If you missed this article, originally published a year ago in the Deloitte Review, it’s a unique thought leadership piece well worth the read, exploring the intersection of predictive analytics and behavioral economics and the pursuit of persuasion/influence and driving behavioral change.
If we want to act on data to get fit or reduce heating bills, we need to understand not just the analytics of the data, but how we make decisions.
Near the end of Thinking, Fast and Slow, Daniel Kahneman wrote, “Whatever else it produces, an organization is a factory that produces judgments and decisions.”2 Judgments and decisions are at the core of two of the most significant intellectual trends of our time, and the one-word titles of their most successful popularizations have become their taglines. “Moneyball” is shorthand for applying data analytics to make more economically efficient decisions in business, health care, the public sector, and beyond. “Nudge” connotes the application of findings from psychology and behavioral economics to prompt people to make decisions that are consistent with their long-term goals.
Other than the connection with decisions, the two domains might seem to have little in common. After all, analytics is typically discussed in terms of computer technology, machine learning algorithms, and (of course) big data. Behavioral nudges, on the other hand, concern human psychology. What do they have in common?
When the ultimate goal is behavior change, predictive analytics and the science of behavioral nudges can serve as two parts of a greater, more effective whole.
Quite a bit, as it turns out. Business analytics and the science of behavioral nudges can each be viewed as different types of responses to the increasingly commonplace observation that people are predictably irrational. Predictive analytics is most often about providing tools that correct for mental biases, analogous to eyeglasses correcting for myopic vision. In the private sector, this enables analytically sophisticated competitors to grow profitably in the inefficient markets that exist thanks to abiding cultures of biased decision making in business. In government and health care, predictive models enable professionals to serve the public more economically and effectively.
The “behavioral insights” movement is based on a complementary idea: Rather than try to equip people to be more “rational,” we can look for opportunities to design their choice environments in ways that comport with, rather than confound, the actual psychology of decision making.3 For example, since people tend to dislike making changes, set the default option to be the one that people would choose if they had more time, information, and mental energy. (For example, save paper by setting the office printer to the default of double-sided printing. Similarly, retirement savings and organ donation programs are more effective when the default is set to “opt in” rather than “opt out.”) Since people are influenced by what others are doing, make use of peer comparisons and “social proof” (for example, asking, “Did you know that you use more energy than 90 percent of your neighbors?”). Because people tend to ignore letters written in bureaucratese and fail to complete buggy computer forms, simplify the language and user interface. And since people tend to engage in “mental accounting,” allow people to maintain separate bank accounts for food money, holiday money, and so on.4
Richard Thaler and Cass Sunstein call this type of design thinking “choice architecture.” The idea is to design forms, programs, and policies that go with, rather than against, the grain of human psychology. Doing so does not restrict choices; rather, options are arranged and presented in ways that help people make day-to-day choices that are consistent with their long-term goals. In contrast with the hard incentives of classical economics, behavioral nudges are “soft” techniques for prompting desired behavior change.
Proponents of this approach, such as the Behavioural Insights Team and ideas42, argue that behavioral nudges should be part of policymakers’ toolkits.5 This article goes further and argues that the science of behavioral nudges should be part of the toolkit of mainstream predictive analytics as well. The story of a recent political campaign illustrates the idea.
The 2012 US presidential campaign has been called “the first big data election.”6 Both the Romney and Obama campaigns employed sophisticated teams of data scientists charged with (among other things) building predictive models to optimize the efforts of volunteer campaign workers. The Obama campaign’s strategy, related in Sasha Issenberg’s book The Victory Lab, is instructive: The team’s data scientists built, and continually updated, models prioritizing voters in terms of their estimated likelihood of being persuaded to vote for Obama. The strategy was judicious: One might naively design a model to simply identify likely Obama voters. But doing so would waste resources and potentially annoy many supporters already intending to vote for Obama. At the opposite extreme, directing voters to the doors of hard-core Romney supporters would be counterproductive. The smart strategy was to identify those voters most likely to change their behavior if visited by a campaign worker.7
So far this is a “Moneyball for political campaigns” story. A predictive model can weigh more factors—and do so more consistently, accurately, and economically—than the unaided judgment of overstretched campaign workers. Executing an analytics-based strategy enabled the campaign to derive considerably more benefit from its volunteers’ time.
