Transcription
Let's talk a little bit about the use cases that we use AI for on the marketing organization and on a daily basis. Now, 90% of our market organization are using AI on a daily basis. We have about 450, you know, marketers on the Snowflake team, you know, around the world. And we have seen, you know, in the range of, you know, time savings for a lot of different tasks that we've been able to use, you know, AI for instead.
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We are live. Hello everyone. We're very excited about this next session. It is how the AI era has directly impacted marketing and sales with Snowflake. It's been a great day for Snowflake so far. This will be a very timely session. So, so excited to have Chris and Denise here with us today. For those who maybe don't know our presenters, Chris was employee number 13 and the very first sales hire at Snowflake. He recently retired as CRO. Because he now advises Snowflake and is on the board for about eight or so companies, many of them AI startups, which he can talk about too. And after taking three other startups public, wow.
Denise joined Snowflake as employee number 120 back in 2016, and she is still actively the CMO. She's also spoken at Sasha before, so we're so great to have her back in the mix and bring Chris so they could do a bit of sales and marketing. Together, they have solidified the data cloud market category, which has resulted in many great things for Snowflake, including the largest software IPO in the history of Wall Street in 2020. Denise and Chris are going to cover a lot. They've got a lot of great learnings they're going to share today about getting into the IPO with sales and marketing and also how AI has impacted all that. So, a lot of great learnings they're going to cover today. But for things they don't cover, for things you want to know more on, they just released Make It Snow. So, this is the book to read, recommended to all of you here today. It captures all the lessons, the near misses, and stories in the book. It's available everywhere. But we're again, we're super grateful to have them here today to do a deep dive on how this has impacted in the AI area and the go-to-market lessons you can apply with that. Chris and Denise, thanks for joining us.
Super excited to be here with the Zester community today. It's always fun to be on the event.
And likewise, thanks for having us.
Let's jump into it. You've got over 10,000 customers, so let's kick off with setting the tone for the day. How do you succeed with so many customers and with AI in the enterprise?
No, I think we wanted to start just by talking what are the things we are seeing out there in terms of which customers are succeeding with AI, you know, how are they succeeding? And a lot of these learnings are applicable to us here at Snowflake internally as well. And the first thing is around, you know, company culture. Culture matters in a big way. It's really a make-it-or-break-it factor for AI success. You really need to have a culture of curiosity and an environment where people are really encouraged to experiment. And it's really been true here, you know, at Snowflake as well. And on the marketing team, we have an AI council with representation from every marketing function. And their job is really to go out and learn, you know, from others and test new use cases, you know, for their function. And on a quarterly basis, we host an AI day for marketing and then the council shares what they have learned with the use cases that we plan to implement internally more broadly and also all the tips and tricks for how to use, you know, Gemini or, you know, ChatGPT on a daily basis. So we have that quarterly forum for the entire organization, marketing organization, to learn it from the council. What we haven't seen working well is really to go out and ask everyone on your team. Go to tell everyone to go out and test new things. It really creates a lot of unnecessary duplication of efforts and also chaos, you know, as well. So we ask the all leaders to really identify those who are, you know, super interested and really curious about testing, you know, new use cases and new tools, you know, across all functions. Again, we're definitely seeing kind of the best ideas, you know, coming from, again, those who are closest to the problem. But it's also important to have an executive mandate. At the same time, if we look at, you know, Snowflake's customer base across the board, those companies that are the most successful are those who really combine that top-down leadership with the bottom-up, you know, innovation. And if the CEO doesn't put AI as a top strategic initiative, you know, for the company, it's not going to be seen as a priority for employees either. So, it's really important that AI needs to be one of the top priorities really coming from the CEO. If it is perceived as something, you know, optional, you know, you're basically sending the signal, right, that this isn't strategic enough for our organization. But the real transformative impact is really coming from when you embed AI into your core business and including it in the development of new products and customer experiences.
