Sinking Ship

Why my first startup failed

The purpose of the article is to share my insights on why my first startup failed so that you potentially don’t make those same mistakes. There were many issues with the way I ran my first startup, however, I’m going to only list core flaws that I thought were the key players in our demise. Hope you enjoy!

Build-Measure-Learn Feedback Loop

I’m going to quote Eric Reis from his book, “The Lean Startup” because I can’t say it any better:

“Plenty of entrepreneurs focus their energies on the individual nouns: having the best product idea, or the best-designed initial product or obsessing over data and metrics. The truth is that none of these activities by itself is of paramount importance. Instead, we need to focus our energies on minimizing the total time through this feedback loop.” (76)

My first startup spent too much time chasing wild one-off ideas and didn’t take the time to learn if what we’re doing is actually working. Startups should focus their energies on validated learning, which is essentially running all of your startup ideas through the scientific method. Using the scientific method for validated learning applies to all departments at a startup – not just tech. Here’s a high-level breakdown:

The Hypothesis

The process of validating learning starts when startups focus on validating the hypothesis about the service they’re providing. This hypothesis must be fallible – meaning that you can 100% prove or disprove it at the end of the experiment.

Example Hypothesis: “My potential customers are willing to pay $10.00 a month for my service”

Setup the Experiment

Once the hypothesis is set your team, in a cross-department collaborative effort, must decide how to set up the experiment so that you can collect the data needed to validate your initial hypothesis. This may mean setting up the proper website tracking code (e.g goals in Google Analytics), sending out a survey, setting up meetings to talk directly with your target market, etc.

Execute & draw meaningful conclusions

Once the plan is in place on how and what you’ll do to run the experiment, it’s time to get out there and do it! Going off the example hypothesis, a meaningful conclusion would look like:

1. Yes, users are willing to pay $10.00 a month for my service for the following reasons…[report of the experiment backed by actual data goes here]

2. No, users are will not pay $10.00 a month for my service for the following reasons…[report of the experiment backed by actual data goes here]

The faster your startup can get to a conclusion, the faster you learn and spend significantly less time building a product no one really wants. Running all your ideas through the scientific method will give you an indication on whether to pivot or preserve the path your startup is on. The most successful startups are not the ones who have the best management, the most money, or the best engineers. The most successful startups are those that learn the fastest.

Final Notes

Knowing why your users love and hate your product is paramount to your startup’s survival. The absolute last thing you want is to not document your results and waste time doing what you knew at some point didn’t work.

Why let your marketing run an ad marketing campaign before reading documentation about a similar campaign ran last year with metrics on what worked and what didn’t?

Why spend time building & deploying an accounting software before finding out how & why your target market handles their finances the way they do? 

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