The Science (and Practice) Of Using Behavioural Insights
The Science (andPractice) of UsingBehavioural Insights@dilipsoman
Structure of remarks Claims and observations Psychological considerations Plumbing problem Using Behavioural Insights
Everyone is in the Business of ChangingBehaviour COMPLIANCESWITCHING /PURCHASINGCONSUMPTIONACCELERATION
Econs!
Humans!
Design for Humans, not for Econs
The Four – “ition”sMotivationCognitionPerceptionEmotion
ContextProcrastinationInertia
Three segments in Behaviour ntenders“Done”“No way”Yes, I’ll do it tomorrow”Source: Soman and Ly (2018), The Growing Market for Self-Control
The BehaviourChange PuzzleSource: Soman (2015), The Last Mile
BehaviourChange asa PlumbingProblemSource: Soman (2015), The Last Mile
The Roles of BIBehavioural Scientist as Problem Solver(last mile challenges)Behavioural Scientist as Auditor(proactive problem identification)Behavioural Scientist as Designer(policy and program design)Behavioural Scientist as Strategist(overall organizational operations)Source: Feng at al. (2018)
The Three Legged Stool for SuccessEXPERTISEBehavioural science (and understanding of human psychology)& empathy. Behavioural engineering (choice architecture andthe approaches Data & empirical science (analytics,experimental design, and ability to analyze and interpretexperiments).AGILITYAn organizational structure and processes that allow for quickfeedback loops to be incorporated, and for the organization to beable to change course so that a test-learn-adapt strategy can beput in place.EMPIRICISMA mindset that data (rather than theory or a pre-committedcourse of action) drive decision making in the organization. Thismindset calls for an empathic mindset at the intersection of BI anddesign thinking, and a relentless desire to test using experimentsthrough RCT’s or other methodologies.
The Costs ofExperimentation
How Do I Reduce the Costs ofExperimentation?Investing in hard and soft infrastructure Online experimental platforms that reduce time to collect data, Participant pools that recruit from the appropriatepopulations, Laboratory space and facilities to collect behavioural data, Researchers with expertise in experimentalmethodsAppropriate problem selection Decomposing large challenges into precise behaviours, Building horizontally scalable behaviour change problems,Studying outcomes with short outcome cycles / feedback loopsBuilding whatworks databases Allows organizations to narrow down possible interventions to testEthics in experimentation Ensuring that interventions a) are consistent with users values and interest, b) are for legitimate ends, c) do not violaterights, d) are transparent and e) do not take things away without consentReframing failure and Incentivizing Experimentation Realizing and clearly communicating that a null-result is a learning opportunity will minimize the fear of failure. Explicitlyrewarding experimentation will further build the test-learn-adapt culture
Beyond frameworksSource: BIAS project, MDRCSource: BIT Process frameworks versus prescriptiveframeworks Descriptive versus prescriptive frameworks Work well: Baseline, training, marketing Pitfalls: Misapplication, glossing over thedetails of the “pipeline,” misallocation ofresources, using framework as a checklistSource: Soman (2017), from BIAS Annual Report
In Conclusion Holistic approach to behaviour change Behaviour change as a plumbingproblem Going beyond the last mile Reducing the costs of experimentation Beware of the Elegant Simplicity (andPitfalls) of Simple Frameworks
Resourceswww.rotman.utoronto.ca/bear@UofT BEAR @dilipsoman @BE101x Soman, D. (2015). The Last Mile: Creating Social and Economic Value from Behavioural Insights. Toronto:University of Toronto Press. Feng, Bing, Jima Oyunsuren, Mykyta Tymko, Melanie Kim and Dilip Soman (2018), How Should OrganizationsBest Embed and Harness Behavioural Insights? A Playbook, Toronto, Canada: Behavioural Economics in Actionat Rotman (BEAR) Report series, available at http://www.rotman.utoronto.ca/bear Behavioural Economics in Action (BE101x) MOOC: n-action All photographic images have been used under the creative commons licensing arrangement. Illustrations are BEAR. Details on sources are available on request.
Behavioural science (and understanding of human psychology) & empathy. Behavioural engineering (choice architecture and the approaches Data & empirical science (analytics, experimental design, and ability to analyze and interpret experiments). An organizational structure and processes that allow for quick
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