You know, what we found, you know, AI hit came super fast, like a fast-moving train out of nowhere. And and so at Snowflake, you know, we had a lot of customers asking a ton of questions about how they were going to deal with this AI train. And so as we started to, you know, partner with our customers to help them deploy AI within their Snowflake environment, the thing that Snowflake was super hyperfocused on since our earliest days was making sure that we stored the data and protected the data that they want to do analytics on in a centralized location in Snowflake and then making sure that it was trusted and governed. And I think, you know, that's key to, you know, to this day of, you know, customers are, especially large enterprises, are super nervous about sending their data out to random AI tools out there because they don't want personal identifiable information, PCI data to make it out to these AI tools that that then can get published to, to who knows who. And so what a lot of what we had the conversations from the earliest days is making sure that you had a data foundation that that was ready for AI. And so, so really to this day is partnering with all of their customers, 10,000 plus customers, on making sure that there is this solid foundation of a secure, organized data set that is is really ready for the enterprise to then take advantage of AI.
You know, AI is only as good as the data that it gets. And a lot of times customers in the large enterprise, Snowflake enables any large language model that the customer wants to use to get access to the data, but the customer has to approve those large language models. And so, you know, if an end user, you know, within a large enterprise decides to bring in a non-compliant LLM, Snowflake will lock that down and not let them do that. And that's really by by design. And so, and so what's super important is that, you know, the data that that the customers are getting is something that is allowed to be accessed by the AI tools and the quality of that data is good because also, you know, using these large language models can get expensive. And so making sure that the tools are being used in an effective manner against the appropriate data. And so I think, you know, what we focused on at Snowflake is initially building a data warehouse and now Snowflake has has built this all-encompassing data platform that can do structured, unstructured, semistructured data and allow customers to do, you know, not just analytics, but AI, apply AI across all of the different data sets that sit and reside within Snowflake.
All right. So let's talk a little bit about the use cases that we use AI for on the marketing organization and on a daily basis. Now, 90% of our market organization are using AI on a daily basis. We have about 450, you know, marketers on the Snowflake team, you know, around the world. And we have seen, you know, in the range of, you know, 90% you know, time savings for a lot of different tasks that we've been able to use, you know, AI for instead. Two of our bigger projects that we've implemented include two agentic models that are specifically built, you know, for marketing. And one is a campaign agent that is helping us run all our campaigns. It doesn't automate in every step of our campaign process, you know, yet, but it really provides, you know, real-time ROI data on every single campaign, you know, we have running and it's helping us in real-time to optimize, you know, all our channels, especially our digital ad spend that we can really optimize in real-time, which has been pretty game-changing for us. It saves us a lot of money and has increased the ROI of our ad spending significantly. It also helps us with all our customer and prospect segmentation as well.
In addition, also we have built a complete, you know, agent and it gives it's really been developed both for us on the marketing side and the sales team. It really gives us real-time answers on how to position Snowflake in the most effective way in every compete situation and it um gives all the talking points back to the sales team. If they're saying, okay, I'm competing against this company for this, you know, use case in this company, the agent will give them the whole talking points back. And that's pretty game-changing. And I think many of you here in B2B can relate that it's really hard to enable both the marketing organization and the sales organization to compete against every single competitor at every single, you know, use case and at every single industry level, for instance. So that agent has been pretty game-changing, you know, for us.
We also use AI for use cases like, you know, pipeline forecasting. That's a use case that we have been running for years. And also in B2B, the ability to be able to forecast exactly where your pipeline is going to be six months from now. That has been game-changing for us. Also, many of you in B2B can relate, right, that you're suddenly in the quarter and you realize, okay, we don't have enough pipeline in, let's say, California this quarter. And from a marketing perspective, it's pretty much, you know, too late at that point, right, to do something about that. Every initiative, every program we're implementing now is really all about, you know, building pipeline for the quarters coming ahead. That ability to really see where pipeline is going to be in the next kind of six months, that has really allowed us to then completely then reallocate and resources investments to make sure that every territory is in good shape. Also for lead scoring, we're generating millions of leads now on an annual basis. So the ability to score those in a much more granular and effective way, that has been really game-changing in terms of just optimizing our whole, you know, journey as well. Of course, like many of you, you know, we're using AI in for assisting us to create, you know, copy. We also use it for localization. At Snowflake, marketing owns localization for the entire company. That includes, you know, documents and for product and everything. So localization has been a big use case for us both from cost efficiency and also from a speed of execution as well. Also drafting interview scripts, video scripts. We do a lot of, you know, customer, you know, interviews. We have our own TV channel, Data Cloud. Now, for them, they have seen that they're saving 90% in terms of time savings for creating their scripts and preparing for interviews and everything. And also mentioned that everything around digital ad optimization, that has been really a critical use case where we now really in real-time can see how channels are performing and will be able to, you know, shift to different, you know, channels in in real-time. And also start out talking about our AI marketing council, which has really been instrumental to the success here. We started out by really identifying those folks that really leaned in a big way and that we're really curious in terms of experimenting, you know, with AI. And that's about, we have about 30 people on that AI council representing every function. And that also alleviates a lot of stress for the rest of the team. But they know, okay, we have a team who's in in charge of this on the marketing side. I can go on with my day-to-day work. And then again, on a quarterly basis, we have that AI day where we're kind of rolling out new tools and use cases to the team.
