A Prototype Business Model Simulator

I’ve been researching business model simulation and wanted to share a beta version of the model for review and comment. I’ve published the model in the form of an interactive web application in the hopes that readers will open the app, play around with it, and provide me with feedback.

Prototype business model simulator. Play with the model and let me know what you think.

Prototype business model simulator. Play with the model and let me know what you think.

The simulation was created by using basic abstractions taken from the business model canvas:

  • Customer Segments (CS): groups and organizations that an enterprise hopes to serve
  • Value Proposition (VP): product features that create value for the customer segments
  • Revenue Stream (RV): the money generated from the customer segments
  • Key Partnerships (KP): the network of suppliers and partners
  • Cost Structure (CO): the sum of all costs incurred

We represent the performance of the business model based on the money invested into the business, CO, and the revenue, RV, generated as a result. We can model the influence of CS, VP, and KP on business performance using a set of simple one-variable mathematical functions {RV, CS, VP,KP}, where

  • RV(CS), Market Potential: Based on the general economics demand curve, market potential models revenue as a function of the number of consumers reached
  • CS(VP), Buyer Behavior: Based on the Bass Diffusion Model, buyer behavior models consumers reached as a function of product features produced
  • VP(KP), Operational Efficiency: Based on the Cobb-Douglas Production Function, operational efficiency models features produced as a function of key partnerships
  • KP(CO), Partner Efficiency: Based on the Wright Learning Curve, partner efficiency models partner activity as a function of cost

I’ve added random jitter to each function to represent uncertainty and chance within the simulation – unforeseen problems with suppliers, chance improvements in efficiency, unexpected jumps in buyer purchases, etc. The final simulation results come from the composition of functions as follows:

  • RV(CO) = RV (CS (VP (KP (CO) ) ) )

Which gives us a function that predicts a final return on investment (revenue as a function of costs). The parameters in the app allow us to tune each of the functions so that they represent a specific business model. The outputs of the application can be thought of as theoretical propositions or hypotheses. In a sense, the simulator is a hypothesis generation machine, for example:

“Companies that sell products with higher coefficients of innovation have a competitive advantage, but only if they also have higher learning rates from their key partners.”

In a previous blog post, I described tuning the model to explore the hypothesis that the Netflix video-by-mail model was a threat to the Blockbuster physical-video-rental model.

At this stage, I am trying to validate the reasonableness of the model by testing if it seems to jive with reality. Please play around with the model and test it against your own intuition, case studies, or business performance data. Leave a comment on the blog, find me on TwitterLinkedIn or through email, and let me know what you think.

overton-2015Jerry Overton is head of advanced analytics research in CSC’s ResearchNetwork and founder of CSC’s FutureTense competency, which includes the Predictive Modeling Research Group, Advanced Analytics Lab and Predictive Modeling School. Connect with him on Twitter.

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