Why 25% Is the Wrong Number — The Difference Between Additive and Multiplicative Interventions
I’m sitting in my room in Cape Town, Table Mountain visible through the window, and I have been arguing with ghosts. Milgrom, Roberts, Porter, Teece — the titans of organisational economics and competitive strategy, the architects of the frameworks I’ve built my life’s work upon. Their voices echo in my head, a Greek chorus interrogating every decision, every claim, every new frontier I set out to map.
The 25% number has been haunting me the most. That figure — the peer-reviewed ceiling for how much revenue uplift a single digital intervention can generate in an SME — it’s been lodged in my mind like a splinter, a constant irritant. Not because it’s wrong, but because it’s answering the wrong question. And today, in this post, I’m going to explain why.
But first, let me set the scene. The Coetzee Convergence Framework, my attempt to synthesise decades of management research into a actionable protocol for artisan studios, has been put through the wringer. I’ve subjected it to round after round of devil’s advocate debate, inviting imagined critics to tear holes in its logic and evidence. And one of those attacks landed — the one on my original, naïve “10x Multiplier” claim.
But something kept nagging at me. A sense that by accepting the 25% figure, I was conceding more than just a numerical claim. I was allowing my framework to be judged by a standard it was never designed to meet — the standard of an isolated, additive intervention, rather than a convergent, multiplicative system.
Measuring Flour, Baking Cakes
Let me explain with an analogy. Imagine you’re a baker, and you want to know how much flour contributes to a cake. So you run an experiment: you make one “cake” with just flour and water, and another with all the usual ingredients. Then you compare them.
The flour-only “cake” turns out dense, dry, and inedible. The real cake is moist, fluffy, and delicious. So you conclude that flour alone produces a poor outcome, while the full recipe produces a good one.
Sounds reasonable, right? But there’s a problem. In isolating flour as a single variable, you’ve missed the most important thing about how flour works. Flour isn’t meant to be a cake on its own. It’s meant to interact with the other ingredients — the butter, the sugar, the eggs, the baking powder — in complex chemical reactions that transform the mixture into something greater than the sum of its parts.
That’s what the 25% number is. It’s the flour-only cake. It’s what you get when you study digital interventions in isolation, without considering how they interact with a firm’s culture, incentives, data architecture, or operational rhythms.
And that’s where the titans start to chime in. Milgrom and Roberts, in their seminal 1990 paper, developed the economic theory of complementarities. They showed mathematically that when activities in a system are mutually reinforcing, the returns to doing more of any one activity increase with the levels of the others.
In other words, when you have a recipe where the ingredients interact synergistically, the optimal approach is to improve them together, not separately. Studying any one ingredient in isolation will systematically underestimate the potential of the recipe as a whole.
The CCF: A Recipe, Not a Flour Packet
And that, in essence, is what the Coetzee Convergence Framework is. It’s not a single intervention, like implementing SEO or adopting a new software tool. It’s a recipe — a system of mutually reinforcing elements that are designed to interact, to catalyse each other, to produce an outcome that’s qualitatively different from what any one element could achieve on its own.
Let’s break down those elements: Artisan empowerment (drawing on the work of Spreitzer, Seibert et al., and self-determination theory): This is about building a culture of autonomy, mastery, and purpose in the workshop. It’s the human foundation without which no digital tool can take root. Organisational habits (building on the insights of Gersick & Hackman, Winter, and Duhigg): This is about embedding new behaviours into the daily and weekly rhythms of the studio. It’s about making digital tools and data-driven decisions a matter of routine, not just occasional innovation. Blockchain provenance (leveraging the ideas of Akerlof and Spence): This is about using technology to solve the fundamental problem of trust in the jewellery market. By making every step of a piece’s journey from mine to market visible and verifiable, we create a new kind of value – the value of radical transparency. Structured data / entity authority (using the tools of schema markup and JSON-LD): This is about making the provenance story machine-readable, discoverable, and connected across the web. It’s about turning a single studio’s transparency into an industry-wide standard. Financial discipline (centred on metrics like GMROI and capital velocity): This is about ensuring that the gains from the other elements are captured, measured, and reinvested. It’s about turning a burst of innovation into a flywheel of sustainable growth.