Jeez, I want to ask you a few questions that have come in from the chat and the live stream just on a few things you said here. That's okay. Before we move on, sales. The AI council that you've mentioned, like who started it originally? And then how did you determine, you just mentioned it's cross-functional now, like how did you determine the folks that should be who started it? And then how did you determine the folks that should be on that council now?
Yeah, it was something that was initiated by myself, you know, from the beginning, but it's led by Hillary Corpio, who was also run, she's been instrumental, is also a person who loves to kind of innovate with new technology, that's kind of she's really passionate about that. So she was really the best, you know, leader to run this group. And then there were a lot of people that raised their hand. We announced we're launching the council, who would like to spend around 20% of their time to really dive deep into this. And again, these folks that join the council are similar to Hillary, right? They were really, you know, curious and excited about what AI can do for their function. So they get to spend about 20% of their time just on on looking at what are the use cases we should implement. They also collaborate as a team because also, right, you cannot not just do all this in isolation, right? AI has impact on the entire team and also sometimes the entire company. So a lot of the new technologies we implement have to go through security review processes, for instance. It needs to meet, you know, our governance regulations, you know, you know, as well. So, but again, most of these people, I would say all of them, they raised their hand. And we we send them to different conferences, right? They're attending events like like today. So we also invest in their in in learning, right, for them to go out and learn from peers, go to conferences and and then test new things.
And related question, does the AI council also own all of the AI budget, or do they just have a say in how like the budgeting and tools that are used?
Yeah, the good thing is that some of these tools and use cases are for free in some cases too. It's not always that there is a cost tied to it, but there is the budget is within the different departments. So let's say if there's an application for the creative team, that budget is most likely in that team. So they, it would be, yeah.
Got it. Makes sense. For the agentic models that you mentioned, so you guys have two, one that is able to chat with your marketing team more so and report on campaigns. So you mentioned ad spend, you mentioned copy. Is that a proprietary model that you guys have set up, or did you use any third-party tools to help you set that up at Snowflake?
Yeah, that's a great question. These are proprietary models based, you know, using Snowflake Cortex and then we're using various large language models to build these agents that could be, you know, it could be Anthropic, you know, could be, you know, OpenAI, you know, and other models, you know, as well. But we, again, we're of course a little bit unique situation that we are a data and AI company ourselves. But the for that reason as well, we need to push the limits more. There was a lot of these use cases that we're developing. We're also taking them to our customers as well. But these models are developed by our, we have our own intelligence team, you know, here at Snowflake. And we actually, it's a shared team between all our go-to-market functions. A year ago, we had all, we all had our own intelligence and data teams, right? We had one within sales, there was one in marketing, everyone has their own. But also we saw that there were some duplication of work there as well. There are some specific competencies that we wanted maybe use across the board. So this team is now a shared team across all of GTM and they're really focused on helping us develop different, you know, agents for instance for front department use cases. But these two are specific to marketing. And Chris is going to share as well what we have on the sales side later.
Yeah, no, that makes a lot of sense to to consolidate, not to duplicate efforts. I have to add, I haven't looked at the dashboard in in a couple of months now. I don't think I ever want to see your dashboard again. The ability to just go in and interrogate your data. I can now get answers to things that often had to slack someone, hey, can you look into why did this happen, right? Or can you share this data with me? And now I can just go in and ask absolutely any question of all our data and get, you know, answers, you know, back. So it's very much like it's very much like ChatGPT, but for all your internal information. And we actually, this product, right, that is a Snowflake product, is coming out in GA in November. So there will be a big announcement on November 4th. Anyone who's using Snowflake is going to be able to use this agent for their use cases as well.
