SaaStr AI Day Streaming Live: AI GTM Agents in Action with Attention's CEO

Okay, hey everyone, welcome to our next AI Day session. Really excited to have Anis and Jacob from AttenTION here, and I'm really excited for this presentation. They're gonna be talking about a guide to AI GTM agents, and we're gonna see a live demo from Jacob. So without further ado, I'm going to turn it over to Anish and please put your, drop your questions in the chat and we're going to get to them at the end. So feel free to put them throughout and then we will take questions in the last 10 minutes or so. And with that, I'll turn it over to you, Anish.

Great. Thanks, David. Great to meet you all. Hi everyone. I'm Anish. I'm the co-founder and CEO of Attention.com. So what we're going to do today is I'm going to walk you through a few slides about what we're seeing with some of the best-in-class, fastest-growing companies in the world that we have the honor to serve and how we've been able to support them in growth and help them gain additional growth leveraging AI agents for go-to-market. So we'll go over a few slides and a few outcomes and use cases. And then from there, what we're going to do is Jacob is going to walk you through a demo of Attention, and then we'll open it up to a Q&A. So let's talk about how AI agents are transforming go-to-market teams, not in theory, but in practice. So importantly, everything that we do today connects back into your CRM with no isolated data. We stitch everything together with your CRM properly, all of your client interactions from calls to emails, to all sorts of touchpoints. And then we fully augmented from there, giving your go-to-market org a real, what we call a system of cognition, a third brain. So this is why I always like to say it this way: attention empowers every ordinary company with extraordinary go-to-market superpowers. Today, AI in the market is moving very fast. Nearly 80% of the companies already use AI somewhere in their business. Actually, sales and marketing teams seem to be leading the charge in most of the, in most of the cases. So what we've noticed overall and what is shared all over the place is that sales teams and RevOps teams that use AI are 1.3x more likely to grow revenue. And the early adopters actually see rates jump 30% once AI becomes part of your workflow. More importantly, buyers also expect AI-assisted conversation. What does that mean? And Jason M. Lemkin always says this on Twitter. Every buyer now expects you to know them before the call starts. Sellers who don't adapt to these new best practices are actually simply left behind. So let's talk outcomes. Across our incredible customer base with some of the fastest and most incredible logos that you're seeing here today, including Scale AI, Elise AI, Engine, Clay, Groq, Bamboo HR, Bridge, and so on, we've seen 5x more efficient coaching thanks to AI, and more specifically thanks to Attention. 40% higher win rates, up to 99% increase in recap email sends, 60% less time managing opportunities, and increased forecasting accuracy up to 85%. In addition to obviously eliminating all of the low-value tasks, or at least most of them. And more importantly, these are not automation vanity metrics. They come from real teams augmenting human performance. So let's go over some actual real-life customer challenges and use cases and how attention helps here. Challenge number one is that only a small fraction of the interactions are actually being analyzed by clients. Before using Attention. And so what Attention will do here is that it will automatically categorize all sorts of feedback, competitive intel, feature requests, everything that actually your business needs to know from the voice of the customer to deliver it to the right person at the right place at the right time. And that could be anywhere going from your CRM to your Slack to an email report to anything or your data warehouse, right? And so the outcome here is that 50% more product roadmap alignment happens from real customer conversations and you know strategically where to move next. The second challenge is that deals typically slip through the cracks. Well, Attention's AI will detect the missing follow-ups, the deals that are stagnating, and why these deals are stagnating. And so how do you, how can you limit your lost closed deals? And then last but not least, forecasting accuracy. AI will detect your deal health sentiment and will also help you validate stage progression. And that helps you improve your forecasting accuracy. So, We'll go through a few of these examples, but the very simple outcome here is that you end up having a note around your deals, your product requests, your competitive objections, anything that you need to know actually in real time across your deals served to you via Slack or email or wherever you need to have it. The traditional approach is really simple, right? It's been in the past, hey, we're going to set up keywords in our call recorder and manually listen to the flagged calls with these keywords. And then we'll use that small sample size of data for the analysis. Attention actually sends this whenever you need it, either every single day or every single week or every single month, a very comprehensive report or even a whenever these things happen in real time. And so that helps you surface the unknown unknowns, right? And so no need to do any manual data entry, no copy paste, everything is given to you in real time. Same for coaching reps, right? The traditional approach has been to listen to your calls, typically someone in enablement or a sales manager, and then score subjectively all of these calls. But the reality is no one really scores these calls, right? Let's be honest about it. And so what happens here now is that you have data-driven objective scoring based on your best framework and custom framework. And so it also adapts to whether this is a discovery call or a demo or anything later in the process. Or also by team, right? Because you can definitely customize this by the team that is selling, whether it's mid-market or SMB or enterprise or any other sort of team. And the last piece here is that Attention will also score your deals based on your custom framework too, right? So earlier we talked about scoring reps. Here at this point, it scores your deals and it tells you What are some of the challenges that you're dealing with within your deal and some of the, the, the places where you need to pay closer attention to be progressing your deal further, right? And so traditionally, um, reps used to go with their gut feelings. Um, you could potentially rewatch some of the calls and try to understand what's happening, but here Tension will actually score your deals and tell you exactly as a leader, A, what will be closing this month or this quarter, but also B, where you need to step in to be progressing your deals. So why conversations with prospects and customers might be the next growth lever for you? Very simple. A, third-party data is commoditized, right? Everyone has access to the same intent signals. Hopefully you want your business to last for a while. And so what will happen is that you will need to use that first-party conversational data to follow up with prospects, right? Think about the entire closed loss pipeline that you've had 9 months ago. Now is the best time to reactivate it and to use all of this first-party conversational data that you have to follow up with your prospects and close them. This is also what's going to be used to, to, to, to hand off all the right information to your customer success team and your account management team so that you can grow and upsell your client base. And so the challenge is that most Conversational intelligence tools stop at the call level. Attention orchestrates this data at scale, surfacing what customers are actually saying across every deal, every region, every team, and across every medium. Jacob, I'll pass on the screen to you so that you can demo Attention. Very exciting, guys.

