As Schumpeter(1) noted over 70 years ago, macro-economic thinking provides little practical guidance for companies when they are tackling their real-world challenges, and micro-economic theory has not advanced very much beyond theoretical concepts supported by little empirical data.
The challenge then is to formulate a robust approach based on a strong empirical database, which can help companies navigate ‘messy’ real world commercial environments.
Developing a ‘meso-economic’ approach which sits between the macro and micro layers is not easy: we first need to identify and define the key variables that matter; then we need to understand and define the ‘dimensions’ used to assess the importance of these key variables, which can change significantly depending on the chosen variable. Tackling this properly requires a large data set covering a wide a range of conditions so that we can generalise our conclusions. As a consequence, this problem has only been addressed by a few ‘evolutionary’ economists(2).
The development of our Triple Chasm meso-economic approach depended on access to a large global dataset covering companies across different market spaces: the data covered their development over time, as the relative importance of the different variables changed. This time-series data allowed us to build a dynamic picture of how companies grow.
The analytical approach adopted consisted of the following steps:
Identify and understand the (more than 50) variables relevant to business growth (these covered a wide range of variables, including, for example, customer types and numbers, investment, products, and sales data)
Define relative impact scores for each variable: this approach was critical because the different variables are normally measured using different dimensions; for example currencies for investment, numbers for customers, and choices for technologies (this approach to handling heterogeneous data worked because we could assess relative importance or impact)
Collect data for all these variables, covering a wide range of geographies, market spaces, products, and technologies, with the goal of achieving a data set capable of providing statistically significant results for relative impact
‘Cluster’ this large number of variables into ‘groups’ based on applying clustering theory (using bottom-up agglomerative methods coupled with top-down divisive K-means clustering based on alignment with previous strategic insights)
‘Tune’ this approach to achieve a reasonable degree of coherence between the Vectors.
Our decade long research with more than 3,000 global companies resulted in the identification of 12 meso-economic groups of drivers(3), which we defined as Vectors (based on the generally accepted definition for a Vector ‘as a quantity with more than one element’). In the language typically used by economists, these could be grouped into exogenous (external), endogenous (internal) and composite (trade-off) Vectors.
The 12 vectors can be summarised as follows:
Market Spaces, including market-space-centric value chains
Proposition Framing, Competition and Regulation, which also covers partners and suppliers
Customer Definition, which covers the different types of customers
Distribution, Marketing & Sales, covering different aspects of go-to-market
Contingent Technology Deployment, which looks at conditional approaches to technology deployment
Intellectual Property Management, covering a wide range of IP, not just patents
Product & Service Synthesis, which looks at product shape
Manufacturing & Deployment, which covers the way products are made and/or deployed in the market
Human Capital, including talent, teams, leadership, and culture
Financial Capital, which covers different types of funding and investment
Commercialisation Strategy, which articulates the relative priorities of the internal and external vectors
Business Models, which covers how the company makes money
Taken in aggregate, these 12 vectors provide, for the first time, a powerful and data driven way of profiling the overall shape of a company for a given level of maturity; and this profiling can be used to understand how priorities can change with maturity.
The Scale-up Manual(4) provides a structured way for any company to address its growth journey.
Schumpeter, J.A. (1947, reprinted in 2008), Capitalism, Socialism and Democracy.
Nelson, R (1981), Research on Productivity Growth and Productivity Differences: Dead Ends and New Departures, Journal of Economic Literature, No 19
Phadke, U.P & Vyakarnam, S. (2017), Camels, Tigers & Unicorns, WSP
Phadke, U.P & Vyakarnam, S. (2018), The Scale-up Manual, WSP, UK