Mondelēz International’s cocoa sustainability program Cocoa Life works with Ipsos as an M&E partner. We collaborate on collecting the data and insights we need to understand how we grow opportunities for cocoa growing communities. We’d like to share the following tips with anyone seeking to measure the impact of their programs:

  1. Only measure what you have to: It may seem sensible to capture as much detail as possible, but this is a sure-fire way to add complexity to your evaluation. Overwhelming analysts with too much data could mean you have little useable information at all.
  2. Key indicators are not learning tools: High level indicators of success are great for communicating impact, but less good for understanding how and why an intervention works, or doesn’t work. Consider what you’re using your data for before you begin measurement, then make sure you collect the right information to meet your aims.
  3. Document and double check everything: It sounds minor, but good due diligence in data collection is essential. Make sure you check and record everything, from conversion factors to units of measurement. Without these, your data could be useless, and at worst lead to very costly mistakes.
  4. See it, then solve it: Data collection and analysis can be problematic. Everyone participating in an intervention and the collection of data gathered on those interventions needs to be educated and equipped to be able to identify and correct mistakes.
  5. Align with external standards: Aligning methodologies, tools and data analysis with common standards is a good idea. It can reduce the burden of planning and documenting, while enhancing the acceptance and usability of information to outside organizations.
  6. Talk about it. Too often, data is seen as a competitive asset, but it loses its value without being shared and discussed. And it’s not enough just to publish it. Data must be communicated in an engaging way. For Cocoa Life, this means adapting how we report our findings, taking time to develop novel ways for our audiences to experience our data. It’s important from a motivational perspective too. There’s little more frustrating for a researcher than doing work that nobody knows about.

This final point plays into the purpose of this article. We hope that we can encourage you to start writing and sharing about your M&E learnings with others. If you’re interested in more advice around M&E, you can read our short report here.