Project Approach
Project Outputs
Country-level Psycho-behavioral segmentation strategy
The segmentation strategy is a culmination of qualitative fieldwork to arrive at drivers of hesitancy and behavior profiles, which are then validated and sized through surveys of probability-based representative samples
Psycho-behavioral segmentation strategy:
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Kenya
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Burkina Faso
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Cote D’ivoire
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Pakistan
Country-level segment-targeted Design Blueprints & Solution Concepts
These design blueprints and concepts will articulate solutions for driving the COVID -19 vaccine uptake that can be locally adapted and implemented by governments and stakeholders. These design interventions will be co-developed with govts. IPls and stakeholders, and rapid tested with the community and frontline workers.
Segment-targeted design blueprints and concepts for localized solutions to drive vaccine uptake for:
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Kenya
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Burkina Faso
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Cote D’ivoire
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Pakistan
Guidance and Support tools
Tools will be designed to support implementers and public health authorities to comprehensively understand the segment profiles and solutions strategy, and to help them effectively adapt and deploy localized interventions to address the barriers to vaccine confidence and uptake in the dynamic COVID context
Consolidated Global Vaccine Hesitancy Output
The outputs from all focus geographies will be synthesized to build a consolidated framework of vaccine hesitancy segments and targeted solutioning strategy
Methodology
Research
Planning
1
Research
2
Design Research and Outputs
3
Dissemination and Support
4
OCT - DEC 2021
Planning & Stakeholder Engagement
Engage with various stakeholders and partners to understand the context, needs, and gaps in knowledge. Build partnerships and alignment on problem-framing and field recruitment
DEC 2021 - MAR 2022
Formative Qualitative Research
Understand the COVID-19 context, vaccine mental models and decision drivers and barriers to COVID-19 vaccine uptake through in-depth interviews and context mapping with end users
Quantitative Research & Segmentation
Measure the prevalence, variations and clustering of decision drivers and barriers to COVID-19 vaccine uptake in the population
MAR- APR 2022
Strategy & Co-creation
Facilitate collaborative workshops with key stakeholders to align on focus segments. Co-create and prioritize solution concepts that will be further developed
Qualitative Design Research
Test usability with psycho-behavioral segments and health system stakeholders to gain feedback on solution design concepts and prototypes
Design Blueprints Creation
Synthesize findings of all research phases to identify pathways to vaccine confidence and willingness for different behavioral segments of the population
MAY 2022
Implementation Support
Embed project research learnings within public health systems to ensure effective deployment of solutions
Deep dive into mixed-method quantitative segmentation: psycho-behavioral segmentation
Psycho-behavioral segmentation involves dividing people into groups based on what they do—in other words, their behaviors—and on the motivations, beliefs, and other factors influencing why they behave the way they do. The segmentation captures differences within a population that are clear, discrete (as non-overlapping as possible), relevant to the behavior of interest, and actionable, in order to create targeted interventions, and it enables researchers to track these segments over time and characterize their key drivers. Messages or interventions targeted to such segments have the best chance of success.
Psycho-behavioral market segmentation has been shown to be superior to purely demographic segmentation, yet it is largely missing from global development programs, despite calls to adopt it from public health researchers and social scientists. Most development programs have been successful in developing solutions such as new drugs or vaccines, and in delivering health services even to the most remote locations, yet they falter when faced with people who don’t access services or adopt behaviors that will improve their lives. Segmentation can help improve engagement with and uptake of development programs by aligning supply and services with latent demand.
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There are many approaches to segmentation. Our approach is a post hoc segmentation, which defines groups by using machine learning to cluster data across a multidimensional set of quantitative data that is representative of the population in question and measures variables predictive of the issue of concern. Post hoc segmentation is robust, nuanced and highly predictable of needs, wants and behaviors, but it can be costly to develop and require more sophistication to use than other forms of segmentation.
Behavioral Science
Despite the importance laid by the World Health Organization on the role of vaccinations, not just for COVID-19 but for prevention and control of other infectious disease outbreaks, emphasis has been placed primarily on improving the supply side. There has been less focus on demand management, and such work has largely been related to economic incentives to spur vaccine uptake. But individual decision-making and actions in relation to vaccine uptake are more complex than commonly appreciated. Overcoming this gap requires identifying and targeting the underlying differential barriers and behavioral drivers of hesitancy within different people. For example, some people may be concerned about safety and side-effects, while others resist getting vaccinated due to low perceived risk of COVID-19, and yet others because of mistrust of experts and institutions.
Formative qualitative research aims to decode the COVID-19 context and identify the conscious and non-conscious drivers of hesitancy or aversion towards COVID-19 vaccines, and the barriers to vaccine confidence and uptake. It does this across a diverse sample that takes into account demographics (age, gender, income, location), vulnerability (health risk, information access, socio-economics, historical, and inclusion), and vaccine attitudes (COVID-19 confidence, hesitancy).
Some of the behavioral factors already studied which appear to be critical in understanding vaccine hesitancy are:
Trust in institutions like government, healthcare systems, hospitals and healthcare workers and their ability to respond to the pandemic
Concerns around safety and effectiveness of the proposed vaccines
Perceived risk or threat from COVID-19
Trade-off between perceived risks and benefits from getting the vaccine
Perceived social norms
Information sources, and quantity and quality of the information itself
Final Mile uses our proprietary research methodology and sense-making frameworks to decode the decision-making process and unearth strategic levers for influencing vaccine uptake. The behavioral barriers, drivers and dynamics identified are used to measure and analyze the most relevant data in quantitative surveys.
Human Centered Design Approach
Human centered design (HCD) is a problem-solving approach that Final Mile uses throughout our project work. We bring mindsets of collaboration, curiosity, empathy and experimentation as we move from a place of understanding to co-creating and testing design concepts and interventions. By focusing on understanding our target users, their decision-making process, the stakeholders involved and the systems they are connected to, we develop a richer understanding that allows us to engage with end users and stakeholders to co-design impactful and sustainable interventions targeting the problem area.
We leverage HCD throughout the project as we seek to build more collaborative moderator onboarding, an empathetic research methodology, co-design of interventions and user-focused testing of design concepts. We value lived experience as much as subject-matter expertise and seek to involve both throughout the process.
Our HCD approach continues to evolve, and we see ourselves as facilitators of the HCD process, which can lead to design with others, rather than design for others. In collaborating with end users, our intention is to co-create interventions that are contextually appropriate, sustainable, and impactful.