Robert Musslewhite: Yes, it’s a really good question. I think traditionally, we would have said kind of before the second half of last year, deal cycles were three to six months with more complex deals being on the longer side of that and smaller single-product deals being on the shorter end of that. It’s hard to say what the actual cycle is on stuff that closes. It’s probably extended by two to three months. But when you ask about consistency and looking forward, what I would say is that our forecasting has gotten a lot better around the sort of new environment and how long deals take to come in. So, I do feel like we have better visibility this year, entering the year kind of assuming the market, kind of taking the market as a given and incorporating that into our forecast.
So, while we might have gone back to September and say we were surprised by a lot of things that pushed off, I’d say now, we do know things are going to push off. We’re not surprised by it so at least that’s progress. And what I’d hope that over time, and I mentioned earlier is that with a little bit of benefit from the macro, we have these large pipelines and you do have deals that are genuinely pushing. Once people decide to spend and put the budgets, we can start getting those sales cycles back down to levels we saw in the past and that would obviously accelerate ARR this year, if and when that happens.
Matt Shea: Okay, got it. That is helpful. And then I wanted to touch on something that Jason mentioned, where you saw in your survey that half of life science organizations think that they’re spending too much time matching data across data sets that’s something you can help with. I’m curious relative to that demand, what modules you have today that can meet that demand head on, whether you would need to leverage some professional services to assist with that? And then to the extent that there’s some gaps in completing that data matching strategy for those life science companies, how that might guide some of your investments over the next year or two to capitalize on that demand?
Jason Krantz: Yes, hi, It’s Jason. Great question. So first of all, as you think about the Atlas Dataset, which we just rolled out, the whole point of that is to be able to provide an enterprise-wide view that’s really the source of truth for these clients. By bringing together our Reference & Affiliations data with our claims and prescription drug and expert data, that really solves a lot of problems for our clients versus them trying to mix and match from lots of different places. So, that is super important. And it also provides us the foundation to where we want to invest in the future. So, as we continue to bring in new data sets through internal development, we’re about to roll in, for example, a digital opinion leader data into the Atlas Dataset, so we can really give our clients a sense of who are the influencers are online.
And how is that important as they think about going to market with new drugs and therapies and medical devices? And then similarly, as we think about our M&A strategy, it’s really about how do we continue to strengthen the Atlas Dataset and bring in more unique data sets to it. But then also how do we leverage that in new and unique ways for our clients, both through new capabilities like Analytical Wizards as well as internal development, where we’re creating new use cases and new ways to allow our clients to solve as many business problems they can with our data. So, it’s all related together and highly strategic in the way we’re thinking about it.
Matt Shea: Awesome. Thanks guys.
Operator: Thank you. Our next question comes from the line of DJ Hynes from Canaccord Genuity. Please go ahead.
