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 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.