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“My advice to other entrepreneurs, when they ask when they should go for it and when should they quit their day job to go work on their new idea is when you have an idea, a problem that you want to solve, and the problem is more important than the solution. Most tech companies don’t fail because the programmers couldn’t write good code. Most tech companies fail because they solved the problem that no one cares about. What’s most important is solving a big problem.”

Paul has had a prolific career as a founder, with all the highs and lows you would expect. But for Paul, the thrill of the next big idea and the next adventure is what keeps him going, and his success has been rooted in his. ability to understand the big issues and develop and implement a plan to rectify them. On this episode of IT Visionaries, Paul goes into detail about that skill, and he explains why Moonbeam, his new company, is solving one of the biggest pain points in on-demand programming. Plus, Paul dives into his past experiences, details key pillars that make up a successful CTO, and predicts why Moonbeam is about to take-off.

Main Takeaways

  • Hey Now, You’re a Problem Solver: For any entrepreneur that is starting out, you have to answer a few questions before you get started. What problems are you solving, and is the problem something that will resonate with consumers? It’s very easy to get locked in on ideas that are important to you. But if your problem is not something that the rest of the public cares about, your business will most likely fail
  • Search and Engagement: The two most important factors of any social platform are the ability to identify relevant content, make it searchable, and then drive engagement for its active users.
  • The Power of the Algorithm: When you are designing a machine learning strategy, it’s very important to make sure that your algorithm is not just feeding users the same content over and over again. Instead, make sure that your platform has some level of randomness to it, so the user is exposed to new forms of media.

For a more in-depth look at this episode, check out the article below.


Article

They say one of the biggest attributes any entrepreneur needs to have is the ability to problem solve. To be able to identify challenges large and small and effectively produce a solution for them. It seems simple, but it’s not. A vast number of start-ups fail, and the reason is although they identified and addressed an issue, it’s not the right issue. So how do you know when the problem your company is attempting to solve is the right one? Paul English, Founder and CTO of Moonbeam, has some noteworthy advice.

“My advice to other entrepreneurs, when they ask when they should go for it and when should they quit their day job to go work on their new idea is when you have an idea, a problem that you want to solve, and the problem is more important than the solution. Most tech companies don’t fail because the programmers couldn’t write good code. Most tech companies fail because they solved the problem that no one cares about. What’s most important is solving a big problem.”

Paul has had a prolific career as a founder, with all the highs and lows you would expect. But for Paul, the thrill of the next big idea and the next adventure is what keeps him going, and his success has been rooted in his. ability to understand the big issues and develop and implement a plan to rectify them. On this episode of IT Visionaries, Paul goes into detail about that skill, and he explains why Moonbeam, his new company, is solving one of the biggest pain points in on-demand programming. Plus, Paul dives into his past experiences, details key pillars that make up a successful CTO, and predicts why Moonbeam is about to take-off.

Less searching, more listening. That’s the Moonbeam motto. Moonbeam is a new podcast platform that helps listeners connect with the best shows and hosts based on a user’s listening habits. As an avid podcast consumer, English routinely realized the amount of time he would spend thumbing through platforms such as Apple and Spotify, but never finding a show that connected with him. 

So he decided it was time to build a player that allowed listeners to interact with the platform in a few unique ways. 

“I wanted to solve a couple of problems in particular,” English said. “[The first was] how do you find that next hot podcast? How do you find something interesting? Because there are a million podcasts out there. How do you find the really good ones? [It’s a] discovery problem. The second thing I wanted to solve is if I’m really inspired by a podcast host or by what they said in a show and I want to interact with that host, and I want to be able to have discussions with them…It’s those two problems that led me to found Moonbeam.”

Search and engagement. Two problems that entrepreneurs, marketers, and technologists struggle with. But what makes Moonbeam different from other platforms that already curate playlists and suggest shows based on algorithms? According to English, it’s how Moonbeam’s system observes your behavior and listening habits.

 “What we want to do is have a machine learning system that observes your behavior,” he said. “In machine learning, there’s a term called features, which are the behaviors that feed into the algorithm. We have a number of features we track of how you use the product. And we’re trying to predict for you what shows you’re going to like. We do this by clustering users. So if we notice 10 shows you like in particular, that you’ve interacted with those hosts, we then find other users who have similar listening behaviors to you.” 

Building a community of listeners through like-minded users requires an extensive machine learning approach that not only feeds listeners content they may enjoy, but also provides them with new listening experiences, which English says is key.

“One of the things that’s really important [when it comes to] machine learning is you need to always have some level of randomness,” English said. “If you simply show consumers what you already know that they like, you become an echo chamber. It’s not a very good experience. So they need to sprinkle in different things to try out. And if they notice that you’re starting to use those things, they will show you more things like that. So it’s trying to find users who have tastes similar to you. But we look at all those things. We look at your interactions. Do you watch or do you listen to the show all the way through?”

But in order to build that community, English said you first need to have a large enough sample size of users to test your theories.

“With machine learning, you often need 10,000 or so users [get a big enough sample size],” English said. “We’re doing a lot of manual labor right now upfront with Moonbeam. We have a number of curators that are going out and identifying interesting shows and identifying the most interesting snippets within a show because sometimes listening to shows at the very beginning is not the most interesting part of it. If we’re giving you a swipe experience, we’re going to give you a taste of a show. It might be three and a half minutes in. There was something the host said that was particularly hilarious, particularly insightful. So we have human curators that are going through shows right now by category and we’re trying to find the most poignant snippets. Once we have our first 10,000 users, then a hundred thousand users, observing user interaction will be more accurate than the human editors we have right now.”

To hear more about the technology behind Moonbeam, and more about English’s other entrepreneurial endeavors, check out the full episode of IT Visionaries!

To hear the entire discussion, tune into IT Visionaries here

 

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