A kind of toxic learning that emerges out of startup lean execution — Startup Ecosystem
Reference
- Reference meeting: ""
- Original context meeting: "
- Parent project: Startup ecosystem
- Participants: Marcio S Galli
- Text language: pt-br
- Tags: Startup culture, truth-seeking culture, learning, openness, transparency, process, lean startup
- Document status: Copyright, draft.
Action potentials
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New article - v2
Potential titles and reasoning statements
Entrepreneurs use learning as excuse to not fail — the founders prefer lack of transparency and accounting to the process of validation. They won't tell in advance how they going to evaluate the process that they are doing. They can use any (random or biased) interpretation to move forward in anything they want.
Toxic side of learning when working with lean — entrepreneurs can fall in the romantic trap of learning. It's in the DNA of lean - iteration to learn. Thus the general passion for learning, or assuming that learning is the default, means a lot; to the point to influence perhaps negatively.
The half-truth of learning — because part is method and part is learning, it leaves the room to potentially fall into any decision under the learning component. Watch out for how you are connecting learning to your process execution.
Related references
- Doing lean versus talking about lean (feat. Steve Blank and Rohan Workman)
Original article and insight - v1
More openness to the startup validation process
A startup case using validation to move forward and how transparency and openness can help a team to assess better their risks
One of the major startup team skills is ability to challenge assumptions. This is specially critical for the CEO and the founding team if they recognize their natural potential to create bias which can be a problem for the goal to move the startup from insight to problem-product and product-market fit.
This essay came out based in an incident from a startup that I have helped as an advisor. Their startup started as a marketplace for local practical courses, but they evolved a bit to make a marketplace for local group-based experiences. Cooking Russian Syrok? having a Thai dinner? not a problem.
The process and the incident
Their team plus friends, family and more, started doing brainstorming sessions. They have collected ideas for these group-based workshops. After that, they came up with a queue to check which workshops could become real. Their process for validating also had to do with looking for potential customers, therefore they had to also go out of the building to check some of the top proposals.
One of the cofounders came back from this out-of-the-building work and shared, in the team chat, that he found evidence related to one of their idea proposals. As soon as the CEO saw the post she wrote:
"Let’s put that idea in works for the March pipeline. If someone is advertising via Instagram it means this is a trend. (Jonas the fictitious name for the CEO)"
The first analysis and the frustration
My first analysis was a sort of good feeling: Oh, how nice the reaction, very proactive and in a way based on some evidence. First, the team is indicating for everyone to seek data points and when they see them as validation then it’s a go. It sounded like they didn’t have a random process and it was pipeline!
On the other hand I was frustrated because the decision making sounded like they had a process, a validation system, but on the other hand, it seemed that their criteria was wrong, or perhaps a bit random. What if the CEO had never written that note in the chat? What if the cofounder have not stumbled in that web site showing a product related to the workshop idea? I also got slightly worried because (by chance) I sort of knew a bit about that topic and noticed that a simple Google query would give significant data.
The analysis and seeking the solution
For a visitor, following their chat conversations, it was unclear what were the criteria or basis for their decisions. How one would know that the collected data had any connection with the decision associated? Using the case, how would they know that the finding of a competitor’s Instagram ad would really mean any indication to move forward?
What seemed to be confusing, in the end of the day, was not only the lack of criteria, or reasoning process for their decisions to move forward, but the lack of explanation for the design of the criteria.
The problem there, and for many other startups when they need to challenge their designs, seems to be around the fact that everything is yet justified as learning. It’s easy for any founding team to enter and leave a bubble when they need, as they need, at any time. For example, if an advisor adds much pressure, a possible response could be that they are a startup and they are learning, or, that they can evaluate and learn as they progress. That would leave the “disagreeable-giver” advisor in bad position exactly because indeed learning, for a startup, is probably the most important strategy.
A softer and yet stronger conclusion I came, however, is to recommend for founders to start to tell in advance their intentions for any activity or experiment: To first reveal the design in any way they can and what they want to learn from the experiment in front of them. It’s like they would need to priorly tell the rules of the contest. And then seek participating entries (in this case data collected from the outside) and later simply check the entries agains rules.
Being more transparent is specially key for these efforts that are followed by decisions. Telling in advance what would happen could help the team to defend the startup against themselves; to avoid self-convincing bias or the comfortable zone of using any data to validate any random next step in a way that sounds elegant. It’s easier, and even honourable, to do anything possible and later claim to have learned from that. Perhaps this is a side track of success that can be hell for startup ideas — entrepreneurs can perfectly execute learning processes.
Conversely, it’s quite challenging to pause beforehand and claim the goal of the experiment and what criteria is in place and what learnings are expected. This does not mean that a team cannot make mistakes. On the contrary, this is exactly the thing that should allow the team to move forward and acknowledge more mistakes along the way; as opposite to keep moving as if everything is perfect.