Stripped down business models: Why
Posted: November 1, 2011 | Author: admin | Filed under: tools | Tags: business model, complexity, financial model, KISS, process | Leave a comment »—————————————————————————-
A good first step in discussing a complex business is articulating a simple business model, not a complex one.
Despite the fact that conventional wisdom, the KISS principle (keep it simple, stupid) and host of other heuristics would lead one to this not-so-earth-shattering conclusion, it amazes me how difficult it can be to actually keep it simple and avoid the trap of piling on complexity. Building a stripped down business model is a great exercise in sticking to the basics and describing the business idea at its economic core. It also creates a powerful, accesible visual communication tool for promoting an idea.
In an informative post on how consumer internet startups make money, Steven Carpenter uses a downloadable set of financial models to focus the reader on the key drivers of various startup business types. These are not complex discounted cash flow models; they are simply 5-10 lines of text that describes the various drivers and associated values that plug into a very basic algebraic equation. That’s all.
Very rarely does one have the opportunity to interact with people (in business and in life) who know exactly what one is talking about. Starting from the most accessible set of relationships, concepts and figures is a solid approach. But its not nearly as easy as it sounds. Taking the time to write out (in a spreadsheet, on scratch paper, in the dirt with a stick, etc.) a stripped down business model is a good habit. It encourages good communication and a simple, elegant organization of thoughts.
Bootstrapped decision-making models: When
Posted: October 29, 2011 | Author: admin | Filed under: tools | Tags: bootstrap, bootstrapping, decision-making, process, random error, risk | Leave a comment »———————————————————————————————-
In practice, bootstrapped models have many uses. The most scalable way to use these models are for informal analysis “gut checks.” The human mind works in funny ways. Simply having a parallel bootstrapped model to casually refer to can quell fears or heighten suspicions, potentially improving even a poorly codified decision-making processes.
Performance benchmarking is another useful area for bootstrapped models. By removing random error, a bootstrapped model can serve as a useful benchmark for analyzing and evaluating decisions to figure out what went wrong (or what went right).
Risk management is another interesting application. Hard-coding checks to catch decisions that lie outside a predefined range of outcomes/values (derived from bootstrapped models) is a direct way to catch exaggerated instances of an analyst’s natural human inconsistency. And it’s fundamentally different from a purely quantitative risk management methodology, since the measure of risk is a deviation from what a human would have theoretically done following her or his own approach.
Finally, one could go all in and build fully employed decision making tools from bootstrapped models. The resulting tools would loosely resemble a true quant model, though using a model in this manner still requires updating the model as the human perspective changes.