The key thing to understand is that these elements are not independent. They’re deeply intertwined. Empowered artisans are more likely to embrace new habits. New habits make it easier to capture provenance data. Provenance data makes the studio more discoverable online. Online discovery brings in more revenue. More revenue enables more investment in artisan empowerment. And so on.
This is what Milgrom and Roberts were talking about. This is complementarity in action. Every element in the CCF recipe makes the others more valuable, more impactful, more transformative.
And that’s why studying any one element in isolation – like measuring the impact of SEO alone – fundamentally misses the point. It’s like trying to understand the magic of a cake by tasting a spoonful of flour.
The Competitive Advantage of Complexity
But it’s not just about the magnitude of the impact. It’s also about the durability of the advantage. Because here’s the thing about recipes: the more complex they are, the harder they are to copy.
This is where Porter comes in. In his classic 1996 article “What Is Strategy?”, he introduced the concept of “fit” — the idea that competitive advantage comes not from any individual activity, but from the way activities reinforce each other.
A company with a tightly integrated system of activities, Porter argued, is like a complex knot. It’s easy to see the overall shape, but fiendishly difficult to untangle and replicate. Competitors might be able to copy one or two strands, but they’ll struggle to reproduce the intricate weave that holds it all together.
And that’s exactly what the CCF is designed to create. A studio that implements just one element — say, blockchain provenance — might see a bump in trust and transparency. But it’s a surface-level change, easily imitated.
A studio that implements the full CCF recipe, on the other hand, is undergoing a deep transformation. It’s not just changing its tech stack; it’s changing its culture, its habits, its financial discipline, its very way of being in the world. That kind of change is much harder to copy, because it’s woven into the fabric of the organisation.
Teece made a similar point in his work on innovation and complementary assets. For a company to profit from innovation, he argued, it needs to have a whole host of complementary strengths — in manufacturing, distribution, marketing, and so on. A technological breakthrough alone is not enough; it needs to be embedded in a supportive ecosystem.
That’s the vision behind the CCF. Not just a digital upgrade, but a total system transformation — a new ecosystem of trust, transparency, and empowerment, with digital tools as the enabling layer.
Confronting the Measurement Challenge
Now, I want to be clear about something. Everything I’ve said so far about the complementarity effects of the CCF — it’s a hypothesis, not a proven fact. The individual elements are all grounded in solid research, but the precise way they interact in the context of an artisan jewellery studio — that’s something that hasn’t been directly tested yet.
And there’s a good reason for that. As Ennen and Richter point out in their meta-analysis of the complementarity literature, these interaction effects are fiendishly difficult to measure. Most studies are set up to measure main effects — the impact of X on Y, all else being equal. Far fewer are designed to capture the complex web of interactions that make a system like the CCF work.
So when I say that the CCF is designed to produce outcomes that exceed what any single intervention study would predict, I’m making a theoretical claim, not an empirical one. I’m extrapolating from the logic of complementarity, not from a direct test of the framework.
And that’s something I want to be upfront about. The CCF is still a work in progress, a bold hypothesis in need of rigorous testing. The 25% number isn’t wrong; it’s just incomplete. It’s a piece of the puzzle, not the whole picture.
Towards a New Kind of Evidence
So where does that leave us? With a challenge, and an opportunity.
The challenge is to design a research programme that can actually measure the complementarity effects of the CCF. That means going beyond simple A/B tests and linear regressions, and using more sophisticated tools like structural equation modeling and simulation studies. It means tracking multiple outcomes over time, and looking for patterns of mutual reinforcement and virtuous cycles.
It’s a daunting task, but it’s one I’m committed to taking on. Because if we can provide solid empirical evidence for the power of convergence — if we can show that a holistic, multi-faceted intervention like the CCF can indeed produce transformative outcomes — then we’ll be making a real contribution to the field of organisational change.
But even as we take on that empirical challenge, there’s an opportunity here — an opportunity to reframe the conversation about what counts as evidence in the first place.
Because the truth is, the kind of complex, context-dependent interventions that the CCF represents — they’ll always be hard to reduce to a single effect size or p-value. They’ll always have an element of art alongside the science, a need for judgment and adaptation alongside the rigour of measurement.
