Part 12 of 14 in How Social Impact Startups Build on Success: A Positive Deviance Guide for Indian Founders← Part 11Part 13 →
This chapter provides case design templates for Indian startups working in education, health and nutrition, agriculture, livelihoods, financial inclusion, and women entrepreneurship. It helps teams identify people who succeed under similar conditions, study their practices, and support others in applying them.
- Education startups can study teachers, parents, and students linked to stronger learning or attendance outcomes.
- Health and nutrition startups can identify positive deviant mothers, ASHAs, and adolescent girls using service and nutrition data.
- Agriculture and livelihoods startups can compare farmers and SHG leaders with better yields, soil health, or repayment.
- Financial inclusion startups can study women entrepreneurs and SHGs with stronger business survival or savings consistency.
- Teams should adapt the problem, data sources, and practice process while keeping the same positive deviance logic.
Education Startups
Problem examples: low learning gains in specific subjects, high absenteeism among girls after Class 5, low use of digital content by teachers. Data sources: U-DISE, your app telemetry, school attendance registers. Positive deviant candidates: teachers whose students show higher learning gains with the same content and class size; parents who ensure consistent attendance despite poverty; students who complete modules at high rates. Practice process: teacher circles where positive deviant teachers demonstrate classroom techniques; parent peer groups. AI use: outlier detection on learning data; generation of discussion guides in local languages.
Health and Nutrition Startups
Problem examples: low exclusive breastfeeding, low IFA compliance among adolescents, high stillbirth in high-risk pregnancies, poor complementary feeding. Data sources: HMIS, ICDS registers, your field app. Positive deviant candidates: mothers with well-nourished children in the same community; ASHAs with higher service utilisation; adolescent girls who regularly take IFA. Practice process: hearth-style sessions or ASHA peer groups. AI use: statistical identification of positive deviant mothers controlling for household assets; clustering of practices from interview transcripts.
Agriculture and Livelihoods Startups
Problem examples: low yield under rainfed conditions, high input cost with low return, high default among first-time borrowers, low adoption of recommended practices. Data sources: your advisory app, satellite indices, SHG digital records, market prices. Positive deviant candidates: farmers with better soil health or yield under same rainfall; SHG leaders with better repayment. Practice process: farmer field days led by positive deviant farmers; SHG practice meetings. AI use: satellite and yield data for outlier detection; generation of local-language practice checklists.
Financial Inclusion and Women Entrepreneurship
Problem examples: low business survival among first-time women borrowers, low savings consistency. Data sources: loan and repayment records, your platform data. Positive deviant candidates: women entrepreneurs who succeed with the same loan size and market conditions; SHGs with consistently high repayment. Practice process: peer exchanges where successful entrepreneurs share communication and negotiation practices. AI use: clustering of success factors from qualitative data; personalised practice prompts.
Use these templates as starting points. Adapt the problem definition, data sources and practice process to your specific community and product. The logic remains the same: find who already succeeds, discover how, and create the conditions for others to practise.
Frequently asked questions
What are positive deviant candidates in education startups?
They may include teachers whose students show higher learning gains, parents who ensure consistent attendance despite poverty, and students who complete modules at high rates.
Which data sources can health and nutrition startups use?
Possible sources include HMIS, ICDS registers, and the startup's field app.
How can agriculture startups find successful practices?
They can use advisory app data, satellite indices, SHG digital records, and market prices to identify farmers or SHG leaders with better outcomes.
How should startups adapt these case design templates?
They should adapt the problem definition, data sources, and practice process to their specific community and product. The core logic is to find who succeeds, discover how, and create conditions for others to practise.