Lauren Silberman: Thank you. I also want to ask about throughput. So one of the key initiatives being the right cadence of digital orders, to what extent have you rolled out that specific initiative across the system or what percentage of the system do you see opportunity to improve that labor allocation?
Brian Niccol: You’re referring to the digital make line and our smart pickup times. Yes, it seems like it’s about half of our restaurants right now. And we’re seeing great outcomes from that. We’re being more on time, more accurate with our digital business. And where we have the correct deployment or aces in places on the frontline, we’re seeing some nice improvement in number of entrees per 15 minutes. So we still have work to do, though, on executing the deployment on the front line, so that people don’t leave their position. But for the most part, we’re seeing really nice progress on the on-time and accuracy on the DML. And we’re seeing some throughput gains on the frontline. But I think there’s opportunity for us to get even better as we keep people in position during the entire peak that they’re faced with.
Lauren Silberman: Great. And just you’ve historically I believe talked about peak throughput being high 20 or low 30 orders per peak 15 minute period. Have you worked to return to those levels? Is there room to exceed prior peak throughput as you now utilize a second make line for a much greater level? Are there any constraints at that high 20, low 30 order level across the restaurant? Thank you.
Brian Niccol: The good news is there’s no real constraint. We’ve got the opportunity to exceed that. Obviously, when we were doing those numbers that was when the entire business was off the frontline. So the good news is no bottleneck. The other really piece of good news is if that does occur, we got a significantly bigger business than what we have today. So we think there’s a lot of room for growth. We just got to execute this throughput program with excellence on the frontline. Just with all of our team directors, frankly, this morning and it’s the number one initiative on everybody’s mind; great people, great food, great throughput. We do those three things we’re going to continue to drive growth from operations.
Lauren Silberman: Great. Thanks so much.
Operator: Our next question comes from John Ivankoe with JPMorgan. Please go ahead.
John Ivankoe: Hi. Thank you very much. The question is related to throughput, but I think it’s more specifically on unmet demand. And I was wondering if your data specifically on the digital make lines side showed how much unmet demand that you actually may have based on the wait times that are quoted to customers? In other words, they get to the end of the transaction, they see a wait time and they just don’t complete the transaction. Is that something you can measure? And related to that, do you have a sense of how many stores actually are at capacity, maybe still in midtown Manhattan and in some other places where people do walk by a line that has 15 or 20 people in it perhaps and just go to a place that’s less. Is there a way to kind of quantify as you see it today the amount of unmet demand that you would serve if you could serve? In other words, if you were kind of fast or if you order time to less, what have you, just the opportunity on your current store base?
Brian Niccol: Yes. John, we don’t have the ability today for the in-restaurant, although we’re talking about it. We’re talking about some of the tools that there may be some ways for us to capture the data, not only in terms of like how many customers are waiting at the end of the 15 minute period, which that would be the opportunity, as well as how well we’re executing on the frontline. So we’re talking about developing those tools. We do know, historically, though, when we drive faster throughput, that we do flow more people not just through the 15 minute period, but we get a lot of incremental transactions as well. So historically, we know that people do — they walk away from our lines. You can see it anecdotally. When you’re in the midtown restaurant, when you see a long line and you see somebody walked by, open up the door and then walk away, that’s a lost transaction. We’re not able to quantify that specifically, but we know it happens a lot.
John Ivankoe: Do you know that on the digital side? Your transactions that kind of get to the final like percentage of transactions that maybe aren’t just completed that are right at the point of payment. I don’t know if that’s exactly the way to look at it, but there must be a leading indicator to some degree?
Brian Niccol: We do have that on the digital side. And your related question was how many restaurants are at capacity? We were able to flex capacity in the restaurant by adding staff. So we will have between one and four people on the DML. And so if you have a very, very busy restaurant on a digital line, you will have as many as four people on that line, including one dedicated person that’s going to run an order back and forth. So we have very few restaurants and very few individual periods within our restaurants that are maxed out from a digital standpoint.
John Ivankoe: Okay. And clearly you can see the faster you are the more customers you serve this current set of [indiscernible] proved that once again. Thank you so much.
Operator: Our next question comes from Brian Mullan with Piper Sandler. Please go ahead.
Brian Mullan: Thank you. Just a question on loyalty. Could you talk about some of the key near-term objectives the team is focused on with the program over say next 12 to 24 months on the path towards I think the ultimate long-term goal of greater personalization over time, which Brian I think you’ve referred to in the past is still a big opportunity?
Brian Niccol: Yes, sure. So the team is focused on taking all the analytics and the insights that we are seeing and figuring out how we commercialize those learnings in a way that’s very personalized for the individual. So a simple example, the suggestive sell. When you get ready to check out, if we know historically you do buy a Mexican coke and we don’t see a Mexican coke in your basket, the suggestive sell will be from Mexican coke. And then we see when we do that, we get a higher take obviously on the suggestive sell. So it’s simple things that actually we know we can commercialize, done in a very personalized way. And that’s what the team is centered on is how do we do this throughout the user experience from the moment you enter your ordering process, the moment you’re trying to pay on your way out?
And the good news is the team’s got a lot of analytics that we’re cranking through, and we’re knocking off the things that we think are the highest leverage points over the next call it 18 to 24 months.
Brian Mullan: Okay. Thank you. And just wonder if you could just update us on the dual sided grills, maybe how many locations that have been rolled out to? And are the benefits proven to be what you might have hoped inside the stores? And if so, when could this be rolled out more broadly?
