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Human life is astonishingly complex. A lot of data being collected these days is attempting to capture an element of human behaviour, and most models being built are trying to predict some aspect of human behaviour. But what if we were able to, not only predict, but actually influence human behaviour? This all becomes possible at the point where predictive analytics and behavioural science intersect.
Nudge theory argues that positive reinforcement and indirect suggestions can be more effective than direct instruction, legislation and enforcement.
“This is me, planting an idea in your mind. I say, ‘don’t think about elephants’. What are you thinking about?”
Here are some examples of behavioural nudges:
At the Intersection of Predictive Analytics & Behavioural Science
Predictive analytics helps predict an outcome, while behavioural science helps influence the outcome.
Predictive analytics can produce some valuable insights, but it is often unclear what to do about these insights. Behavioural science enhances predictive modelling as it allows you to operationalise your insights, convert insight into action, and bring your analytics to life.
Nudging New Mexico – A Real-Life Example
In the US, more than 1/8 of all unemployment benefits are paid to people who are not eligible for benefits due to dishonest claims. The government of the state of New Mexico engaged Deloitte to help. Can claimants be “nudged” to be more honest?
In New Mexico, the process of claiming unemployment benefits is all done online. Initially there is an online application process where claimants report that they have lost their job. From that point on, claimants log on weekly to report the progress of their job search. If certain criteria are met, the unemployment benefits are automatically deposited into the claimant’s bank account.
The first step was to identify the key areas in the application process where inaccurate information is usually given. These were:
Now that those areas are identified, what do we do about it? Traditionally, the government would throw resources at the problem to investigate would-be fraudsters. But what if we used predictive analytics to identify potential fraudsters and then tried “nudging” them, in real time, to do the right thing.
Here’s how it worked in practice:
I lost my job and I log on to the website to complete the initial application process. I am asked whether I was fired or laid off. The truth is I was fired, but I think it was extremely unfair! So I choose, ‘laid off’. At this point, the predictive models flag me as high probability of fraud. Immediately, a pop up appears on my screen showing a letter addressed to my previous boss asking for verification that I was laid off, rather than terminated. This proved to be quite an effective nudge.
I’ve been unemployed for a few weeks now and I log on weekly to report my activity. This week, when asked about my weekly earnings, I think to myself, ‘Well, I did a few shifts at McDonalds but I earnt nothing, just pocket money. Plus I’m sure everyone else is working part time and not reporting it.’ I choose ‘No income’. A pop up appears on my screen, which I have not seen before on any previous week, saying, “99 out of 100 people in <your county> report their earnings accurately. If you worked last week, please ensure you report these earnings”. This proved to be one of the most persuasive nudges.
For the weekly work search requirements, claimants were asked to commit to a detailed work search plan for the coming week – which channel will they use to search for jobs? How many phone calls will they make? How many jobs will they apply for? It turns out that if people commit to a detailed plan, they are more likely to follow through with it.
As a result of these and other nudges, claimants were half as likely to commit fraud, twice more likely to report new earnings accurately and 20% more likely to find work in the next few months. And this project saved the government of New Mexico millions.
Imagine what the application of analytics, coupled with behavioural science, can do for your organisation.
Basem is a Director in Deloitte’s Business Algorithms team with a focus on enabling businesses to use analytics to realise the power of data. Prior to that he worked in London as an independent risk analytics consultant to some of the largest European, Asian and Australian financial institutions as well as holding a management position with Deloitte UK. His experience spans analytics, general insurance, counterparty credit risk modelling, derivatives pricing and futures trading. Outside of work, Basem enjoys playing, watching, and talking about football (soccer).