Case Study

Using Social Proof to Decrease Discretionary Spending

In 2016, more than half of Americans said they were actively looking for ways to cut back on weekly spending. Yet, federally collected spending data shows that on average, individuals spent 2.4% more in 2016 compared to 2015.

To help people spend less, we tested if social proof could be used to effectively drive lower spending. This an extension of our some of our social proof studies from 2016.

We partnered with Arizona Federal Credit Union (AZFCU), an innovative, 125,000 member credit union, and NCR’s Digital InsightTM banking solutions for credit unions and banks. Together we designed a social proof intervention that would help AZFCU members reduce their expenses.

Behavioral Diagnosis and Key Insights

We began our diagnosis by understanding where people see the biggest gap between others’ spending compared to their own and where they feel they can make a change. We conducted a survey on Amazon Mechanical Turk and then compared those results to what others actually spent using federally collected spending data by the Bureau of Labor Statistics.

This deep data analysis allowed us to get four meaningful insights about spending:

1.People believe others spend more than they do. Research has shown that the “Lake Wobegon” effect, a well- documented tendency to overestimate our own abilities and qualities, is strongest for behaviors that are easy, common and controllable like spending. As such, we found that people thought others spent more than they did in all 11 spending categories.

2.People are surrounded by spending. When asked to think about the spending of others, most people immediately conjured up images of packed restaurants and lines for the newest iPhone. Research shows that we have a tendency to use this ease of recall to estimate an event’s importance or frequency. Our study showed that the relative gap between what people thought others spent compared to what they spent was highest for highly visible, public expenditures such as transportation, eating out and recreation.

3.People believe they can easily curb their eating out expenditures. Respondents rated eating-out spending and fees as being the easiest to change, while housing and transportation were rated as the hardest to change.

4.People have a very hard time accurately estimating what they spend. Most of our respondents estimated values for their own spending that were rounded down to the nearest 10 and well-below what federally collected data would suggest based on their income and household size.

Ratio of how much respondents think others are spending vs. what respondents are spending on each category


To combat people’s overconfidence about their own spending, we employed social proof. Social proof is the idea that when people are unsure of the correct way to behave, they will often observe and follow the behavior of others. Eating out spending was a category that people felt they could change. It’s also a category we know people regret spending on, which makes it a good candidate for testing social proof.

The intervention we designed worked as follows. First, we identified 11,000 members of the credit union who had some eating-out spending in the previous three months and who used FinanceWorks (a personal financial management tool that would let us track their actual spending).

Eight thousand of those members were offered the chance to estimate their monthly eating-out spending via an email and the remaining 3,000 were not sent the email and kept as a control.

Those that choose to participate via the email were then shown how their spending compared to people like them (people with similar incomes and household size in their geographic region).

To accomplish this, we collaborated with Plaid to leverage anonymized, aggregated data insights. Plaid empowers consumers to securely connect their financial data with third- party applications. They power some of the top financial tools and services available today, including Clarity Money, Digit and EarnUp.

On that same page, after seeing what other people spend, participants were asked if they wanted to increase, decrease, or keep their spending the same.

Finally, they were also asked how much they planned to spend next month and what cost-cutting behaviors they wanted to commit to.

For the subsample of AZFCU’s population that was using FinanceWorks, we were able to track their real-life transactions and see how their eating out spending changed following the intervention.

Sample screen of social proof intervention


We launched our experiment to Arizona Federal Credit Union members on June 2017. Among the 8,000 members who received the intervention email (our full treatment group), 401 members started the intervention. Before we analyzed spending behaviors between the groups, we identified a few key insights:

  1. People are interested in others’ spending. Our email had a 22% click-through rate. This was 4 times higher click rate than past emails from the credit union.
  2. People did think others were spending more than them. People estimated others were spending around 18% more than they were spending on eating
  3. People don't want to see bad news. Roughly 35% of the people who were told they were spending the same or less than others dropped out of our In contrast, 58% of people who were told they spend more than others dropped out of our experiment.

So did people reduce their spending? The results suggest that people who went completely through the treatment were more likely to reduce their spending in the following week.

In fact, they spent about $17 less in the following week than the control group. However, this only holds if they both committed to spending less next month and specified an amount.

They reduced their spending over the following month by $21, but this was only marginally significant (p=.06).

Change in eating out expenditures by conditions

Our results do have limitations. First, people who received a social proof message that showed they were spending within +$100 of the norm or more than +$500 were almost 12 times more likely to drop out of our study. Since our intervention was only effective for those who completed the full experience, we are unable to disentangle whether our results were due to the selection bias or our actual intervention. Future studies will work to understand this.


In total, 270 users received social proof feedback. Roughly 58% of these users spent less the following week on eating out, 22% higher than in the control group. They also saved a total of $2,224 over the previous week.

If rolled out to AZFCU’s full population (in a way where everyone completes our full intervention), we’d expect to see an additional 13,276 people spend less the following week and a total reduction of over $2 million in eating-out spending across all members in the following week.

We are in the process of running the same intervention with a broader network of credit unions.

In our next iterations, we will focus on getting more people to complete the full intervention and helping people turn their success cutting expenses during their first week into a longer term and more durable change.