Okay. Yeah. I think it's great. You guys were your own first, first users, right, of okay, we'll build it for ourselves and then if it works and it's safe, we'll roll it out to other customers zero, right, on this.
Exactly. Yeah.
Yeah. Yeah, there's a related question for that science team you mentioned that's now consolidated that's building these in-house models. Can you share a little bit more about the composition of that team? Is it mostly product? Is it more AI technical people? Do you also still have salespeople on it to be more forward deployed? Can you share a little bit more about that?
Yeah, again, this team is run by Anita Tasi, who's our Chief Data Officer at Snowflake. And and no, there are no marketers or anyone from the sales team. They work, they have people assigned to work with us to truly understand, right, what are the business problems we're having? Right? They need to really be, they're embedded in our organization. They don't report to us. So I hope that answers the questions. They actually worked with in our teams before, right? So they they know the individuals on the team. They have been living, right, you know, the day-to-day work, you know, we do here both on the marketing and and sales side. But they are now centralized under one team. They're mostly coming from a BI background or data scientists. So it's a combination of data scientists and also product folks, not Snowflake product managers, but product folks from from their developing data and AI applications, right, that that we use internally. So it's product, data scientists, and and analysts.
Yeah, to to Denise's point, like we used to have a very siloed type of data teams within each group. And and when Shreer came in, one of the things he brought in was Anahita as the Chief Data Officer. And we took any kind of data analyst or business intelligence people that were on my team and moved that into this centralized team. And, you know, Anahita's job was still to support Denise and I from a business standpoint, but really, we consolidated those sources. So there was not any siloed applications and stuff like that, which was really helpful as we scaled out the organization.
Yep. A question that will take us straight into the sales use cases as well, which we'll get into next, and you guys can both speak to this. Is there still a RevOps team at Snowflake, or has this new intelligence layer replaced it?
Yeah. Yes, there is a RevOps team. There's still a ton of work for them to do, but the intelligence team is kind of there to support the RevOps team. So, think of the RevOps team as the business stakeholders, the people coming to the data office saying, I need these things. And they, and then they collaborate with the data office to make sure, and this intelligence team, they collaborate with the teams to do that. So, so I think that's all really been helpful because again, anything that, you know, Snowflake deployed in sales was deployed in marketing, and marketing and sales were looking at the same data that maybe the CFO was looking at as well.
Okay. So, you know, as Snowflake started to scale out our organization even more, you know, and I'll give Shreer Ramaswami, Snowflake CEO, a ton of credit on helping us, you know, pay attention to what's important. From the pre-sales engineering team, we used to call them sales engineers, and they're now called solution engineers. And one of the things that we did was, you know, treat, asked a good question, how do you know if your sales engineers are good? And so we, which I couldn't really directly answer when he first asked me that question. And so one of the things we focused on was certifying every single sales engineer or solutions engineer, all the way up to the senior, the person running the organization. So it wasn't just like the individual contributors in the, you know, running on all the sales calls. It was every senior leader, fourth-line leaders and below had to actually get certified. And that was really important. That was an important first step on building out the organization and the technical credibility of the solution engineering team. And then as we started to do that and we certified, you know, there were some people that were that that excelled at that certification, others that realized, oh, geez, I need to, you know, up my skills. And that was, and we obviously helped them do that. I think one of the cool things is now we have a really technical pre-sales or solutions engineering team. And so in just six weeks, we were able to roll out Cursor AI to create custom demos and custom content a whole lot faster across the entire solutions engineering team. And that allowed for iterations in the field. So, so as you all know, AI is moving so quickly. So making sure that the people that are in front of customers giving them tools that allow them to customize and change on the fly is incredibly important. And it's important that you have that skill set in the pre-sales team. So that was a really a big thing. And again, I will give Shreer and and Snowflake's solution engineering leader, Mo, a ton of credit on on really bringing that to to bear and bringing that expertise in that.
