By James Guszcza, Deloitte Consulting LLP <\/p>\n
What do Moneyballapplying data analytics to make more economically efficient decisionsand nudge using principles from psychology and behavioral economics to promote decisions that are consistent with peoples long-term goalshave in common? Quite a bit, as it turns out. <\/p>\n
Business analytics and the science of behavioral nudges are different types of responses to the observation that people are predictably irrational. Predictive analytics aims to guide people toward rational behavior by using data to correct for mental biases. Behavioral techniques aim to nudge people toward certain actions by designing choice environments in ways that go with, rather than against, the grain of human psychology. <\/p>\n
The science of behavioral nudges should find a place in the toolkit of mainstream predictive analytics. Predictive models, however strongly backed by analytics, can only point the end user in the right directionand no model can deliver the benefits it is designed to deliver unless appropriately acted upon. It is at this last mile stage that most programs meet with the greatest resistance, and behavioral nudges can play a part in solving this problem. <\/p>\n<\/p>\n
An example from the 2012 US presidential campaign illustrates the power of programs powered by predictive analytics and designed with human behavioral tendencies in mind. Though both Romneys and Obamas campaigns were propelled to a large extent by big data and analytics, Obamas stood apart for its combined use of predictive analytics and behavioral nudge tactics. In an example of the latter, campaign workers would ask voters to fill out and sign commitment cards with a photograph of Barack Obamaa tactic informed by research indicating that people are more likely to follow through on actions they have committed to. <\/p>\n
Push the worst, nudge the rest <\/p>\n
When the goal is behavior change, predictive analytics and the science of behavioral nudges can serve as two parts of a greater, more effective whole. For example, predictive models could be used to identify noncustodial parents at risk of falling behind on their child support payments. These high-risk parents could then be targeted with nudge tactics aimed at keeping them current with payments (such as filling out commitment cards and designing outreach letters using devices such as addressing the parent by name and using colloquial and forthright language). Similar ideas can inform next-generation fraud detection insurance claims, especially to combat what is usually referred to as soft fraudpractices such as opportunistic embellishment or exaggeration rather than premeditated schemes. Behavioral nudge tactics offer a soft touch approach that is well suited to the ambiguous nature of much fraud detection work. For instance, judiciously worded letters that remind the claimant of the companys fraud detection policies could have a sentinel effect. <\/p>\n
In 3D: Data meets digital meets design <\/p>\n
Just as behavioral science can help overcome the last-mile problem of predictive analytics, data science can assist with the last-mile problem of behavioral economicsto bridge the gap between peoples long-term intentions and their everyday actions. In certain contexts, useful nudges can take the form of digitally delivered, analytically constructed data products. <\/p>\n
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Read more here: By James Guszcza, Deloitte Consulting LLP What do Moneyballapplying data analytics to make more economically efficient decisionsand nudge using principles from psychology and behavioral economics to promote decisions that are consistent with peoples long-term goalshave in common? Quite a bit, as it turns out Continue reading
\nThe Last-Mile Problem: How Data Science And Behavioral Science Can Work Together<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"