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[https://www.cambridge.org/core/journals/behavioural-public-policy/article/personalized-nudging/E854A04226DEA94B623ECA2ACF64C8D0/core-reader] - - public:weinreich
behavior_change, design, target_audience - 3 | id:309764 -

Nudges have been critiqued for being too blunt of a tool. For instance, a retirement savings default may be helpful for a group of employees on average, but subgroups, say under-savers or over-savers, might be helped or harmed by this one-size-fits-all approach. As such, there have been calls to develop a more personalized approach to nudging (see here in our collection: “Imagining the Next Decade of Behavioral Science”). This paper outlines two dimensions that behavioral scientists could consider when designing personalized nudges: choice personalization and delivery personalization. Think of choice personalization as “personalization within nudges”—the method of nudge has been set (say, a default) but is tailored to specific individuals (different default leves of retirement contributions, for those over-savers and under-savers). Think of delivery personalization as “personalization as across nudges”—understanding the most effective method to nudge a certain individual. Personalizing nudges does come with data privacy and legal concerns, but these can be overcome, the paper argues.

[https://www.behaviourworksaustralia.org/behaviour-change-101-series-five-steps-to-select-the-right-behaviour-to-target/?utm_source=Habit+Weekly&utm_campaign=1f1cda8506-EMAIL_CAMPAIGN_2020_02_02_02_55_COPY_01&utm_medium=email&utm_term=0_ab93d31fb5-1f1cda85] - - public:weinreich
behavior_change, design, how_to, strategy, target_audience - 5 | id:285232 -

At BehaviourWorks, we often prioritise behaviours using the Impact-Likelihood Matrix (figure below). In this approach, behaviours are prioritised by mapping them based on: The impact they have on the problem they are intended to address. The likelihood of the target audience adopting the behaviour.

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