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Optimizing mobile tech for farmers
Introduction
Microsoft, in partnership with the Alliance for a Green Revolution in Africa (AGRA), developed AgriBot, a mobile-based platform that leverages technology to provide localized, real-time agricultural information and advisory services to smallholder farmers, helping to improve their productivity and livelihoods.
The challenge
We set out to understand farmers' usage of the platform, identify their unmet needs, and generate insights and potential solutions to increase farmer engagement and retention.
Our approach
Barclays collaborated with Marathon to design the experience for the Absa flagship branch.
To identify opportunities, we developed an end-to-end experiential branch cross-section to understand all users and activities at every internal touchpoint. Leveraging customer statistics from nearby branches, we simulated daily traffic from future customers across different segments to the new branch.
Qualitative Research
We conducted qualitative in-depth interviews and observations across three countries. This research phase aimed to refine insights and align design opportunities for optimizing content and knowledge sharing for farmers.
Archetype Development
We developed behavioral archetypes rooted in users' aspirations and challenges, illuminating new user segments often overlooked. This approach enabled targeted design solutions with significant potential impact, guided by the insights gleaned from the interview phase of our research.
Customer Journey Mapping
Visual representations were created to outline farmer interactions and experiences with the AgriBot platform across touchpoints, identifying critical engagement moments, pain points, and improvement opportunities. These insights guided targeted enhancements to improve usability, efficiency, and overall satisfaction.
Qualitative Research
We conducted qualitative in-depth interviews and observations across three countries. This research phase aimed to refine insights and align design opportunities for optimizing content and knowledge sharing for farmers.
Archetype Development
We developed behavioral archetypes rooted in users' aspirations and challenges, illuminating new user segments often overlooked. This approach enabled targeted design solutions with significant potential impact, guided by the insights gleaned from the interview phase of our research.
Customer Journey Mapping
Visual representations were created to outline farmer interactions and experiences with the AgriBot platform across touchpoints, identifying critical engagement moments, pain points, and improvement opportunities. These insights guided targeted enhancements to improve usability, efficiency, and overall satisfaction.
The outputs
Through our rigorous research efforts spanning three countries, we delved deep into smallholder farmers' desires, challenges, and experiences with the AgriBot platform.
We provided a three-part report containing:
1.
Research Findings, Insights, and Recommendations
The research revealed the critical agricultural needs of smallholder farmers. These insights led to actionable concepts to ensure the AgriBot platform delivers a consistent, reliable, and high-quality user experience.
Recommendations included technical enhancements, service improvements, VBA training programs, and personalized agronomic tips, ensuring the platform remains dependable and user-friendly, ultimately driving farmer engagement and retention.
Recommendations included technical enhancements, service improvements, VBA training programs, and personalized agronomic tips, ensuring the platform remains dependable and user-friendly, ultimately driving farmer engagement and retention.
2.
Customer Journey Maps
These detailed visual representations provide a comprehensive view of how farmers interact with the AgriBot platform at various touchpoints. They highlight specific pain points encountered during their user journey and identify key areas where improvements can be made to streamline their experience.
3.
Archetypes
Developed through a thorough analysis of qualitative data, these personas represent distinct groups of farmers based on their behaviors, preferences, and specific needs. They serve as a foundational tool for designing targeted solutions that cater to the diverse user segments identified during the research phase.
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