Amazing. How's it going, everyone? Really excited to show you around Attention. We'll kind of do a high-level flyby of the things Anice just chatted about. So let me share my screen. Awesome. So first thing I'm going to showcase here quickly is how attention helps you, A, just better work your opportunities, especially when it comes to making reps more productive, right? So the first thing you're going to see is probably something you've seen before. On the left, right, is the call. The talk time where Attention really starts to take it to the next level is over here on the right. What you can see happening over here is Attention is automatically parsing out all of the data that is required on our opportunity throughout the lifecycle of a deal. So what Attention's going to do is continuously update all of our custom fields within our CRM, right? It can be a sales methodology like MedPic, or it could be like a multi-select dropdown of a prospect's favorite color. The cool thing that Attention is doing, to Anice's point, it's not just looking at a singular conversation, right? If we take something like decision criteria, the decision criteria you're seeing right here is not based off of one singular conversation. It's actually a summarization of decision criteria based off all of the touchpoints we've had on this opportunity as of today, right? So this decision criteria could be a decision criteria for this opportunity through 27 calls and 30 emails. But if I'm a CRO, I'm, I'm able to see exactly what I need to see as of today as it relates to the decision criteria. So if I wanted to, I can click update records and now all of my fields in the CRM automatically start to be filled. Um, this can also happen, uh, fully automatically. So the rep doesn't even have to come in and click any buttons automatically. That data can just flow into the CRM after every single touchpoint. The next thing is with coaching. The first thing I want to call out are the tags at the top of these calls, right? What the tags are doing is helping you better understand what's happening in that conversation that can then make coaching way more effective. So what Attention was able to do was understand that this was a demo call based off of the interaction. Because of that, we actually get our demo scorecard, right? So if it was a discovery call, we would have a totally different scorecard, which allows you to grade the right conversations 100% of the time. So when you view this in aggregate, which I'll show in a second, right, you actually have statistically significant, significant data across how we demo, right? So what you can see here is how we did on this conversation based off of the framework in which we train our reps to do our demos. So I get a score and I also get feedback on what I could have done differently. When it comes to the conversation. So if I'm a rep, this is really great for me to come in and get better after each and every single conversation. It also almost acts as a map for your next call to focus on things to hit. And if I'm a manager, instead of having to listen to the entire call, right, I can just focus on execution on how the team to get better. So from there, I can actually go into aggregate scoring. Right, which I can then see if we're using that same demo scorecard, I can actually view our reps holistically to really understand as a team, where are we doing well and where are we struggling? Right. So for me as a sales leader, I know asking open-ended questions after showcasing features is the weakest part of our demo framework. So when I'm focusing on building enablement and coaching and training the team, this is exactly where I know I need to focus. To really make the team get better. So this is from a coaching perspective. Then like Anisa mentioned, from a deal perspective, I can now also see how good is this opportunity for our business, how likely is it to close based off of things that make a good deal for us, right? So something like CRM complexity is really interesting for us, attention and our prospects, right? Because if you have an overly complex CRM, your reps are probably not filling it in. So for us, that's really important, but for someone else, right, like it could be a prospect's favorite color, how many retail locations they have. So this allows you to get a very custom score based off of your business of how good an opportunity is, and you can see all of the touchpoints and all of the different information. So this really functions as a command center for all of your opportunities. The next thing I wanna showcase is kind of on the Agentic side of how do we get the right insights to scale? From our business. So we've made it really, really easy for our customers to deploy AI agents. All you have to do is come into our agents, then you just describe exactly what you want. So I just told Attention to create me a weekly report, a report that sends over friction points from our customers, right? And then link those accounts in Salesforce. So, you know, that took me about 30 seconds to type. All I did was add the tools that are required to do that. And now once a week I get this report that says, you know, Attention Weekly Feedback Agent, right? And once a week this gets delivered to me via Slack of everything I would want to know, um, around friction points with our product, right? So in, in today's world, I'm now able to set up these AI agents that pull me insights to scale across all of our datasets, whether it be Salesforce conversations, emails, pulled and sent to me really, really effectively and really, really efficiently. So this is one way that we engage AI agents where it's more of this proactive reporting, like once a week, once a day, send me coaching reports. The other use case is more reactive. Say you're in an executive meeting and someone asks, hey, why is our deal velocity slipping? Historically, that was a really big analysis where you'd have to loop in RevOps folks and it would be 2 or 3 weeks of going through call recordings and CRM data. Now what we can do is just go into our Attention helper, which can live in Slack, it lives in product, and you can just ask, right, identify the most recent closed won opportunities with the highest ACV and what are the main characteristics of those deals. What Attention was able to do was go through thousands of conversations and all of our CRM data to deliver me the insights I need, right, in a matter of minutes. And it was able to tell me my top deals by ACV, right? And what was causing them to move quickly and convert through our funnel, right? So this can be win-loss analysis, competitor analysis, top talk tracks, top objections, really anything you'd want to know in real time. You can go into Attention and Ask and it'll analyze thousands of conversions, emails, CRM fields, anything that you want to connect to it to get those insights in real time. So that was kind of the quick flyover of Attention because we have limited time, but happy to show anyone around personally in more depth if you're interested.

