TL;DR - Monte Carlo Simulation can be used to model your historical sales pipeline data to account for some of the randomness and uncertainty in the sales process. In this post, Monte Carlo Simulation is introduced and then applied to the same case introduced in a previous post.
What is Monte Carlo Simulation? Monte Carlo Simulation is used to simulate a process using random sampling. It is a very general technique that can be used to model all kinds of processes in fields ranging from physics and chemistry, to economics and finance.
TL;DR - Using a Markov Chain to model your historical sales pipeline data can help you understand the probability of eventually closing a deal based on (1) the stage the opportunity is currently in and (2) your historical data. This lets you estimate the future value of your current pipeline based on your own past data.
What is a Markov Chain? It is a mathematical model used to describe how probabilistic systems or processes evolve over time.
TL;DR: Listen to customers, test your assumptions, follow the data, repeat
I participated in the NSF I-CORPS program quite a while ago now (LA Node, Fall 2016). The goal of the program is to assist scientists with technology transfer. It’s basically a crash course in entrepreneurship for STEM researchers. The program takes place over about 8 weeks and participants interview at least 100 people (customers, competitors, thought leaders, and partners) with the goal of finding product-market fit and determining the feasibility of commercializing the result of their NSF sponsored research project.