Name: How I AI: Top AI Plays from Canva and Motive
Uploaded: 2026-04-23T20:40:08.416Z
Duration: 48 min 32 s
Description: How I AI: Top AI Plays from Canva and Motive

Transcript for "How I AI: Top AI Plays from Canva and Motive": I think it's live now. Okay. Tell us if oh, yeah. Got it. Tell us, folks, if you can hear me finally, and it's not just the whole music anymore. Alright. Thank you, Hannah. Amazing. Appreciate the confirmation. Folks, we have had the most incredible technical difficulties today, so I sincerely apologize. Thank you so so much for being so patient and understanding. One of the privileges of of, working with a audience in the technology industry is that you know that we are, more prone than most people to tech difficulties because we try to do the coolest things, and that is certainly the situation here. So thank you very much. Let me introduce myself. I'm Sunny Manivaran, founder and CEO of Pure Bound. I could not be more excited, to be joined by two incredible customer marketers, customer marketing leaders, Chris Van Anderson from Canva, as well as Liza McGraw, from Motive. And one of the things that we really want to do with today's webinar, which is titled How I AI, is demystify AI to everybody, here. And I don't know about you, but I certainly feel like every single day I hear about all these amazing things that people are doing with AI, and I feel behind. I think a lot of us feel behind. And, really, the point of this, webinar is to give you some use cases that you can deploy yourself, within your companies and show you, hey. Here's how you can build your own and how you can also work with, you know, external solutions like potentially Pure Bound or others, to go, you know, do great things as well. So without further ado, Crystal, I would love to have you introduce yourself quickly to us and tell us maybe what was your first moment with AI? Alright. Hi, everyone. I'm Crystal Anderson. I am on the customer marketing team at Canva. I think my moment for AI was that I was helping run all the reporting, and I was manually pulling all of these advocate metrics and pipeline influence data, and I'm sure everyone's familiar with that. It was taking me hours, every single week. And so on a whim, I basically uploaded our data into Cloud Cowork and never looked back. My moment was that realization that I wasn't just saving time. I was, it was allowing me to actually spend more time on other projects that would really push the needle. So, I am humbly joining you guys on this webinar. I'm not going to pretend like I know all the ins and outs. AI is changing every day like Sunny said, and I've seen some pretty sophisticated AI projects and models that my peers have put together. But hopefully, I can help steer you in a starting direction. Amazing. Well, thank you so much for joining us, Crystal. And, Liza, I'll turn it over to you. Give us a quick intro, and then what was your first moment with AI? Yes. Hey, everyone. My name is Liza McGraw, and I am a senior customer marketing manager at Motive. And, Motive might not be as well known as Canva, so I can do a quick intro on what Motive is. But Motive isn't the only AI powered operations platform, which is a mouthful, that unifies safety, asset tracking, compliance, and spend management in one single solution. So, our customers were our platform's helping, our customers save lives and be less distracted on the road. So it's been really, really rewarding as a customer marketer to be able to tell that story and promote the customers that are making a change on the road every single day. And I would say that my moment was really probably what everybody else's it was is that taking, like, a recorded customer call and asking AI to write that first draft of the case study in the social copy, that was something that used to take me hours, probably, like, ten hours to create. And I was so thrilled to be able to spend my energy on a piece that actually required judgment when writing. So, you know, like, after the first draft. And so we were actually one of Pure Bound's first customers about two years ago. So seeing how Pure Bound worked, and how just it was just eye opening to me that we could do this so quickly. And Pure Bound's truly just gotten better and better for us over time. And so I would say one, the content creation piece, and then the second piece is similar to Crystal's where it's, you know, the data analyzation aspect. I remember thinking that I was, like, a developer two years ago when I figured out that I could export all of the g two reviews in a spreadsheet, throw them in the chat GPT, and ask ask it to surface the most, like, positive and negative takeaways providing like, provide graphs based off of, who is writing review, what their titles are, all those things, and being able to hand that over directly to product and sales and engineering in a matter of minutes instead of spending probably a week on what I used to do. So, really, the time savings and the data analyzation piece was absolutely my first moment, and I can't believe it's been probably, like, two years now of trying to build workflows since then. And I am absolutely no expert either, but I'm really excited to talk about two projects that I am excited to share and hopefully that can resonate with everyone else's line of business too. Okay. Amazing. Well, thank you for that. And, yeah, excited to hear, your stories. Very quickly, let me just share pure what PeerBound is. For the audience, I think