Analytics Tools Could Be the Key to Effective Message-Driven Nudging

Using Internet of Things ecosystems, cities can provide residents with real-time information so that they may make better-informed decisions.

by / March 2017

Appealing to the nuances of the human mind has been a feature of effective governance for as long as governance has existed, appearing prominently in the prescriptions of every great political theorist from Plato to Machiavelli. The most recent and informed iteration of this practice is nudging: leveraging insights about how humans think from behavioral science to create initiatives that encourage desirable behaviors.

Public officials nudge in many ways. Some seek to modify people’s behavior by changing the environments in which they make decisions, for instance moving vegetables to the front of a grocery store to promote healthy eating. Others try to make desirable behaviors easier, like streamlining a city website to make it simpler to sign up for a service. Still others use prompts like email reminders of a deadline to receive a free checkup to nudge people to act wisely by providing useful information.

Thus far, examples of the third type of nudging — direct messaging that prompts behavior — have been decidedly low tech. Typical initiatives have included sending behaviorally informed letters to residents who have not complied with a city code or mailing out postcard reminders to renew license plates. Governments have been attracted to these initiatives for their low cost and proven effectiveness. 

While these low-tech nudges should certainly continue, cities’ recent adoption of tools that can mine and analyze data instantaneously has the potential to greatly increase the scope and effectiveness of message-driven nudging.

For one, using Internet of Things (IoT) ecosystems, cities can provide residents with real-time information so that they may make better-informed decisions. For example, cities could connect traffic sensors to messaging systems and send subscribers text messages at times of high congestion, encouraging them to take public transportation. This real-time information, paired with other nudges, could increase transit use, easing traffic and bettering the environment.

Delivering data from IoT sensors may also help appeal to people’s tendency to value immediate payoffs at the expense of more important long-term value. Providing real-time data may encourage residents to take immediate action to combat problems that often seem distant or abstract like air pollution. Distributing data from air-quality sensors via public monitors or messaging systems may inspire people to take action at times when air quality is poor by showing them that the problem is immediate. If residents see that the air they are presently breathing is unhealthy, the need for environmental protection becomes more salient; citizens understand that the problem is no longer in the abstract future, but rather poses a present danger to themselves and their families.

Instantaneous data-mining tools may also prove useful for nudging citizens in real time, at the moments they are most likely to partake in detrimental behavior. Tools like machine learning can analyze users’ behavior and determine if they are likely to make a suboptimal choice, like leaving the website for a city service without enrolling. Using clickstream data, the site could determine if a user is likely to leave and deliver a nudge, for example sending a message explaining that most residents enroll in the service. This strategy provides another layer of nudging, catching residents who may have been influenced by an initial nudge — like a reminder to sign up for a service or streamlined website — but may need an extra prod to follow through.

All of these approaches may not only raise important public policy issues, but also create opportunities for change. However, integrating these technologies into nudges comes with one significant barrier: cost. Deploying IoT and machine-learning capabilities is more expensive than traditional nudges, but many cities can repurpose systems that are already in place. Moreover, incorporating these tools can produce tremendous value, ameliorating infrastructural, environmental and compliance problems that cost local governments millions. In many cases, cities may find that the value of pairing data science tools with behavioral science far outweighs the cost. 

Chris Bousquet, a research assistant/writer at the Ash Center for Democratic Governance and Innovation at the Harvard Kennedy School, contributed to this column.
 

Stephen Goldsmith

Stephen Goldsmith is the Daniel Paul Professor of the Practice of Government and the Director of the Innovations in American Government Program at Harvard's Kennedy School of Government. He previously served as Deputy Mayor of New York and Mayor of Indianapolis, where he earned a reputation as one of the country's leaders in public-private partnerships, competition and privatization. Stephen was also the chief domestic policy advisor to the George W. Bush campaign in 2000, the Chair of the Corporation for National and Community Service, and the district attorney for Marion County, Indiana from 1979 to 1990. He has written The Power of Social Innovation; Governing by Network: the New Shape of the Public Sector; Putting Faith in Neighborhoods: Making Cities Work through Grassroots Citizenship; The Twenty-First Century City: Resurrecting Urban America, and The Responsive City: Engaging Communities through Data-Smart Governance.