But the story does not end here. The Obama campaign was distinctive in combining the use of predictive analytics with outreach tactics motivated by behavioral science. Consider three examples: First, campaign workers would ask voters to fill out and sign “commitment cards” adorned with a photograph of Barack Obama. This tactic was motivated by psychological research indicating that people are more likely to follow through on actions that they have committed to. Second, volunteers would also ask people to articulate a specific plan to vote, even down to the specific time of day they would go to the polls. This tactic reflected psychological research suggesting that forming even a simple plan increases the likelihood that people will follow through. Third, campaign workers invoked social norms, informing would-be voters of their neighbors’ intentions to vote.8
The Obama campaign’s combined use of predictive models and behavioral nudge tactics suggests a general way to enhance the power of business analytics applications in a variety of domains.9 It is an inescapable fact that no model will provide benefits unless appropriately acted upon. Regardless of application, the implementation must be successful in two distinct senses: First, the model must be converted into a functioning piece of software that gathers and combines data elements and produces a useful prediction or indication with suitably short turnaround time.10 Second, end users must be trained to understand, accept, and appropriately act upon the indication.
In many cases, determining the appropriate action is, at least in principle, relatively straightforward. For example, if an analysis singles out a highly talented yet underpaid baseball player, scout him. If an actuarial model indicates that a policyholder is a risky driver, set his or her rates accordingly. If an emergency room triage model indicates a high risk of heart attack, send the patient to intensive care. But in many other situations, exemplified by the challenge of getting out the vote, a predictive model can at best point the end user in the right direction. It cannot suggest how to prompt the desired behavior change.
I call this challenge “the last-mile problem.” The suggestion is that just as data analytics brings scientific rigor to the process of estimating an unknown quantity or making a prediction, employing behavioral nudge tactics can bring scientific rigor to the (largely judgment-driven) process of deciding how to prompt behavior change in the individual identified by a model. When the ultimate goal is behavior change, predictive analytics and the science of behavioral nudges can serve as two parts of a greater, more effective whole.
Once one starts thinking along these lines, other promising applications come to mind in a variety of domains, including public sector services, behavioral health, insurance, risk management, and fraud detection.
Several US states either have implemented or intend to implement predictive models designed to help child support enforcement officers identify noncustodial parents at risk of falling behind on their child support payments.11 The goal is to enable child support officers to focus more on prevention and hopefully less on enforcement. The application is considered a success, but perhaps more could be done to achieve the ultimate goal of ensuring timely child support payments.12 The Obama campaign’s use of commitment cards suggests a similar approach here. For example, noncustodial parents identified as at-risk by a model could be encouraged to fill out commitment cards, perhaps adorned with their children’s photographs. In addition, behavioral nudge principles could be used to design financial coaching efforts.13
Several US states either have implemented or intend to implement predictive models designed to help child support enforcement officers identify noncustodial parents at risk of falling behind on their child support payments. The goal is to enable child support officers to focus more on prevention and hopefully less on enforcement.
Taking the logic a step further, the model could also be used to identify more moderate risks, perhaps not in immediate need of live visits, who might benefit from outreach letters. Various nudge tactics could be used in the design of such letters. For example the letters could address the parent by name, be written in colloquial and forthright language, and perhaps include details specific to the parent’s situation. Evidence from behavioral nudge field experiments in other applications even suggests that printing such letters on colored paper increases the likelihood that they will be read and acted upon. There is no way of knowing in advance which (if any) combination of tactics would prove effective.14 But randomized control trials (RCTs) could be used to field-test such letters on treatment and control groups.
The general logic common to the child support and many similar applications is to use models to deploy one’s limited workforce to visit and hopefully ameliorate the highest-risk cases. Nudge tactics could help the case worker most effectively prompt the desired behavior change. Other nudge tactics could be investigated as a low-cost, low-touch way of prompting some medium- to high-risk cases to “self-cure.” The two complementary approaches refine the intuitive process case workers go through each day when deciding whom to contact and with what message. Essentially the same combined predictive model/behavioral nudge strategy could similarly be explored in workplace safety inspections, patient safety, child welfare outreach, and other environments.
Similar ideas can motivate next-generation statistical fraud detection efforts. Fraud detection is among the most difficult data analytics applications because (among other reasons) it is often the case that not all instances of fraud have been flagged as such in historical databases. Furthermore, fraud itself can be an inherently ambiguous concept. For example, much automobile insurance fraud takes the form of opportunistic embellishment or exaggeration rather than premeditated schemes. Such fraud is often referred to as “soft fraud.” Fraud “suspicion score” models inevitably produce a large proportion of ambiguous indications and false-positives. Acting upon a fraud suspicion score can therefore be a subtler task than acting on, for example, child welfare or safety inspection predictive model indications.
CONTINUE READING: Access the complete article in Dupress, where it was originally published.
By: James Guszcza, dupress.com
Originally published at dupress.com
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