Russell Quelch: Thanks for having me on. We’re starting to hear from some management teams in the sector around internal efficiency opportunities from generative AI. I was wondering if you could talk to how big an opportunity that might be for FactSet and when we might expect to see that in the P&L. I’m wondering if the opportunity is similar in size to peers or maybe a little smaller, given you have the majority of employees already in low-cost centers.
Philip Snow: Russell, it’s Phil, and I’ll start and I’m sure Linda has some additional comments. So yes, we’ve been talking about three areas there where we’ve been focused. So as you can imagine, there’s a lot of developers at FactSet. So they are currently being armed with tools that look very good in terms of being able to increase the efficiency of producing code. The question is like how do we apply that? Do we apply it to more product? Or do we apply it in other ways? So I think we’re getting a good handle on what those efficiency gains could be and which parts of our tech stack and product it makes sense to apply them. We’re also looking at content collection. But as you mentioned, we have quite a large number of our employees in low-cost locations.
So we’ve done a lot to automate workflows for content collection over the years. This adds, I think, some more juice to that even. So there’s some good opportunity there. Although the people of costs there are much lower, so the efficiency gains may not be as high as they are with engineering, for example. And then there’s a big opportunity for us in terms of just supporting clients. So FactSet is a very good tool. Some people use it in different ways. Some people use it in a programmatic way. So there’s a lot of coding that’s involved if you really want to customize your workflow. So I think it can really help both clients and people that support those clients internally. So those are three of the bigger buckets. We do see opportunity. And we’re going to be looking very hard at how we can apply that in our rolling three-year plan.
So we’re just beginning our efforts there to think about a budget for next year and the following years, and this will certainly be part of that equation.
Linda Huber: Yes. Russell, it’s Linda. You should look for those efficiency gains in our FY ’25 guidance. We’ve only given these new tools to our engineers relatively recently. They’re reporting good things, but it’s a little early to put a number on it. We think the biggest advantage from this will be cost avoidance as we bend the cost curve. And perhaps as the engineers get more efficient, we can sort of ramp down some of our hiring. So we think that, that’s a bright spot, but we’re going to look to first direct Gen AI efforts outwardly to make sure that we’re able to generate ASV with those efforts, and you’ll see more of that at our FOCUS conference. And then in FY ’25, the focus will turn more constructively to internal cost savings. We’ve been managing the cost of the company pretty darn effectively, I would say, and we’re looking to ensure that we are able to continue to do that, but gen AI is only a portion of that. So thanks for the question.
Operator: Thank you. One moment for our next question. Our next question comes from Keith Housum with Northcoast Research. Your line is open.
Keith Housum: Thanks. A question for you on capitalized costs. It seems like the amortization cost is going up, the capitalized costs have been going up year-over-year as well. I guess can you give us a little bit of color about what you expect it to be in 2024 versus ’23? And then how long are you amortizing this cost for?
Linda Huber: Yes, great question. We are — we’ve added some automation to our time tracking, which has helped us be more accurate. And I think for the year, we’re looking somewhere between $80 million and $85 million, which is better than we did last year. I don’t have that number right at hand, but it has grown. So you’re going to see some more coming through in terms of that capitalization. It probably has not peaked out quite yet. But we feel pretty good about what we’re doing there and that it’s very helpful to everyone involved that we spread these costs generally over three years would be the normal situation there. So pretty much right down the middle of the road in terms of how you would think about the time period over which we spread those costs.
So I think that should help you a little bit. As we move forward, we’ll probably look at that capitalization percent of revenues kind of moving up to 3% to 4% of revenues. I think we have previously said sort of 2.5% to 3%. But again, with automated tools, we are doing a better job of tracking the cost of building software and then moving that through in a capitalized manner.
Operator: Thank you. One moment for our next question. Our next question comes from George Tong with Goldman Sachs. Your line is open.
George Tong: Hi, thanks. Good morning. You mentioned earlier that price realization against your rate card is about flat versus last year, and that price realization in new business is a bit lower, reflecting a more competitive environment. Can you talk a little bit about where you’re seeing the most competition come from? And how would you describe pricing trends among your competitors?