most of you know who we are. And really, we were founded in 2023 because I felt that, with LLMs and AI at large, that there was a huge opportunity to unlock and activate the intelligence of your customer base, in real time at scale. And I felt that every buyer was going to want to hear from your customers because we were all going to get bombarded with so much AI slop, AI stuff, whatever you wanna call it. And the only source of truth about your company that is completely trustworthy is what your customers have to say. And I felt that customer marketers were going to be huge winners, as AI progressed, and we're starting to see signs of that, you know, across, across the board. And so that's really what our software AI native, software solution does, is unlock and activate customer intelligence, for companies like AlphaSense, Braze, Canva, Databricks, and, and Motive, amongst others. And our three core values of the company are, you know, we want to dazzle customers, deliver great results, and then demand excellence from ourselves and each other, and from the world at large. So that's us in a nutshell. Obviously, you can come to our website anytime you'd like to. And, I wanna really spend most of this time, you know, having Liza and Crystal talk about their stories. So, Liza, you're actually going first. So tell us a little bit about AI and action at Motive and how you use AI to scale advocacy programs, for a 100,000 customers. And, I will, run your slides. So you just tell me next slide when you're ready. Okay. Perfect. Well, we can just go to the next slide so you can have. the next slide too because I did kinda go over what phone I've already done. Okay. Perfect. So y'all can just have a visualization of the project that I'm gonna illustrate. But, so as a team of one, my focus really is, of course, on delivering as much value as possible for our customers and for our advocates through opportunities that build our brand but also help us expand into new markets. And so, again, as a team of one, a big part of how I'm doing that is building AI workflows to make my work way more efficient and way more data driven. And so today, I'm gonna talk about, which I said a little bit earlier, two projects where AI has helped me analyze customer feedback at a much faster rate in a much larger scale, resulting in more of a customer centric outcome, which I know is the goal for all of our work for everyone that's on this call. And so we're gonna talk a little bit about, customer conferences. So as many of you know, customer marketing, we own many, many hats at a customer conference. And so we are deep into planning ours right now. And so something that customer marketing was tasked with in the beginning of planning mode was to help pitch ideas for the breakout sessions. So we are having 30 breakout sessions, and, it is, of course, product marketing's ownership, but they wanted customer marketing's input because we're talking to our customers every week. We've got great insights. And so I knew that AI would be able to help me generate my thoughts based off of current content, you know, if I pulled in our case studies or content that we have with our advocates in the advocacy program. But what was really important to me was that I was including our customer voice at scale because that really is the heart of the conversation. And so, luckily, Peer Bound has the coolest feature in the world, and it's called customer moments. And customer moments picks up on impactful customer quotes from recorded sales conversations, and it then tags each quote into a certain theme or category. And just for reference, we had 982 customer moments come in just in q one. So that is technically, like, 982 possible approved quotes that we could throw into our, testimonial deck if you got those approved. Like, that's a crazy number, and you don't have to do anything for it. It just flows in there. And so it's totally changing the game in terms of customer more voice at scale, and finding those customers that you might not have tapped into for other opportunities. And so I bring this up because there is an export feature. And so I exported all of the moments that discussed when a customer was prompted, either a new use case or they talked about something innovative or they discussed, like, overall positive sentiment or they talked about our AI capabilities. So I, segmented it on those those key themes and then I exported all of those quotes. There were 3,000 of them and it populated into a very detailed spreadsheet. And the spreadsheet had the account name, the customer's name, their quote, of course, and even the date and time of the call. And so I put that spreadsheet into ChatGPT at the time because that was our preferred AI. Now we're Gemini folk, but at the time we were ChatGPT folk. And, I titled the prompt, like, here are customer conversation snippets with account names. Please put these into eight key themes for breakout sessions at our annual customer conference. And then don't forget the most important part, please recommend three to five customers per session based off of their own words. And then that way, a document was populated. I had eight session themes populated because the team only asked me to do around 10 sessions. And it was grounded in what customers were actually saying and not what we assumed they cared about or what we thought because that we as marketeer marketers that sometimes does occur. And so we were able to finalize, you know, a new