So, so as Denise indicated, Snowflake is always, you know, customer zero. And so, you know, as we looked at rolling out different solutions across Snowflake, you know, rolling AI out across Snowflake, we looked at areas where we could actually save, you know, time. And I think, you know, the way I view AI in a lot of ways, and I don't want to offend anyone, I view it as a task automator. I think it's something that if there's mundane tasks or things that aren't that that humans have to do, I think AI is doing a great job of automating that. And I think there's, as Denise indicated in the beginning of the of the presentation, there has to be a return on investment. And so I think, you know, as we roll out tools, we have to actually see cost savings as well. Otherwise, it won't live in the real enterprise. Customers won't buy something just because it's AI. And they initially they would, but over time, that's changing. And you feel that across the enterprises. There are some tools that are, hey, they're cool, that I'm using it, but it, what's the actual business reason I'm using it? Is it generating more money, or is it saving me more money? That those are the two things that, you know, ultimately we feel we're seeing in the enterprise. And that's important for everyone to kind of recognize.
So, you know, as we rolled out, you know, our, you know, AI tools across our global support team, we saw 418 hours per week saved. That's a big deal. That's a huge. And allowing our support engineers to be to do things that matter more with customers. So again, on the task automation things, it was incredibly important. And as Denise said about dashboards earlier, Snowflake built a go-to-market assistant called Raven. And if you go talk to any sales leader, you talk to anyone that interfaces with a a a customer at Snowflake, they literally can go to Raven and ask questions of Raven of like, "Hey, I'm going to see XYZ customer. Tell me about, you know, what's happening. Give me a 360, you know, view on, you know, how are they, how's their consumption going? Are they a happy customer? Is there a bunch of support tickets? What opportunities are they looking at from a use case to adopt Snowflake? And or what are the detractors of Snowflake?" And there's a lot of stuff because it's looking across structured, semistructured, and unstructured data. It's really giving the person that's interacting with that customer really a full understanding of what's happening. And so it's a, it's also a productivity gain because, you know, prior in prior lives, you'd have to go through multiple systems. You'd have to go to a dashboard over here, you'd have to go to Salesforce over. And it wasn't as easy to find that information. But now instead of doing that, going to that dashboard, going to this, you know, Salesforce or whatever application, you're looking at this centralized tool that we call Raven, that's built on Snowflake intelligence to really help give the sales team a more accurate view and real-time view of what's happening with in the customer. So it's important also to note that like because I and I talked about this earlier, because Snowflake focused on security, like our customers are super worried about the security of their data. We also focus on making sure that the these tools that we use internally that we will roll out to our customer base, they're governed and they're highly secure. And again, I have to overemphasize that customers, especially a large enterprise, care so much about that. So, and so does Snowflake because we have these obligations to our end-user customers. And we want to make sure that we're protecting their data and our sales teams are governed with these tools. So, I think it's great to give a tool like Raven out to these customers and allowing them to interact with the data in real-time but in a secure and governed way.
Yeah. And Raven is used across all departments by our leadership team as well. So another use case is, you know, our co-CEO Shreer Ramaswami, right? He probably meets with at least 10 customers every week. And the sales team would have to create this five-page, you know, briefs for him right before, right? What the ask is for Shreer, all the information about the customer. Shreer, he just brings up his phone, right, 30 minutes before he asks the questions, and he got all the answers he needs about that account. And I think when a lot of people think about Snowflake, people think, but you know, data structure data, that's for this, right? And with Snowflake today, you can query any type of data. So you can query images, you can query videos, you can query PDFs, you know, documents. So for with Raven, right, you can ask any question of essentially all your intelligence in your company. That's great.
Before we go into guys's closing thoughts, so you guys both mentioned like AI and tools across sales and marketing that they're using. Janice, you've got this, um, you said on this slide of, you know, 90% of your marketing team is using it daily. How do you guys govern the tools that individuals are using? Because you also mentioned experimentation of how people can use AI. So, how much of that comes from Snowflake and how much of that do you let folks implement themselves if they want to use it?
Yeah, again, at Snowflake, first of all, security is the number one thing for Snowflake and keeping our data, you know, of course, customers in a data. It's the one thing that is the top priority, you know, here. So like Snowflake and many other larger enterprises, we cannot just go out and implement, you know, any application, right? They have to go through security reviews here. And that's why, again, it cannot happen in isolation here. Maybe they can go and experiment with different applications, implement in production at scale. It takes longer time here. And it's very similar, right, to many other larger companies. And I just came back from Ad Week in New York yesterday. The number one priority and that everyone talks about is of course, you know, privacy, right? The trust you have with your consumer in regards how you're using the your data when they are actually being so generous with you giving the data to you.
Absolutely.
