Bill Magnuson: Yes. I think this just points back to the reality that customer engagement is a really multifaceted operation that is always on in some extent. And I think that a lot of people look at marketing technology in general and they think, oh, that’s — it’s like advertising spend, it’s discretionary, et cetera. The responsibility to be communicating with your customers isn’t always on one. And especially as we see customers continue to expand into new use cases as they take advantage of new feature development like cloud data ingestion to bring in new data sets and drive yet more use cases and more messaging. We continue to see them expanding across multiple channels. There’s just a lot of expansion in the surface area of the product.
There’s expansion of our customers as they find ROI in their early use cases and they expand into new ones. And then, just the fact that so much of what BRACE does mission-critical communication in order to deliver products and services. I think that the secular trend toward being able to build up first-party relationships inform those first-party relationships with first-party data and then take action on them directly is one that’s becoming an imperative for brands of all kinds across every vertical and of every size.
Operator: And our next question comes from Arjun Bhatia with William Blair.
Arjun Bhatia: Bill, maybe just to stick with you. You mentioned some of the AI investments that you’ve made over the past several years. But when you think about the advent of generative AI, what do you think that can do for the platform from a customer standpoint? Does it help you be more competitive? Does it drive more productivity for your customers? Just would love to get your thoughts on how this plays out over the next several years?
Bill Magnuson: Yes, absolutely. So I’ll take a step back from just generative and give you kind of a framework for how we think about AI and ML and its influence in customer engagement in general. So you’re effectively kind of bucketing things in a few ways. One is — is this a copilot for a human? Is it automated optimization? Or is it a full autopilot. And I think that the nature of first-party data and managing first-party relationships is that the bar for full autopilot is much higher than it is in things like advertising or even in areas like prospecting and B2B. And thus, you should expect to see the move towards something where you have like a Magic Black Box or a full autopilot move a little bit more carefully in the first-party data space and the first-party relationship space.
It’s one thing to have an ad or an e-mail that’s well targeted on someone that you, or that’s been crafted for someone that you don’t know very well, it’s very different to have that live inside of your own product experience. A related concept that require training data and that you need to learn over time versus those that can be used immediately. We definitely balance our investments to make sure that we’re delivering on both. Obviously, a lot of the generative AI stuff recently has been particularly special because much of it can be used immediately. It’s easy to get up and running and learn and even the learning cycle when you’re in the middle of a conversation in Chat happens pretty quickly. We found that the systems which learn over time are best suited for partial automation, especially for things like variant or full strategy testing for which Canvas is a really robust framework or other things like predictive intelligence targeting.