list of of sessions, and it had customers that I could already tap into. And most importantly, this was not based off of, like, the 50 customer stories we have. It included 3,000 customer inputs. And so I think that was, just a really great way to showcase that everyone is talking about this topic. And so, we ended up actually using from this project a lot of those sessions. So I was really proud about that. And, we lined up some of the speakers from this export feature as well. And so, that's just one way to do this export feature for, like, speaking opportunities. But this workflow also, you know, unlocks three different team team use cases as well. Our content team is doing this export feature as well. One of our content marketing manager, one of our our priorities is creating more content for a certain segment that we are targeting this year. And so she, went through moments, segmented it based off of that, fleet size. And she has been creating blogs so quickly based off of these customer moments because now she has, you know, a summary of what they're caring about, what they're talking about. But most importantly, she has the customer quote to back it up, and we don't have to go and find a customer to talk about what we were going or to talk about the topic of the blog. We're creating the topic of the blog based off of the quote that we already have. And so the only work we really have to do now is say, please, please, please, we wanna feature you in this blog. You're a thought leader. Let's put your voice out into the public, you know, what we all say. But that's, like, really the only work that we have now. It really just is helping us trim down the time of going to find someone. And so our content team has been really, really excited about that. And our product and research teams have also been using this export feature as well for research purposes. And they have been using this more so on, like, education. I always, like, laugh with my team and I say, my morning news every morning when I hop on my computer for work is to read the moments channel, and I educate myself on, like, what all our customers are speaking about for that week, and that's how I do my news. So the research team and our product team is doing the same thing. And so as a summary, like, the data in all of our companies, whether you have peer bound or not, which I highly suggest you should get, it exists in our companies. And so the prompt and having these customer quotes at scale and using the quotes at scale truly is the unlock. And so, this is just one, great example of using customers at scale, I think. And so I'm happy to kind of do a quick demo as well on that export feature for those that do have PeerBound. And I just wanted to show the output of what ChatGPT created, a few months ago for me. Go for it. And, while you're pulling that up, I think I'll just say a few words about this use case, which I really love. Having been on marketing teams in the past, I think planning for an annual conference is such a huge undertaking. And what Liza just talked about is just getting a first draft of a full agenda in less than an hour. And it's not just peer bound. Right? It's peer bound. It's chat GPT. It's you're building some things. You're buying some things, and it's really how do you orchestrate all these different AI solutions to go get answers really, really fast and, drive value for your business. And I think that's the sort of wow moment, that you can create within your companies with not a lot of, you know, crazy, you know, work that has to happen. And, Liza, I'll turn it back over to you. Tell us a little bit about this workflow, and and what the results look like. Yes, absolutely. If you are a Pure Bound user, so you go into moment types and you pick the type of moments that you want featured in your export. Of course, if you are creating blog content or you are creating what I did for for speaker breakout sessions, it's gonna be around AI hype, expansion, impact, sentiment, or use case, and not necessarily about people or partner praise or we don't wanna talk about our competitors. So, I picked the ones that, you know, were more future facing, and then I wanted to showcase what it produced. And so everyone knows how to put a spreadsheet into their AI of choice. But what it the outcome was, I was pretty impressed with. So this is the only prompt that I used, and then it came up with, this is a session that we actually did in implementing from this and it's building a scalable defensive driver coaching program. So for our customer base, our customers are, a lot of them, it we call it that the physical economy. So trucking and transportation, field services, so customers that are going the technicians that are getting into vans and going from site to site. And so and and oil and gas, I mean, we have, like, food and beverages. Really anything with wheels, they're our customers. And so, a whole department and category for us is creating safe driving programs. And so a lot of our customers do gamification when it comes to safe driving. And so this also was a great, it's great insight because, like, we do market that. And so it was a nice reminder that we are doing the right thing. We talked about why this matters, what customers are asking for based off these moments, and then breakout session ideas, like, for the creative topic or the creative name for every session, which we all know AI is great for doing certain tasks, but creativity is not really their