, after all, are only as good as the questions we ask, so long as we know how.