The tone determines your Positioning and positioning determine the success of your startup. Naturally, there is a lot of advice on how to present your startup. Among these tips, a common suggestion is to avoid using jargon. In fact, experts will tell you not only to avoid jargon, but also that your speech should be understood by a five-year-old. Unless your clients are five years old, this is bad advice. Jargon is the shortest and most precise way to explain a problem. The key is to make sure your target audience can understand it. If you sell to niche customers, you can use domain-specific jargon that they are familiar with. It can make them feel like you are speaking their language.
Why slang gets a bad reputation
When I was a developer, I used jargon a lot when communicating with other developers. They appreciated it because it gets the point across accurately and saves everyone time. But when I became a founder, that became a problem. We had created a machine learning algorithm that worked without training data. When I presented it to other developers, I would say that we are solving cold start problems in machine learning. The developers understood this. But when I presented it to clients, none of them understood what I meant. I followed common advice and tried to simplify my speech by saying, “You can build machine learning algorithms like Google even if you don’t have a lot of data like Google.” It still didn’t resonate. Finally, an e-commerce founder told us that his algorithm for recommending his products doesn’t work for new users because they don’t have much information about them. He asked us if our technology could solve that problem. We said yes and changed our pitch to “product recommendation engine for new users.” It worked.
Jargon is the shortest and most precise way to explain a problem. The key is to make sure your target audience can understand it.
The problem is not the jargon itself; It’s about using jargon that matters to your customers. “Product recommendation engine” is also jargon, but it addresses a problem that worries customers, while “machine learning algorithm that works without training data” doesn’t convey what customers can use it for. It is assumed that the problem we are worried about (“cold start problem” in our case) is something that customers are also worried about, which is often not the case.
Image credits: Mani Doraisamy
