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My proposal:
The attorney John Travolta played in A Civil Action went bankrupt because he opened negotiations at seven times his internal case assessment.
Had he used a data-driven approach to negotiation planning, he would have known that Boston lawyers generally start closer to 2.5 times their target, rather than the 7X he chose. With a class action / mass tort bump, he might have gone to 3.6X – half the multiplier he used in a failed attempt to anchor negotiations. The result was an immediate and very expensive impasse.
Litigators now have predictive analytics to help them plan successful concession strategies and make mid-course corrections based upon real-time projections.
Picture It Settled® uses deep data from thousands of cases to predict negotiating behavior. It doesn’t predict what a jury will do, it predicts what negotiators will do probabilistically. It’s said that there are only seven Hollywood storylines that are continually retold. Negotiating behavior too has common themes that are acted out in a variety of contexts.
Our projections look like hurricane forecasts – the darker colors at the center of the graph illustrate the most likely course. These projections were made with only two offers and are within 3.1% of the final settlement. Of course, they get more precise with additional offer data.
Armed with these projections, parties continually fine-tune their strategies by moving the target dot at the bottom. Users can also simulate individual offers by moving those dots around.
If the target is outside the projections, the odds of impasse increase. But the dotted lines are modeled off the target in a way that increases the odds of inducing cooperative or mirroring behavior.
The combination of setting targets within probabilistic outcomes and following a reverse-engineered concession plan to that target is a recipe for success.
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