Digital agriculture services provide a key opportunity to improve smallholder farmers’ access to information, markets and financial services. However, these services are hard to scale, often meeting low levels of adoption and usage. In donor-funded programmes, Monitoring Evaluation and Learning (MEL) can play a key role in exploring service adoption challenges among smallholder farmers and identifying lessons to make services work for rural, low-income populations. But MEL is often imposed by donors to track the impact of their funding, and service providers often associate it with tedious reporting and struggle to see its value. So how can we rebrand MEL and adapt its tools to effectively collaborate with private sector partners?
This question greatly influenced the MEL approach I designed for the GSMA Innovation Fund for the Digitisation of Agricultural Value Chains. This FCDO-funded innovation fund provided technical assistance and results-based grants to 6 companies developing and scaling digital agriculture services. You can read more about the project, the MEL methodology and the findings in the report Improving Farmer Livelihoods Through Digitised Agricultural Value Chains. A similar approach is now being implemented in the GIZ-funded GSMA AgriTech Accelerator, which provides Technical Assistance to 10 agritech companies scaling digital services that improve farmer livelihoods and climate resilience.
This blog provides key takeaways from designing an engaging MEL approach tailored to digital agriculture service providers.
Rebrand MEL to resonate with the private sector
Many MEL tools can serve a dual purpose, meeting reporting and learning needs while being useful for companies. When designing MEL frameworks, it is useful to reflect on the value proposition of MEL that will be communicated with private sector partners. Differentiating between internal objectives such as results verification or donor reporting, versus learning objectives for private sector partners helps to shape this MEL “brand”. Aligning MEL learning questions with the vision and objectives of service providers is key to generate a clear value proposition. For example, instead of emphasizing the role of MEL in monitoring project performance and reporting to donors, we rather framed it as technical assistance that would help them better understand their users, and that would allow them to improve and scale their services. Tools can also be renamed to resonate with the private sector, for example our Theory of Changes have become Impact Roadmaps or Project Blueprints. Finally, getting buy-in from and working with senior executives, as well as with people close to implementation such as product managers, is critical to ensure buy-in and that MEL findings will be acted on to improve business models and make services more farmer-centric.
The GSMA MEL brand communicated to private sector partners
Develop MEL tools that are also useful for private sector partners
The MEL skillset lends itself well to projects focused on testing or scaling new services, and to commercial entities seeking to raise further investments. Designing impact strategies, setting KPIs, collecting and analysing service data, and reflective management are familiar to both MEL specialists and commercial product managers. Conducting a MEL needs assessment workshops at the start of a project is a good way of identifying private sector partners’ priorities, potential synergies and how MEL tools can help them meet strategic objectives. We used the Theory of Change to map potential business plan risks for services to scale up, and to identify impact areas that align with environmental, social, and governance (ESG) investing, as companies are often asked to demonstrate the value and the impact of their service to raise funds or for sustainability reporting. Specifically, we explored overlaps between donor-facing impact KPIs we needed to collect and agriculture ESG data, such as the agriculture metrics identified by the IRIS+ benchmark tool developed by the Global Impact Investing Network. In summary, KPIs and data collection should include data points that are useful for companies in their reporting, communication activities and their efforts to raise investment.
GSMA AgriTech MEL tools, focusing on their value proposition for private sector partners
Establish feedback loops to leverage findings for product iteration
Higher outreach and activity rates are key commercial objectives for service providers and human-centric design can help address some of the challenges of smallholder service usage. It requires collecting regular feedback from users to inform changes in services or their delivery. MEL can leverage qualitative and quantitative research expertise to provide independent user journey analysis and identify smallholder pain points and opportunities for service improvements. We dedicated up to three quantitative surveys per service to gain smallholder feedback on services, which also helped us report on outputs and early outcomes KPIs such as farmer satisfaction with services and behaviour change in farming practices. Setting up regular touchpoints with private sector partners to debrief and brainstorm results from data collected by MEL helps articulate lessons and identify concrete actions to be taken as a result of user feedback. For example, one of our surveys found that farmers using digital advisory services provided by our Indonesia and Pakistan agritech partners were not aware of new farming advice added to the service or new features such as weather forecasts. We shared the findings with both agritechs and user experience specialists and discussed potential ways to address this challenge. As a result, push notifications from the mobile apps and SMS advisory were introduced to pull users to specific content.
Screenshot from an anonymised brainstorm session to identify learnings and product improvements based on MEL data
Rebranding MEL, developing tools that were useful to partners and establishing feedback loops to make sure findings were actionable greatly helped MEL’s image with private sector partners. Feedback gathered during workshops found high perceived usefulness of MEL, and data collection was seen as leading to concrete learnings. This buy-in ultimately helped with more efficient collaboration in implementing MEL activities and allowed us to improve our processes.
What is your experience of tailoring MEL to work with digital agriculture service providers?
Feel free to provide your comments and feedback below.