3. Ask yourself: How do emotions play into your data game plan?
Don't rely solely on emotions to make decisions: Emotional decision-making is human – but it can perpetuate poor results. For example, Billy Beane realized that emotions – like loyalty, admiration, or nostalgia – caused managers to make poor choices about the team's line up and roster. By collecting and analyzing data – such as player performance stats – with AI and machine learning, you can spot patterns to evaluate a player's strengths and weaknesses without the cloud of emotion or bias. Now you can base decisions on the numbers as well as experience, rather than emotions alone.
Embrace emotions as data: On the other hand, collecting data on emotions can be one of the best ways to make accurate, useful predictions about customer preferences. This is especially true for companies selling products that consumers buy based on personal taste.
But how do you quantify an emotion? You try and find a scientific way to understand why, for example, one wine makes a person happy or nostalgic versus a sip from another that spurs unease or even disgust. In wine, it's all about finding connections between the physical reactions (taste and smell) that a person has and the words they use to describe how that wine makes them feel.
Other industries can do the same – find ways to quantify customer emotions by listening closely. Then, you can use those insights to better meet their needs on a deeper, more emotional and, arguably, more permanent level.
After spending time with Pam and Billy, I'm now eager to see how other industries – even the most venerable ones – can leverage and experiment with these learnings using digital technologies. With the rise of prediction and proliferation of AI and analytics, the possibilities for unexpected, game-changing innovation are truly endless.