strong suit. But it wasn't too bad. I don't think we didn't go with these, but it wasn't too terrible. And then, recommending customers that I can reach out to and why they work. And so I did this for all, nine sessions that I or 10 eight sessions that I asked it for. And I sent this this, document to our product and research teams as well and, events teams, of course, and we got some of those on the docket. So if you go on to our website for vision 26, you'll see some of those. Okay. So, yes. Awesome. So that's a look, that's a incredible use case, very real use case, and your conference is, I believe I believe is coming up in a month. Yes. Congrats and best of luck. And that is a very cool use case. And let's go to the next one, Liza, if you don't mind. And there's more to a conference than just the agenda and the speakers. In your case, there was also a awards program, Yeah. with what I understand was a fairly tight deadline. So this is not necessarily a peer bond specific use case, but it's a great AI use case. So walk us through what was the situation, and what was the challenge at hand, and, how did you how did you resolve it using AI? Yes. Of course. So, I was tasked to create a, awards program in a very, very short timeline. I was given six weeks to build our first customer awards program from scratch. And, you know, on top of other things at the at the event running breakout session, customer testimonials, etcetera. And customer awards programs are really important, and it was I could have said, we don't have time to do this, but I didn't want to say that. I wanted to have an experience for the customers that are doing wonderful things, great things, and I wanted to be able to celebrate them. So the only way that I could do that credibly and with the customer in mind, you know, being respectful of their time in the tight timeline that we're in was to make it a data based awards program. And so we, decided to choose the winners based off of the impressive platform data that we can pull, for a few categories. And so after aligning, with our executive leaders and the internal stakeholders, on the vision of what this would look like, I wanna go through the five steps of how I did this, just so it could maybe inspire others to do something similar. And it doesn't have to necessarily be an awards program. I think that you could follow these six, five steps, and do something for, like, using data for anything, for, like, a customer appreciation day or using that data to figure out a gap in your customer testimonials and and finding pipeline for new advocates based off of data. So I wanted to start with that. So maybe you could think of how this might resonate in your, with your priorities so it doesn't have to be awards because I know not all of us do awards programs. And most certainly not all of us do six week awards programs. I realize this is crazy. So, once I was happy with the the call, I, dropped that into into Claude, and I asked the Claude to, the prompt was build me a six weeks execution timeline with major milestones based off of this conversation. And so that I was I have not really used Claude very much. I just got access two weeks ago. So I was really impressed with the one pager that came back. It talked about every deliverable that I needed to have, every dependency, every stakeholder that needed to to help me execute this program. I dropped that into a Slack channel and then it became the North Star for our entire project. And so once I was happy with that one pager that included selection methodology, the roles, the responsibilities, the budget, the key milestone, I wanted to provide everyone examples of what winners might look like. That way the stakeholders that were involved because some weren't in marketing, they would have a better visual and perhaps a better understanding of what each award meant. Claude has access to our Google Drive. I asked Claude to provide examples of a winner based, provide examples of a winner based off of our already existing data and also based off of peer bound moments. Again, this export feature from peer bound moments really has changed my life. I threw that in here as well. That was a great way to showcase real examples for winners. I learned that Claude can integrate with Asana. Our marketing team relies heavily on Asana for every project. I had Claude create a new task in our already created com conference board, and I asked it create subtasks for each stakeholder with due dates based off of that one pager. And I was truly blown away about how Cloud was able to do all of this for me. It not only created a one pager based off of one thirty minute call, but had everything in it, but it also created the Asana board. From then, I had everything aligned organizationally. I worked with our business value team to build out a data model that pulled real platform metrics amongst the five categories that we're focused on. They weren't vanity numbers. They were numbers that proved you know, the customer's operation had generally transformed such as miles driven without an accident or number of fraud savings from our motive card, like real world metrics. And then once I had that spreadsheet, I had Claude pick the winners for me based off of that. So, really, I was able to create an awards program with the winners in technically one day's time, which is probably, like, a 99.9% savings from maybe doing this in six months. And so like I said, I wanted to showcase this project because