Thanks, Jacob. One of the best clients that we see coming in very quickly are typically the ones who have tried to stitch things together using, let's say, their call recorder like Gong or Fathom or any the other ones that they use, and using also some workflow or agent builders, and eventually they end up hitting walls. And so they end up doing it with attention, taking literally a few minutes. So let's go back to the last few slides before we move into Q&A. So why not just go all in today, right? Typically what we see is, first of all, AI transformation is not a light switch, right? We— you do need to get your data cleaned up, which is something that we end up doing extremely simply and fastly because of our expertise there when we onboard a client. And the change— let's say the change fatigue is real, right? Like too many dashboards kill adoption. The other thing is adoption follows trust. You need to start with one simple pain point, one simple bottleneck that you have, and then you prove out the value and then you expand. And so some of the best clients who are able to do it, they start with one simple challenge, which typically starts at, hey, I need to start filling the CRM with all of our customer-client interactions and connect it properly. And then from there you start as you clean up all the data in your, the vertebrae that is your CRM, things start going better and better and faster and you can start having increasingly powerful agents. And last but not least, process first, tech second, right? Your AI will amplify both good habits and bad data. So make sure that you have a good process before putting in AI. And that's typically where we can help a lot because we've seen a lot of these best practices. Thank you, everyone. I'm open for Q&A.

Thank you, Anis. Thank you, Jacob. That was super interesting. I have a couple questions actually from the livestream, and one of them I thought was particularly interesting around rep adoption. You know, sales reps, the question says, sales reps are often skeptical of AI coaching. You know, do you guys have any stories of how you get buy-in, especially from top performers? Who feel like they might not need this?

Yeah, that's a great question. There's a couple of things. So first of all, AI coaching typically is more helpful for managers and enablement teams who need some more data-driven info on how their reps are doing, right? The AI coaching won't replace your manager. It will just make them a lot more efficient at coaching the reps and have all of that data to go into the rep and say, this is what the data objectively say. And very often reps, you know, when you see it practically, the reps will reject whatever a manager will say, claiming that this is too subjective or whatnot, whereas having actual data-driven info will help managers better enforce whatever they get, the insights that they get from the coaching. Cool.

There's a question here in the chat about context to train a new agent when it comes to implementation. Are there certain things that work better than others when it comes to that implementation and training?

