Garrett Lord: evaluation

Transcript context

…And it's experts mostly at this point. Yeah. And then they put them onto an experience that is treating them like they're drawing boundary boxes around stop signs in the Philippines. The frontier tax accountants don't want to be treated like low cost international labor, and I don't think anyone enjoys that process. And so, the ability to build a experience that's rooted in community, that's rooted in high quality training. If you're getting your PhD at MIT, chances are you're just not being taught well enough on how to use the tools. It's not you can't break the models, it's just like the other platforms, they're spending thousands of hours to acquire an individual user and they're put right into a project with no training. So, we just started from day one at building this expert ... We believe there'd be a deep network effect here that's very connected to our core business of starting, jumpstarting or restarting your career. And you come in, you build a profile, you see the community, there's groups and a feed of here's how people are learning. You come into actual individual cohort with peers that look like you and have your similar background. You're being taught on how to interact, and there's a trial and error, and we have an instructional design piece so you can't do it. Then you're put on the projects where building ... There's certain swim lanes where we're actually pre-building data and selling that data to all the labs. So, we can do this thing where we produce one unit of data ourselves. We pay for it, almost like a movie production. We pay for a unit of data, and then we make sure it's very high quality. We run our own post-training on it, and then we produce a bunch of specifications of the data, and we actually sell that individual package of data to many different labs. And so, you get put on a project like that. Once you're doing a really, really good job on our projects, oftentimes then we'll put you on customer projects where they only want the best of the best people in machine learning. And then they go from our projects to their projects. And so, there's a huge customer acquisition. You love going deep on your podcast, so just to talk about it, it's like you really have a couple of things that matter. You have a cost to customer acquisition, your CAC, and then you have your LTV, like the lifetime value of a user. And an LTV is calculated pretty simply in this business. It is based on the retention of a person, and how many projects they can participate in. So, if you treat people really well, you train them really well, well, A, we have no customer acquisition costs because we partner with 1,600 universities, power 92% of the top 500 schools in the country. We power almost every institution and community college in the country. We have no customer acquisition cost to acquire the people. We have a ton of brand and trust with them built up, so they convert at really, really high rates. ion and community college in the country. We have no customer acquisition cost to acquire the people. We have a ton of brand and trust with them built up, so they convert at really, really high rates. And then, if you treat them really well, because what they expect from us, they know Handshake, their school buys Handshake, we care about treating these people well but the universities would not tolerate our partnership with these fellows unless we treated them well. So, you put them into this process where our LTVs and repeat engagement rate and retention rate on different projects is really high. And so, these structural advantages are quite significant when you contrast a leading provider that has 200 individual contributing recruiters, and are spending tens of millions of dollars a month on performance marketing. So, that's I think why we've seen so much success.…

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