Founded in 2014, Mighty AI delivers training data to companies that build computer vision models for autonomous vehicles. They work with some of the world’s leading automotive original equipment manufacturers, suppliers and startups to supply autonomous vehicle data. In addition to its offices in Seattle and Detroit, Mighty AI recently opened an office in Boston to better support its East Coast and European customers.
Hiring too fast and ignoring product issues.
My background is in startups. When you’re doing a startup you’re building a product, but at the time you really have no idea whether or not customers want that product. Oftentimes what happens is you build a product, you get early traction, and you start to hire a bunch of people, specifically sales and marketing people. Then you hit a wall. That wall is either product challenges or customers don’t renew, and you end up burning a lot of cash. Then you either have to go to your current investors or raise more money, and raising more money when having customers cancel is not a good thing.
The biggest mistake I’ve made is just that. You launch a product and you see good traction and you start to step on the gas, but you tend to step on the gas a little too fast.
I was working at a startup when I was young and naive. We had this backup product and everyone’s telling us it’s going great, but the sales team is telling me there’s lots of issues. I said, “No no no, it’s going to be great.” So we hired a bunch of salespeople to sell the product because we had a lot of customers using it already.
What I really didn’t drill into was, how happy were the customers using the product? What was the defect rate and issues that the product was having? Were the backups really occurring? It got to the point where six months later we ended up laying off all but two salespeople. It was very painful because a lot of those people that were at the company on the sales side were great. They signed up to join a fast growing venture-backed company. What they ended up signing up for was certainly fast growing, but with a product that had a lot of problems.
The good news is we ended up rehiring some of the salespeople about six months later. We fixed the product, the company grew and eventually was sold off to another larger company.
At the end it was a great success story, but there was significant pain along the way we could have avoided by really listening to our customers, getting closer to understanding their experience with that product, and looking at overall sales productivity and their performance over a period of time.
Customers literally pay your paycheck. Get as close to the customer as you can.
The lesson is, don’t hire too fast, and understand your customers. You really have to understand the problem that you’re solving for your customers by talking to your customers and looking at the data before you hire a big sales team.
In our example, we had a backup product where customers downloaded a software agent they would run locally. We could look at the data being generated from that agent and be more data-driven rather than think, “Oh money’s coming in, that must be a good indicator, let’s go hire more people.” In some case that’s the right thing to do, but the money that’s coming in could be masking a bigger problem.
It’s all about your customers, and customers literally pay your paycheck. Get as close to the customer as you can. At the end of the day you’re there to solve a problem. You’re solving that problem and they’re giving you money. If you get separated from the problem you’re solving and are focused on other things, you’ll ignore why you’re really there.
That’s what we learned. We were signing up customers, but we weren’t making them happy and we weren’t keeping them. It was like a leaky bathtub—we were signing up new customers, and filling up the tub, but other customers were leaking out. We had to decide to plug the bathtub and keep as many customers as we could. By fixing the product and understanding what the issues are, delighting customers by making them fans of our service, then we could grow. It required us to lay off people and slow down, in order for us to go faster over a period of time.
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