Crunch the Numbers: Using Big Data to Achieve Better Verdicts

Too often, lawyers use guesswork to make decisions in cases worth tens of millions of dollars. And while gut feelings can be powerful, what if you could make decisions based on hard data instead? That’s exactly what John Campbell is helping lawyers do every day.

John has come up with an innovative method of using scientific survey techniques to predict outcomes of jury trials. His studies can tell you whether you’re likely to win or lose a case, how much compensation you’re likely to get from a jury, and even how you should structure your case to get the best verdict. John developed this technique during his tenure as a professor at the University of Denver Sturm College of Law, and to date, he’s helped lawyers recover nearly $2 billion in trial.

This week, John walks us through how his system works and the amazing results they’ve seen. Learn how your firm can start making better-informed decisions in trial, from jury selection to your closing argument.

Key takeaways

Big data can help you get better verdicts. John’s system has proven to be incredibly accurate at predicting what a jury will award in a case. Not only can you make sure you’re not asking so much that the jury will vote against you, but you can make sure you’re not leaving any money on the table.

The earlier you start studying the case, the more options you have. If you call John in two weeks before trial, he can perform a study, but you’ll be limited in the changes you can make to get the best result. If you call him in early, he can help you shape your case to get the best possible outcome. 

Creating empathy with the jury is key. Using techniques like “the man in the black suit” can help jurors start to put themselves in the plaintiff’s shoes. This may make them more likely to return a larger settlement for your client.

To contact John Campbell, visit his website (below) or text him at (314) 249-2500.


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Previous Guests: Bob Simon, Gary Sarner, Jen Gore-Cuthbert, Muhammad Ramadan, Amanda Baggett, Sara Williams, Joe Fried, Bibi Fell, Sahm Manouchehri, Sevy Fisher, Taly Goody, Teresa Diep, Dan Ambrose, Rick Ferri, Glen Lerner, and many others

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