Thanks Jennie! I agree that in this case they are very closely related. I think there was definitely an element of the business model that was purposeful, based on the competencies and connections of those who set it up. I also think the focus on innovative funding mechanisms and market development was deliberately chosen to drive sustainable vaccine market growth. But I also think your instinct may be right and Gavi may have recognized early that they would be better placed to scale across countries with different health and governance structures through a lighter touch model. Looking at other global health foundations originating in the US, the coverage is generally lower when the operating model is more hands on. For example, the Clinton Health Access Initiative has a strong footprint of country teams that work with national governments and existing health structures, but operates in 38 countries vs Gavi’s 73.
Cool organization Eric! I’m keen to understand a bit more about the influencer aggregation model. Are there any synergies/overlaps between influencers in different disciplines? Or is the benefit of aggregation all on the content user/customer side? I’m wondering whether, as well as providing different types of content for a defined customer segment (Arab women), there is an opportunity to have influencers help each other (and thus make some of your value-add crowd-sourced). Is there any overlap in what makes one influencer “authentic” and another “authentic”? Can that be a source of explicit or behind the scenes value to your contributors?
Do you see any downsides to the DBA organization decision? I see the point you make about the benefits of aligning manufacturing and research functions within a disease biology area, but does it reduce collaboration across DBAs? Does this mean that potential for expanded applications beyond the main target disease/marker are missed? Are key R&D techniques and advances communicated across teams, so that learning is shared beyond the DBA? I think the operating model makes a lot of sense but I would probably want to see particular initiatives promoting cross-DBA sharing in order to maximize company-wide learning and avoid inefficiencies.
Interesting business model and article!
The agent-focused model you describe brings up a few questions for me. If Safaricom is only dealing with the first layer of agents, who then in turn work with the subagents who work with customers (and I imagine in many cases there is even another layer or two), how does Safaricom try to control the interactions with customers? It sounds like they used to do site visits, but you mention they now operate (understandably) at arms length. M-Pesa is so linked to the Safaricom brand, which as you mention is very strong, but I imagine Safaricom has to work hard to flow training, brand messaging, key updates etc through its dispersed distribution network. They have the same challenge with their airtime distribution network, of course, which brings me to my second question – is there still little or no overlap between those two agent networks? I understand the initial concern of airtime sellers losing their business, but given the likely frequency of cash in/cash out transactions it seems you could make the case for investing in this new type of business to replace any lost airtime sales. I wonder if they have evolved into combining those two forces or if they remain focused on keeping them separate. Finally, I think the thesis of your post is that M-Pesa’s success is largely thanks to its agent model and footprint, or at least that these were necessary contributors to its ability to grow. Do you think this is why others have failed where M-Pesa succeeded? Or do you think others have understood that element but face different regulatory/environmental challenges?