It's really that trust you have between vendors and consumers. There's going to be there's nothing important here from an AI perspective. So no, we cannot just implement any application in production, you know, at scale. It takes time here to get those reviewed. There are many AI applications that are already built on top of Snowflake, right? Those we can use immediately. And what we're seeing more and more now is that those AI applications are being developed directly on the data. So there are hundreds of different applications just in marketing that are developed on top of Snowflake. And that is what customers are asking for because especially for larger companies, they don't want to now their marketing departments, sales departments just to go loose and bring in hundreds of applications, right? It's a nightmare to manage from a governance standpoint. So we're seeing more and more of those applications, again, being built directly on Snowflake and distributed through our marketplace.
One more question for both of you before you get into final thoughts, which is how have the AI era impacted hiring across sales and marketing?
Uh, I think what's most important today is to look for people that are really always eager to learn, that are really curious about trying new things. If we have learned something over the last couple of years, adaptability is a superpower of business today. You need to be able to adapt fast. You need to embrace, you know, change. You need to be a lifelong learner and curious. Since everything, look at skill set. It's more important to hire for aptitude. You can learn the skills today. If you're a curious life learner, you can learn everything, right? It's changing so rapidly. Anything. So the things you know five years ago are not, you know, relevant today. That's why I'm looking for aptitude. I'm always asking people, how do you learn? How do you go out and advance your craft, right? How do you innovate yourself? Those are the questions I ask. Those are the type of people I'm looking for.
And I just would add that, you know, I'm now officially an advisor to different companies now. And I advise a lot of AI companies. And what I'll tell you is that there's a ton of, especially the younger people, they're super interested in working in companies that are AI relevant. And I think, like, you know, of the folks that used to work for me at Snowflake, they're all, you know, Snowflake is a really AI-centric focused place. They're, it's, they're able to hire, you know, wonderful talent. And I think that's important. And I think, you know, I'm advising another small company called Factory.ai. And and Factory, you know, I, they just hired a head of sales. And the same thing. He's, he thought he came from another company called and he thought it would be hard to hire at early phase. And he's like, I have no shortage of of people who want to come work at an AI-relevant company. So I think it depends on how you market your company. I think Denise obviously has done a wonderful job of working with the executive team at Snowflake to making sure that Snowflake is well positioned in the AI world. But there's a ton of interest. If you're an AI-relevant company and you have a good story, you have a return on investment, as we talked about earlier, people in general are wanting to come join the AI revolution. And you go into San Francisco nowadays, and it's incredible to see, you know, how many AI companies are out there. It is the mecca for AI. And it's, it's super exciting to see San Francisco come alive right now.
Yeah. Just a few, you know, closing thoughts here and a summary from the conversation. Again, I, we talked about people a lot. In the end of the day, right, it's people making, you know, AI, you know, happen. And, you know, identify those, you know, change agents, those are really that are curious, you know, to lean in from all your different departments and have them kind of lead the way. But at the same time, it's so important to have a top, top-level endorsement and engagement, you know, as well. Again, there's no AI strategy without a data strategy. You need today to have all your data unified in one place, you know, governed in order to build these AI experiences on top of your own enterprise data. And also, again, leadership really needs to put AI as a top priority. I also advise some companies, sit on board of companies, and there are some companies where the leadership demand doesn't come from a leadership level and nothing is happening in those organizations. The CEO really needs to say this is one of our top priorities, you know, for the company.
Yeah, and for those who missed it, Shreer actually did a session at our last AR annual event. If you guys want to go back and watch that one, he also talks about AI. It's a really great companion piece to this one if you haven't seen that one yet. And then I know we're just at about time. And so for folks who have missed anything in this deep dive or want to learn more, just a reminder, Chris and Denise just put out the book, Make It Snow, so you can grab it now. It's available everywhere. It's makeitsnowbook.com. And with that, Chris and Denise, thank you so much. I know we've had, we've been so grateful to have Denise, you come before Disaster Annual and Street this year, and it's so great to always hear from the Snowflake team and what you guys are doing and innovating on and AI in the space, especially with data and keeping it all safe. So, thank you so much. And for any folks that want to maybe get in touch with your teams, what's the best way for them to do that?
Yeah, LinkedIn. It's probably the best way to reach me. Thank you all for attending today. We really love the SAS community.
Thanks again, Chris and Denise. We'll see you again soon.
Thanks. Bye-bye.
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