Elena Verna: prediction

Transcript context

…Cool. Okay, and then just one last question on what you were just talking about, which is finding correlations and causal relationships between usage and users and understanding who to send to sales. What are some common metrics and attributes that you've found that are indicative of they should go to sales? One of the most important things is to create network effects within the company, within a team, which each additional team member added to an account benefits from everybody else. This does not mean you have to have platform network effects. You're not trying to be LinkedIn. You're not trying to be Instagram. Those are platform network effects. What you are trying to create is the value of each additional user into account. What that means, I'm going to go through slight deviation here is that there should not be only pulling mechanism where I pull you into my account. There also should be pushing mechanism. Any new sign-up that happens from the company that already has an account in my system should be presented with an option to join that account, not just create a brand new account. What you don't want to create is hundreds of rogue individual accounts that don't know about each other. We as humans, we are creatures of community, we're creatures of we like to be surrounded by people, to be validated by people, and at least you want to create that herd mentality inside of the company and push and pull people into the account. One of the biggest PQAs is number of users in the company using your product. By the way, the magic number is usually seven. The more time you have seven or more users in the company and there's like some weird parallel here to seven friends at Facebook and seven collections at LinkedIn, but seven I've seen very often as a check mark. "Hey, there's enough value here distributed across multiple people inside the company that there might be an enterprise conversation to be had." The second-most important is usually some sort of volume threshold. For example, for Amplitude, it's number of events that you're sending through, which means that you are really starting to tackle your entire application with analytics as opposed to just a small portion. Or with Miro, it was number of boards that you have in the account. With Figma, I think it's number of revisions that you have on any given design. The last one I would say is velocity. Velocity is the trickiest one just because we don't have a very easy way to measure velocity in our transactional data, but it's actually one of the most powerful one that triggers the right timing for the involvement because the change in velocity either number of users being added or events being sent or storage that is being utilized is usually a fantastic predictor. Amazing. Okay, so kind of like the simplest model to tell you there's a peak-y way is number of users usage and velocity.…

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