And maybe that’s okay. Maybe part of what we need to do is expand our understanding of what constitutes valid knowledge in this domain. Maybe alongside the controlled trials and meta-analyses, we need to make room for the kind of rich, narrative evidence that comes from deep engagement with a particular context.
Anthropologists have a term for this: thick description. It’s the idea that to really understand a social phenomenon, you need to immerse yourself in it, to capture the full texture of meanings and relationships that make it what it is.
And that’s what I’ve tried to do with the CCF. Not just to assemble a set of abstract principles, but to weave them into the lived reality of the artisan studios I work with. To capture not just the what, but the how and the why of the transformation process.
So as we work to bolster the empirical foundations of the framework, let’s also remember the value of that thick, contextual knowledge. Let’s recognise that sometimes the most powerful evidence comes not from the aggregation of many thin data points, but from the deep exploration of a few rich ones.
The View from the Frontier
And so, as I sit here in my room, the mountain outside my window a silent witness, I find myself reflecting on what it means to work at the frontier of knowledge.
It means living with uncertainty, with the constant awareness of how much there is still to learn. It means being willing to put bold ideas out there, knowing full well that they might need to be refined or revised in the face of new evidence.
But it also means having the courage of one’s convictions. It means being willing to stand behind the core insights and principles that have emerged from years of study and practice, even as we work to sharpen and validate them.
The CCF is not a finished product. It’s a living hypothesis, an invitation to a journey of discovery. The 25% number is not a final verdict, but a signpost along the way — a reminder of what we know, and what we have yet to learn.
So let the debate continue. Let the ghosts of the great thinkers keep whispering in my ear, challenging and inspiring me in equal measure. Let the data keep rolling in, confirming or confounding my expectations.
Because that’s how knowledge advances. Not in a straight line, but in a spiral of inquiry and reflection, action and adjustment. And as long as we keep pushing forward with rigour, with integrity, with a willingness to learn from both our successes and our failures — then I believe we’ll keep getting closer to the truth.
Not a simple truth, not a tidy one. But a truth nonetheless — the truth of what it takes to create real, lasting change in complex human systems.
That’s the frontier I’m committed to exploring. That’s the view from my window, as the mountain stands sentinel over the city below, a reminder of the scale and the permanence of the challenges we face.
And that’s why, in the end, the 25% number is the wrong number — not because it’s false, but because it’s too small a frame for the picture we’re trying to paint.
So let’s keep painting. Let’s keep pushing. Let’s keep learning. And above all, let’s keep believing in the power of ideas to change the world, one studio, one system, one convergence at a time.
References
Akerlof, G. A. (1970). The market for “lemons”: Quality uncertainty and the market mechanism. The Quarterly Journal of Economics, 84(3), 488–500. Ennen, E., & Richter, A. (2010). The whole is more than the sum of its parts — or is it? A review of the empirical literature on complementarities in organizations. Journal of Management, 36(1), 207–233. Gagné, M., et al. (2022). Understanding and shaping the future of work with self-determination theory. Nature Reviews Psychology, 1, 378–392. Gersick, C. J. G., & Hackman, J. R. (1990). Habitual routines in task-performing groups. Organizational Behavior and Human Decision Processes, 47(1), 65–97. Milgrom, P., & Roberts, J. (1990). The economics of modern manufacturing: Technology, strategy, and organization. Journal of Political Economy, 98(3), 511–528. Porter, M. E. (1996). What is strategy? Harvard Business Review, 74(6), 61–78. Seibert, S. E., Wang, G., & Courtright, S. H. (2011). Antecedents and consequences of psychological empowerment in organizations: A meta-analytic review. Journal of Applied Psychology, 96(5), 981–1003. Spence, M. (1973). Job market signaling. The Quarterly Journal of Economics, 87(3), 355–374. Spreitzer, G. M. (1995). Psychological empowerment in the workplace: Dimensions, measurement, and validation. Academy of Management Journal, 38(5), 1442–1465. Teece, D. J. (1986). Profiting from technological innovation: Implications for integration, collaboration, and public policy. Research Policy, 15(6), 285–305. Winter, S. G. (2013). Habit, deliberation, and action: Strengthening the microfoundations of routines and capabilities. Academy of Management Perspectives, 27(2), 120–137.