I think it can be replicated for many, many projects that involve using customer data and being able to use that to celebrate people that are doing incredible things with your platforms. And so I don't have success metrics for this at the moment because we're still in the works, but reach out to me in six weeks. I'll let you know. Hopefully, there is proof in the pudding when I when I'm successfully finished with this. But, yes, this is my this is my second example. Amazing. Well, thank you so much, Liza. And, yeah. Look, I love this example as well just because it's just an example of how projects can suddenly land on your lap, and they are mission critical for the company. And now I think, you know, using AI and frankly having the right dataset at your disposal, you're able to get to, maybe not the final answer, but at least a very good first answer in a very short amount of time. And then people can react, you know, to something rather than having this be a fire drill that lasts, you know, many, many days, if not weeks. So incredible job. I think by now, all of us know that the Mortivision Conference is happening in a month, which is fabulous. So, very, I've been talking about for six weeks. Nope. yeah. Longer. Exactly. Three months. Exactly. All I've talked about. So we're all we're all in the journey with you, which I'm excited for. all for Exactly. Thank you so much, being in. the church. And now let's turn over to Crystal. Tell us about what you and the team are doing at Canva, to really create a top tier advocacy program. Alright. I see a comment about the slides. Are you guys able to see our slides still? Great. Okay. Folks can see at least Hannah, Spencer. Okay. Folks can see it now. Hi, everyone. If you're not familiar with Canva, I do have to step back and just walk through this. Here's a little context on where our team is coming from because the scale of Canva really shapes how we approach everything. Canva was launched in 2013 with one mission, make design accessible to everyone. For the past decade, we've really focused on individual empowerment, that B2C, getting design tools into everyone's hands, and we did just that. Over two sixty million people now use Canva every month, we have 31,000,000 paying seats, and 95% of Fortune 500 companies use Canva today. We are entering our second decade with an expanded mission, so in powering our organization to fundamentally change how work gets done. We're not just considered a design tool anymore, we are a full visual communication and productivity platform. That's the world that we operate in and that context really matters because running customer marketing advocacy at this scale with a lean team of three, is exactly why AI is not just optional for us. It's really, a force multiplier and it's part of our infrastructure now. So, my first use case that I want to talk about is the revenue impact dashboard. Before I dive in, I really want to tell you why it was built. I think that's very important to start. That's going to be actually the most valuable piece. Right? So the question that kept me up at night was, are our sales reps actually using the customer proof we create? I'm sure everyone else has the same nightmare as well, but we are creating those case studies, quote cards, logo decks, everything, reference content. And we really had no way of knowing whether any of it was actually being used, landing on actual calls. And so we wanted to understand the influence pipeline. Leadership really wanted that from our team, and so we wanted to know whether the work that our team was doing was moving revenue. All of this data already exist and Gong records every call, Guru tracks what content reps are accessing, Slack shows where reps are asking for proof from Peer Bound. The data is all there. It's just scattered across four different platforms that don't really talk to each other. I thought, what if I built something that brought it all together? I started building a GTM, Revenue Intelligence Dashboard in Cloud to really pull all these platforms together and get a more accurate view. We were feeding it information from Gong, Guru, Slack, Purebound, tying everything to influenced pipeline, rep activation, and recommended strategic place to really fix late funnel execution gaps. To start, there was a clear need and question that we needed to answer and that required multiple systems and data to be analyzed. That is when I decided to turn to AI for help. No engineers, no data team, no budget required, just me, a clear question that we needed answered, and Claude. Let's get into what this looks like. This connects to all of our platforms. It analyzes them together. Obviously, I couldn't show you all the metrics that it's pulling, so it's anonymized, but it really answered the question, is customer proof actually being used and is it influencing revenue? It has tabs for how much ARR is in play where proof has been mentioned on a call, which case studies reps are actually using versus ignoring, which reps are activating proof versus who's leaving it on the table, how Guru usage correlates with Gong proof mentions, and where all the activation gaps are. It really generates this AI power recommendations based on all of that in real time. If you take a look at this dashboard, there were basically five steps that I took. I basically started a conversation with Claude and just described the problem. Very easy. The starting point was not code, it was a clear description of our problem. Step