So it's 2022 and you're already familiar with one unavoidable fact: it is a year of adjustement. The post-pandemic valuation shakeout due to recent global issues has affected founders, professionals, funds, and individual investors. The main impacts are the massive layoffs and plummeting stock prices of public companies, shrinking entire markets and dissipating decades of gains. It is now harder to raise big money, down rounds are progressively common, and everyone feels compelled to adjust both funding and execution strategies.
Nevertheless, many companies and funds have been left untouched by that systemic winter. They were following the path of capital efficiency. They kept hiring, growing steadily or scaling even further by deploying capital the right way.
I do hope you're one of those relieved founders and investors. If you're not, you have everything to do with CxO. This is the main reason why it has been founded: to maximize the outcome of the Capital x Operation equation. Existing frameworks were packaged and made available to the market to service different customer profiles, mainly founders, investors, and corporations.
Understanding capital efficiency
There is always great potential on cool products launched by incredible teams in huge markets. The problem lies exactly in those words - cool, incredible, huge -, how often they are used, and how they're mistaken for real value.
Many founders and even investors tend to justify their assumptions over a startup with subjective claims and theories. Unfortunately, the real question often remains avoided: no matter what has been done and one's personal opinions, how more productive could a particular startup have been if business decisions were different, say, for the past six months?
- Was there space for better performance?
- How should the funding capital have been assigned and distributed over time?
- How much additional value could have been captured if small changes were made on execution?
- What could have been the additional gain on valuation?
- If anything could have been improved, have the founders quickly learned what to change, how, and when?
- The final question: would the 2022 downturn be so devastating had those answers have been found and widely practiced by founders?
To rectify that, the smart ones perform execution experiments and analyze resulting metrics such as revenue, churn, and MAU, having growth rates counterposed to estimates on competitors' performance and the size of their addressable markets. Even so, for early stage startups, those estimates are very tricky: you can't infer realistic future numbers from near-zero historical data. This argument holds true throughout the initial phases of a startup, such as validation and traction, because revenue sources are still unstable in every chart until the growth phase starts showing trends towards scale.
Without reliable data, that whole problem remains unsolved. Since precision is often insufficient during the journey from product to scale, estimates can be easily questioned. That context makes it extremely difficult to define how efficient a startup is at any point, how capital is internally deployed, and how their results impact its valuation. Nevertheless, that definition is a key decision for both investors and founders before signing a termsheet.
When numbers can not be trusted, decisions based on unstable data are shifty and uncertain. Most startup founders aren't able to collect meaningful data in a systematic and non-subjective fashion. Since reliable data is key to reduce risk in venture capital and startup execution, many founders and investors end up performing the so-called art of investing (or the art of execution) instead of using science and reasoning based on reliable numbers.
CxO: when heuristics meet big data
After two decades working on that topic and profiling thousands of startups for investment or improvement, a fast, simple, and less unreliable solution was built and tested for the execution efficiency riddle. That particular solution has been derived from the analysis of thousands of empirical data points. The engine predictions show strong correlation with the real performance of test sets in different startup stages.
So far, the CxO expert system and reports have shown improved results when compared to subjective approaches assessing startup execution efficiency, plus dramatically increasing the productivity of VC analysts.
While looking for an assertive and data-driven solution, the research was based on two initial hypotheses:
A) Founders should never execute more efficiently than required by the market they service and operation they run, considering the typical scarcity of data for unproven or pre-scaling startups
B) Due to many reasons, such as inexperience or lack of reference metrics, founders will often execute way less efficiently than required by the same circumstances
The sweet spot would be somewhere between A and B. The description of that sweet spot would be the execution playbook used by founders to maximize productivity and growth with minimum capital. Thus, execution efficiency towards a scalable business would be just enough effort to cope with market and client demands, but not more... otherwise, there would be waste as a consequence of overperformance or overspending.
The search for that sweet spot in execution efficiency lies in finding that particular playbook, which varies according to each startup's stage, team, product, market, and many other variables.