Psycho-Behavioral Segmentation

Psycho-Behavioral Segmentation

Developing a safe and efficacious vaccine against COVID-19 in record time has been a success, but swift and comprehensive rollout of the vaccine has been marred by a number of challenges.

On the demand side, one key challenge is vaccine hesitancy and resistance. Low confidence in COVID-19 vaccines, and low willingness to receive them, is a significant and growing risk to beating the pandemic globally.

An Africa CDC survey in 15 countries in 2021 found that although 79% of the population overall expressed their willingness to get vaccinated, vaccine hesitancy in individual countries ranged from 4% to 38%. In Pakistan, a national survey (Arshad et al., 2021) found that only 48.2% of respondents would agree to receive the vaccine upon its availability.

Overcoming this risk requires identifying and targeting the range of barriers and behavioral drivers that underlie vaccine hesitancy within different populations.

Uncovering the specific attitudes and mental models affecting vaccine hesitancy in different segments of the population requires integrating several research methods, including applied behavioral science, psychometric surveys and machine learning data-clustering algorithms. These can be used to identify and profile psycho-behavioral segments of people based on their differential barriers and drivers of vaccine hesitancy.

Given the varied and complex nature of the factors driving attitudes, beliefs and decisions, solutions must be specific to segments and localized to each country’s context, with a particular need to focus on vulnerable populations.

Need for Segmentation

Norm in Private Sector

Segmentation involves clustering individuals by shared characteristics.

First developed in private sector for three key purposes:

Prioritization –
niche, ease of engagement

Customization –
higher engagement, better subjective experiences and market expansion

Targeting –
efficiency in distribution and supply

Activating Demand

Latent Demand

In contrast to the traditional demand generation approach which is heavy touch, a behavioral psychology-driven approach posits that demand isn’t ‘generated’, but exists in the form of needs, preferences and tendencies of individuals, which can be converted into actual demand, given the right context and cues. We call this latent demand. Latent demand is often non-conscious and inexplicit, therefore it has to be inferred and decoded.

Self-Selected Attention

When the design of products, services and communications is aligned to the behavioral drivers and latent demand of the target population, they are intrinsically driven to engage with the product/service, without any external pushes or influences. This outcome is known as ‘self-selected attention’. Diversity and variability of latent demand necessitates multiple, differentiated solutions for self-selected attention.

Unique Benefits in Development Sector

When used in activating latent demand for global health and development programs, many of the benefits of this approach in private sector, like higher engagement and efficiency, translate well to the sector. Others, like prioritization, do not align with the needs of the sector. Beyond its purpose in private sector, segmentation provides some unique benefits in the development sector.

More inclusive and equitable outcomes due to customization for diverse needs and preferences

Reduced externalities and collateral impact of strategies that may work for some but not for others

Reduced resource and time requirements due to a light touch approach

Managing complexity and scale of behavior change by identifying smaller, meaningful clusters

Universalization is only possible with a differentiated approach that serves even the hardest to reach

Stability, Scalability and Predictive Value

Typical Knowledge-Attitude-Preference (KAP) Surveys

Based on self-reports of individuals’ attitudes, beliefs, preferences and intentions
Self-reports don’t capture non-conscious tendencies, therefore insufficient for latent demand
Attitudes and preferences are unstable and context dependent, therefore not predictive of real-world behavior
Gives us the current preferences but not the strategies to change them

Psycho-Behavioral Survey

Deconstruct decision-making, intent formation and preference construction
Capture the components and processes
Build a psycho-behavioral model to not only understand current preferences, but also predict preferences in other contexts
- Future scenarios
- Other geographies
- Response to programmatic interventions

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.

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.

Intended Beneficiaries

This approach will benefit people who are at significant risk of remaining unvaccinated, with a focus on vulnerable populations. We consider multiple dimensions of vulnerability, which can vary across geographies.

Initial beneficiaries include:

Populations whose vaccine perception, mental models, barriers and decision-making are not well understood because they are difficult to engage with, due to their remoteness and lack of access to media, the internet and smartphones

Populations at high risk of diseases, hospitalization, complications and fatality (e.g. persons with co-morbidities, the elderly, and the immune-compromised)

Populations whose vaccination status may have a disproportionate impact on others (e.g. frontline workers, healthcare providers, essential workers, sole breadwinners)

Populations that have been historically disadvantaged and marginalized (e.g. women, minorities, tribes and the urban poor).

A key objective of this approach is to identify population segments that are more likely to be hesitant or averse towards vaccination, but who also have the potential to have their beliefs and behaviors moved towards vaccine confidence and uptake.

Identify

Project Objectives

Identify the conscious and non-conscious drivers of hesitancy or aversion towards vaccines and barriers to vaccine confidence and uptake.

Assess

Strategize on effective levers of behavior change for each segment to boost confidence in and willingness to receive vaccines.

Strategize

Strategize on effective levers of behavior change for each segment to boost confidence in and willingness to receive vaccines.

Co-develop

Co-develop segment-targeted solution concepts with governments, implementing partners and local stakeholders supporting vaccine demand and uptake.

Guide

Build guidance materials and tools to support implementers and public health authorities to:

Understand and use segment profiles and their solutions strategies
Effectively adapt and deploy localized interventions to address the barriers to vaccine confidence and uptake in the dynamic contexts

Intended Outcomes

Increased confidence in safety, efficacy and relevance of vaccines

Build trust in credible sources of information about vaccines and trust in vaccine providers. Develop an understanding of the benefits of vaccination as well as the risks involved for self and others to create an ownership of vaccine decisions. Cultivate social norms supportive of vaccine uptake.

Increased intent to receive vaccines

Support a willingness to receive vaccines upon availability in the overall population, particularly among marginalized, vulnerable and vaccine hesitant populations.

Ability and
action to access vaccines

Address informational, logistical,
social and cost barriers to accessing vaccines and provide cues to drive action.