two, I basically uploaded all of our data, did the connectors to Cloud. Cloud parsed every file, deduplicated across reps, isolated the specific trackers that we wanted to track, and really joined everything together. Again, no code was needed for this. I asked it to do the analysis that we really cared about and clarified details for Claude to drill down, spent some time in that. And then step four really layered in more data as I went. So we cleaned up the metrics that didn't hold up, and now we have the dashboard that you see today. So the main piece here that I wanna point out, there's a few, stats here, but the revenue impact tab, that headline number is where our total ARR is influenced by customer proof. That's active pipeline where proof was mentioned at least once on a Gong call. That's really proof in play in real deals. Right? And then that second number, closed won ARR with proof, is the only figure that I'll call truly proof attributed. Those numbers is it's basically a deal where a rep demonstrated using proof in the sales motion on a call that closed. And then the stat in the top right, the percentage of all q one calls with proof, that's our activation rate. For us, that rate was a little lower than we expected, and we really took a look at that. And that gap is exactly why this dashboard is super valuable and exists so we could see this data analyzed this way. And then these are the table at the bottom shows the closed won deals with proof touch so we can dig in and see the deal type, the ARR, and what actually happened, with that proof. So you can see there an upsell where ROI framed, tip the negotiation, and that came from a specific case study. And then there's another one where you can see the second one, a new logo where the prospect really self referenced a case study, and then the rep presented the full use case for them. These, again, are signals, not assumptions. We were going off of these high level reports from each of these respective systems, Salesforce, Gong, Guru, but tying them all together allowed us to gain this granular view of real proof influence and gaps. This is a second tab. I wanna get more to how this helps understand our gaps, and this tab is really where it gets actionable, for our sales team. So you can see the chart on the left showing proof calls by deal stage. You can see immediately where proof is being used and more importantly, where it's dropping off. The gap between meeting booked and negotiation is a late funnel activation problem, and proof is being deployed early but not carried through to close. That's something on us to enable our teams and train them on. Then you can see the table below shows an actual list of our top influence deals where we're able to see where proof is in play and how customer marketing can continue to support the sales team with additional enablement and customer proof to push that deal over the line. So we continue to build out these tabs, continue to dig in, and we are able to truly see these critical insights that is critical for our business. So the next use case we have is a peer bound MCP. We use the peer bound MCP, to map customer stories to accounts, which really lets our GTM agents, build in Relevance AI. That is a separate platform, not cloud. It sends personalized outreach automatically. There's three simple steps that you see here. So we first identify the lead when a prospect downloads content on Canva's website. Relevance AI analyzes who they are, and then the AI queries appear around MCP to find the most relevant story, use case, or quotes that matches that type of buyer profile. So if we're reaching out to a real estate company, it pulls the relevant real estate customer story. If we're talking to a tech company, it's gonna grab Salesforce or Stripe. That proof then gets inserted into the outbound email automatically, and the lead gets a targeted message, not just a generic template. This is what it looks like in practice. The copy creation agent is asking Pure Bound for social proof for Canvas. Specifically, here you can see B2B enterprise customer stories about marketing teams using AI powered design to scale campaign production, reduce agency spend, maintain brand consistency. Very specific prompt. The peer bound MCP will run multiple searches across our customer story database and pull the most relevant proof points by use case in persona. What I love about this is that the MCP extracts the key details and data points from these stories automatically. It does not require a team member to manually hunt through a content library. We describe the buyer in the context and the right proof surfaces. And the part that really makes this genuinely scalable, because we're global, when our team publishes a new customer story, it's immediately available everywhere when we use it. Automatic outreach, meeting prep, follow-up emails. We connect to the Pure Bound MCP once in relevance, and then every workflow that touches customer stories always has that most up to date content. No need to update a separate database or maintain another automation, it just flows. Once the MCP surfaces that organized set of proof points, this is what gets injected directly into the outbound e mail, proof that speaks the prospect's language from customers they recognize. You can see here that it's Stripe and that gets input into the e mail sequence. As Robert Jones, our Head of JTM AI put it, With the PureGround MCP, we can easily embed customer proof into our