Have you ever had that moment doing analysis on a certain problem, and in the middle of it you stop because you can’t even remember what question you are trying to answer? Or your boss keeps asking for one more piece of analysis to help answer the problem and it feels like the cycle will never end? There are many decisions to make for a startup, some big and some small. Being data driven will definitely help you make better decisions, but be careful to avoid analysis paralysis where you overanalyze everything, especially using data that is based on early faulty assumptions vs. real-world customer or market data.  Here are three things to consider to avoid analysis paralysis.


The first thing to be clear on is what problem you are trying to solve and what decision you are trying to make. This is important because some decisions require high-level analysis/low precision (i.e. market sizing), whereas others may require more detailed analysis/high precision (i.e. cash flow forecasting). Make sure you understand what level of precision is required to make that decision. A related area to clarify before you start your analysis is to develop a specific hypothesis to test. This really helps you focus the analysis on proving/disproving a hypothesis versus spinning your wheels looking at lots of “interesting” numbers in a spreadsheet.


One trick I like to use on any analysis is to apply the cocktail napkin test. Without spending a lot of time, do some quick math to test if idea even remotely makes sense. For example, if it costs me $1 Million to build product X, and I plan on selling each subscription for $1,000/year, I would need to sell 1,000 customers to break-even … does that even make sense? Oftentimes the quick cocktail napkin test can show that a certain decision doesn’t even make sense and you save yourself lots of analysis time. Once you pass this test, you can move onto the next level of analysis.


Lastly, remember you are doing the analysis to make a decision regarding a hypothesis. Only do enough analysis necessary to make that decision. For example, if you are trying to target a new product in a market >$1 Billion, whether the market size is $5 or $5.5 Billion doesn’t matter. It’s more that it is $5 Billion vs. $500 Million. It’s easy to get lost in lots of numbers, pivot tables, and fancy graphs. Keep your focus back on the decision to be made, and stop the analysis once you have enough information to make that decision.

If you are unsure on the answer from the data, there are other tricks to get you more comfortable such as triangulating analysis from multiple sources/inputs, running sensitivity on key assumptions, etc. But big picture, remember that a clearly defined hypothesis/problem and some quick analysis can usually get you the directional answer you need to make a decision and move out of your spreadsheet and on to more important work like getting in front of customers!

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