This post is part of a learning series exploring good practice, examples, or new applications of MSD.
Time for some good old fashioned myth busting.
One of the misperceptions we see more often than not in MSD programmes is the idea that ‘expand’ should happen automatically. The “if I show it, they will come” approach to expanding benefits for your target group. All the expanding eggs in a demonstration basket, so to speak. Prior research confirmed the limitations of the demo effect in practice, and yet this myth persists. Our experience is quite the contrary: assume the demo effect won’t work and be pleasantly surprised if it does. Scale doesn’t just happen, you need to plan for it, manage it, and measure it.
But how? Scale up is a primary objective of MSD, but also one of the toughest challenges. All programmes struggle with it. We think part of the reason it continues to be a challenge is that programmes sit back and wait for the invisible hands of the market to scale things or they follow an assembly line approach to scale, copying and pasting what they did with a first partner over and over, irrespective of this second mover’s incentives and capacities to do anything different. Better practice is simply back to basics – following the same due process as you did with a pilot. Analysis for scale, designing feasible interventions at scale, and measuring at scale.
Analysis for scale
During initial market analysis, you would have been focused on what functions were underperforming and why. When moving to scale up said innovation, your analysis will take a different focus. For example, you might want to understand which functions could replace your role, to improve scale and sustainability. The Market Development Facility in Pakistan initially piloted a business model for locally produced silage that delivered impressive productivity and income benefits for dairy farmers, but to scale uptake, MDF needed to analyse the machinery and machinery finance functions since it cost-shared equipment in the pilot to test the innovation but did not wish scale up its financial support to other actors wishing to purchase machinery.
Or you might need to understand if there is a different actor who could spread the innovation. During a pilot in the Cambodian sanitation system, WaterSHED found that in areas with active local government staff promoting sanitation, more households purchased toilets from enterprises with which WaterSHED had worked to introduce an innovation. This led to a secondary innovation: fee-based leadership training for government officials. To scale and sustain this, however, WaterSHED needed to find somebody else to do it, rather than run hundreds of training sessions itself.
Designing feasible interventions at scale
If it worked at pilot, then surely, surely it’s feasible to do more of it at scale, right? I feel a bit like Captain Obvious suggesting not to scale up something that isn’t sustainable, but I’m afraid it needs saying. Sometimes pilots seem to work because a programme is doing a lot of the heavy lifting, or the partner is so unrepresentative of the wider sector that scaling up simply isn’t feasible. For example, a vertically integrated aquaculture company in Sri Lanka successfully improved its supply chain management but was such an outlier in the sector, that it was not feasible to replicate an innovation based on vertical integration. Early analysis can identify such considerations, such as the nature of the market structure and how many potential partners there might be should an innovation work. One way programmes can try and quantify this is to use Everett’s Diffusion of Innovation curve not just to project uptake amongst target beneficiaries, but also amongst market actors.
Having a toolbox of tactics is important at any stage of an MSD initiative, but perhaps even more so at scale. Firstly, because different stages of innovation will have different needs. You may be moving from stimulating innovators to encouraging early adopters and the early majority; so, you not only have to find ways to replace what you provided during the pilot with other resources as you scale up – you may also find that early and late majority actors have different needs. You will likely need to use different tactics.
Beyond first and subsequent movers, though, is the potential to shift into an entirely different function to replace what a programme was doing. Going back to the MDF example – although its pilot strategy included cost-sharing small bale silage equipment to test a business model and its impact on farmers’ incomes – its scale-up strategy was not simply “more co-finance for machinery.” Rather, it brokered a relationship between an equipment supplier and a finance provider and supported industry events to enable silage producers, equipment suppliers, and financial service providers to communicate the opportunity, know how, and benefits of small-scale silage production. Likewise, in a different system, far far away, the WaterSHED programme expanded by supporting the national government to roll out a fee-based leadership training programme to equip local leaders to promote public health messages. Different functions, different partners, different tactics.
Measurement at scale
We still need to measure at scale, but, in practice, there are important differences. At pilot phase, frequent monitoring is needed along your entire theory of change to see if a partner does adopt and adapt an innovation and if that translates to a benefit for the target group. When you “go big” so to speak, you can usually get away with less frequent monitoring. You know an innovation works, so monitoring is more about validating, quantifying and identifying obstacles to widespread uptake. Rather than tight and frequent partner-focused monitoring, scale monitoring may include a larger sample size and less frequent assessment.
Good measurement at scale is not bound to a specific tool or research method. A blend of formal and informal research methods will be required: target group surveys, field observations, and key informant interviews can tell you quite a lot about what is happening at scale.
Getting to scale is difficult. There are not simple hacks, answers or tools that can scale your work for you. But there are good practices than can increase the likelihood of an innovation scaling. We know the strategy of “hope to scale” more often than not leads one to fail. So why not go back to basics and use the same process you did to facilitate a successful innovation – analysis, targeted partnerships, and ongoing measurement – instead?