agentic workflows and establish that trust with buyers in the very first point of contact. This is the final product. Four emails, each one with a distinct angle, each offered, a relevant specific case study to their industry or their use case. The heavy lifting of finding that relevant case study is done. That's basically the through line of everything I've shown you today. I think I said this quote to Sunny during our webinar prep, and it's really the teams that figure out how to build the right engine, how to use AI to scale and signal discovery while keeping humans in the loop for the relationships and to review it before it goes out to the judgment calls. Those are the teams that are going to pull ahead. That's what we're building at Canva, and I hope that one of these examples for both me and Liza today gives you a starting point to build it at your company too. Amazing. Well, thank you so much, Crystal. And, yeah, two more incredible use cases. Although I would say the revenue attribution dashboard is not a beginner use case. Although I do think you've done such a phenomenal job of stitching together so many data sources. And then actually, you know, doing the thinking to say, okay. What is the real insight here? How is that gonna change how we operate, you know, within customer advocacy, and how do we help our company win? And I think there's just so much depth behind that one workflow. And then I'd love the obviously, you know, the MCP use case of getting customer proof to everybody that, you know, is on your website and downloading content. This is my dream. Just my personal dream for customer marketers is that customer proof isn't literally every single thing that your company touches. So whether it's, you know, early stage hand raisers who are just downloading a piece of content or, heck, even a job descriptions at your company. Here's why our customers love us and why you may wanna join us. And I see a world in which I think this stuff, you know, customer advocacy is really bringing all these insights to every single other team within the organization, and both of you shared some amazing use cases, and examples of using AI to do do just that. So, huge thank you. And, Crystal, I had a couple of questions from the audience. One question from, Chris was around, was your dashboard limited to seller usage or were you also tracking, marketing uses of proof like website, social, newsletter, and things like that? We were only tracking. It was just for sales. As we continue to grow, we are taking a look at pulling in more of that attribution for marketing campaigns as well. But because we do have our Salesforce dashboards specific to marketing campaigns, we are looking at two separate things with us. Awesome. That's great. And then, Alex had a question. Alex Eckhart had a question around what was your process of getting access to Gong calls from the sales org? How did you get access to to Gong? Yeah. So, I have an admin access, customer marketing does on our team, and we specifically started going in and pulling out what data that we needed. We definitely downloaded, all of the different signals, the keywords, that we were looking at. So we do take a look at how many times someone is speaking about use cases and how many decks. So, in Gong, you can also pull in and look for the title of a deck, specific to the sales org. And so that's what. we were pulling out and taking a look and, pulling into that dashboard. And then just, I know Emily had a question earlier about our MCP. Emily, you can see that our MCP is live. And, you know, I think you're gonna you're all working with software vendors. You're gonna see a slew of software vendors, release, quote, unquote, MCPs. And what a lot of them are doing is just having it work in cloud, which, of course, you know, an MCP great use case for that is working cloud. What we've done is, of course, we have the cloud use case, of people being able to chat with your customer proof data, but also other apps and other agentic workflows can be built with our MCP. And that really is when you start, you know, being able to stitch a whole lot of, incredible workflows together. And so I'm very excited to share more about that in the future. But, let's yes, Emily. Perhaps a different, different call, maybe a one on one call so we can talk about, MCP in more detail. But the world is the world is changing fast. So let's move to q and a. I know we have, you know, five or six minutes left, and we ask for questions from the audience. So I am taking, sort of the three most popular themes, and we came up with some questions. And I will ask Crystal this first question. How did you internally sell a buy versus build approach? Like, how do you guys even think about building, buying, sometimes both, and are there specific parts of advocacy that you think should be done by people manually? Oh, man. That's a great question. So, honestly, I didn't really sell it as a buy build versus buy. I sold it as what is the right tool for the right job. I would say our rule is buy where the use cases are already solved for and then build where it's specific to you. Pure Bound was a clear buy. You guys help with advocate identification, machine scoring, that integration also helped. But the dashboard that we built, that was a clear build because it was specific to our program metrics and tool sets. The internal cell for that, it was basically showing the output. I ran the initial dashboard, put that in front of leadership