Yes. So typically we will start with what you have already existing. Which will upload, get uploaded into the Attenzion platform as the ontology. And then from there, we'll start collecting more and more data around your close-wons and close-loss and what works best. And you can then take that context and put it back into that ontology, if that makes sense. And so you start having that flywheel of data around your pipeline and how this pipeline progresses over time. And that actually makes it better to— that ends up giving better recommendations to the system.

Well, I got another question from the livestream. You cited 5x coaching efficiency, which is quite a remarkable number. What are the implications of that? Can a manager handle 5 times more reps? Or is it that the coaching is 5 times better?

Yeah, it's the first, right? It's basically that the, the managers are able to coach a lot, 5 times more reps. And so that specifically came from Abridge, one of our clients. And then we ended up and they had incredible growth, right? They went from 12 reps to 120 reps in a year. And so their leadership was able to coach a lot more reps through this hypergrowth journey.

That's pretty awesome. Another data point that people were jumping on was the deal slippage, right? 25% reduction. I think that's the number you guys cited. Do you guys have any examples of that? Like how that works in terms of, you know, a deal that was slipping and an action that remedied it?

Yeah, a couple of ways, right? The first one is seeing that there's a lack of, a lack of touchpoints within that deal, right? Let's say 7 days, no touchpoints, uh, or 10 days, no touchpoints. And so helping reps to, uh, go in and follow up or have some of that, you know, exec-to-exec, uh, touchpoint. Uh, but also the other thing is deeply understanding what is missing in your, uh, within some specific deals, right? And so a very clear example is you, and this is a story that a customer gave us, was that they were about, you know, a deal, they were forecasting a deal for a quarter, a specific quarter. And then that deal was, the rep was saying this deal is going to happen at the end of this quarter. Leadership was saying that it was most likely not going to happen because of what they were seeing in the score. Eventually, um, the, the leader was right and the rep was wrong. And so they gave the feedback to the rep to go in, build a business case for the CFO. And the problem was specifically that the CFO had rejected it for that quarter, and then it was won within the next 60 days, right? Um, and so this is how it helps you, uh, understand your pipeline better by having that bird's eye view on all of your opportunities and understanding what reps can do to accelerate their deals.

Yeah, that's super interesting. I mean, it's, it's almost, it's related a little bit to the sentiment analysis you talked about, I think, maybe, you know, which is notoriously pretty nuanced. How accurate is your AI, right, in detecting sentiment, especially in like a complex, you know, B2B conversation? You described one there with multiple stakeholders, right?

Yeah, the first thing is you need to get your data right. Right. So what does that mean? You need to capture all as many touchpoints as you have. So calls and emails and so on properly connected to the CRM. And then from there, Attention will be able to capture everything that you have. But if you get the data layer correct, which is also where we help, And at that point, the accuracy is incredible. One of our clients actually was saying that he spent a lot of time pulling reports in Salesforce and analyzing data for multiple hours. And then what happened was that Attention's agents— this is another thing that I would say is worth mentioning— is that Attention's AI agents have also the ability to adapt to your go-to-market stack, right? So even if you're data is not 100% correct, the AI agents are still able to pick up some information that is, you know, lost somewhere in space, as long as it's connected to something. And so that one leader was saying that within a few minutes, he had prompted Attention's helper to give him the same outcome of that report. And it ended up being more accurate than all the hours of work that he had done previously.

Super interesting. I think we have time for one more question, and I have a good question that's also— that's coming in here. Where do you see conversation intelligence evolving in the next 2 to 3 years? Will AI agents start taking actions autonomously based on conversation insights, or How do you see this playing out?

Yeah, so it's really interesting. I think a lot of the previous generation was seeing conversational intelligence as the product. We see conversational intelligence as one of the inputs that gives you this bigger brain that we call the system of cognition, or, you know, your revenue operating system. And it's one out of many inputs, right? The other inputs being your emails and your, your dialer calls and so on. And so that conversational intelligence is just getting morphed into revenue intelligence and it will help you at the end of the day just be— have a more and more efficient go-to-market org.

Nice.

Cool.

So how can folks here get in touch with you guys if they want their own demo, Jacob? They have more questions, what's the best way to connect with the Attenzion team?

attenzion.com or add us on LinkedIn.

There you go. Good. Well, thank you guys so much for joining us today. Thank you everyone for joining for this AI Day session. If you are staying for more sessions at sasterai.com, you can see the rest of the schedule for the upcoming sessions. And Anis, Jacob, thank you for the great presentation, the great demo, really interesting session.

Awesome. Thank you, everyone. Bye.

All right. Bye, everyone. Thank you.