and said, This is what we can do with it. Here's what we built. That conversation became, how do we scale this? Are we looking at the right metrics? How do we optimize not? Should we do this? It's really, I would say, show the output every single time. Then on what's what should stay, my rule is simple. So Basically, use AI for scale in signal discovery and let humans obviously own the emotional layer. Relationship building is always human. We always say this, but AI can tell me who's ready to do something, speaker, reference call, but it can't be want to say yes. So I leave you with, I It's helps you run 80% faster. The final 20% sprint to the finish line is ours to get to. awesome. Liza, I'll ask you the second question. What resources have you found most helpful for for learning about how to use AI beyond content creation, particularly for customer marketers? Would love to hear your thoughts on this one. So I thought of resources. I was thinking and I have been a newly subscriber to Kevin Lau's customer newsletter, and he has really, really impacted the way that I'm going about workflows. I highly recommend following him on LinkedIn if you're trying to think beyond just the content generation piece into AI and more so on to life cycle strategy and how to get buy in for from leadership for more budget. He talks about that a lot and how to get buy in for leadership for why our jobs are more than just the case study people. He talks about that a lot. And he just posted a post on LinkedIn that, he is showcase or he's showcasing created over 100 Claude Code agents to assist with our jobs with customer advocacy and life cycle management. So I immediately said, check check out, put into cart, buy. I haven't put them to work yet, but I'm really, really excited. That's truly a gold mine, and I'm really excited to look into it. So, that's just a resource that I've really, really it's really helped my education around AI. And a lot of us in this community know who Kevin is, and maybe maybe Kevin's even on this call. If you are, thank you for your resources. But I've been looking into a lot of his, emails in his newsletter. I'm gonna share this tidbit with him. I think he'll get a kick out of it. That's really cool. That's really cool. And then, look, last question from the audience before the before the webinar. I'll give this one to both of you. Crystal, perhaps I'll start with you first, which is what's one AI workflow that you've actually scaled that moved a real metric, not just save time? And I know you have a handful of examples here. So I I would say it's not really the workflow, but the rep activation data from our revenue influence dashboard, we've been able to start working with enablement to increase customer proof deals, at the right stages. So because we can see how many calls each rep is doing per week, but then also overlay the amount of times that proof has been layered in throughout these calls has been really insightful and helpful for deal velocity and content gap. So it's allowed us to dig beyond the noise, faster closes. That's the AI that I hold up as proof. It's not just about efficiency. It's about that revenue impact. Totally. Totally. Liza, what comes to mind for you? I like this question because I think, of course, every time we touch any AI tool, we're saving time, and that is the biggest metric. But with time saved, we are also able to amplify our work at a greater scale, which affects the bottom line. So when you're saving time, you're also being able to be more profitable because you're accomplishing more. And, I just reported on our q one performance and, for case studies. And our case studies influenced the largest amount of pipeline through, SQLs more than any other type of lead. And I really do think that that can be directly attributed to the ask peer bound bot and the customer proof emails. And then I've also created a few Glean agents, that help our our reps being able to mirror a prospect's pain points, and it provides like a script based off of the case studies that we have. And so I think just like the tools that we've been using, our AI tools, Pure Bound, Glean Agents, what have you, it's really helping our sales team provide the right pizza content at the right time. It's really meeting the reps where we are. And so, I think just having that and then having the the data that I just reported on, it's really just showcasing that this is working, and I'm excited to continue to see what else we can build. Amazing. Well, folks, we have two minutes left. So I think I'm gonna cut this one at this point and just say a huge thank you, to Crystal and Liza for, giving us your time, your expertise, and just incredible work, you know, at your companies using AI for real practical purposes to go solve real business problems, and to go open, you know, really new ground for customer advocacy within within your companies. And if you're in the audience, I hope that this was, you know, certainly educational, but also inspiring because I think a lot of this is just a few clicks and a few prompts away. And, again, I'm very bullish on this function and this whole community, and, really excited for, all of us, and thank you so much for joining. So with that, we'll say goodbye for now, and, you'll get a follow-up email from us with the recording, and, hopefully, you'll join us for the next webinar as well. But thank you, Crystal, and thank you, Liza, and thanks everybody for joining us. Take care. Thank you. Bye bye.