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 Anise and Jacob from Attention here and I'm really excited for this presentation. They're going to be talking about a guide to AI GTM agents and we're going to see a live demo from Jacob. So without further ado, I'm going to turn it over to Anise 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, Anise.

Great. Thanks David. Great to meet you all. Hi everyone, I'm Anis. 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 and 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 touch points. And then we fully augmented from there, giving your go to market org a real, what we call a system of cognition, a third way. So this is why I always like to say this way. Attention empowers every ordinary company with extraordinary good market superpowers. Today, AI go to market is moving very fast. Nearly 80% of the companies already use AI somewhere in their business. Actually sales and marketing teams are 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 rev ops 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 the workload. More importantly, buyers also expect AI assisted conversation. What does that mean? And Jason 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 Elite, AI Engine, Clay Grok, Bamboohr Abridge 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 sense, 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 are stagnating and why these deals are stagnating and so 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 flat 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 report report, 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 places where you need to pay closer attention to be progressing your deal further. Traditionally reps used to go with their gut feelings. You could potentially rewatch some of the calls and try to understand what's happening. But here attention will actually score your deals and tell you exactly as a leader, a what will be closing this month or this quarter, but also be 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 intense 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 nine 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 Anise 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 is 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 Anisse's point, it's not just looking at a singular conversation. 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 on all of the touch points we've had on this opportunity as of today. 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 able to see exactly what I need to see as of today as it relates to 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. This can also happen 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 touch point. The next thing is with coaching. The first thing I want to call out are the tags at the top of these calls. 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. 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. When you view this in aggregate, which I'll show in a second, you actually have statistically significant data across how we demo. What you can see here is how we did on this conversation. Based off of the framework in which we train our rep to do our demos, I get a score then I also get feedback on what I could have done differently. When it comes to the conversation. 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, I can just focus on execution, on how the team to get better. From there I can actually go into aggregate scoring 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. For me as a sales leader, I know asking open ended questions after showcasing features is the weakest part of our demo framework. When I'm focusing on building enablement and coaching and training the team, this is exactly where I know I need to focus on to really make the team get better. So this is from a coaching perspective, but like Nisan 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. For us that's really important. But for someone else it could be a prospect's favorite color, how many retail locations they have. 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 touch points and all of the different information. This really functions as a command center for all of your opportunities. The next thing I want to showcase is on the agentic side of how do we get the right insights to scale of 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 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 attention weekly feedback agent. And once a week this gets delivered to me via Slack of everything I would want to know around friction points with our product. In today's world, I'm now able to set up these AI agents that pull me insights to scale across all of our data sets, whether it be salesforce conversations, emails pulled and sent to me really, really effectively and really really efficiently.

Right.

So this is one way that we engage AI agents where it's more of this like proactive reporting. Like once a week, once a day send me coaching reports. The other use case is more reactive. Like say you're in an executive meeting and someone asks like hey, why is our deal velocity slipping? Right. Historically that was a really big analysis where you'd have to loop in rev ops folks and it would be two or three 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 identify the most recent Close 1 opportunities with the highest ACV and what are the main characteristics of those seals where attention was able to do was go through thousands of conversations and all of our CRM data to deliver me the insights I need 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 using also some workflow or Asian 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 and 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? 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 let's say the change for T 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 custom client interactions and connect it properly. And then from there you start. As you clean up all the data in 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 an 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 to Q and A.

Thank you. Nice. Thank you, Jacob. That was super interesting. I have a couple questions actually from, from the live stream and one of them I thought was particularly interesting around rep adoption. You know, sales reps, the question says, are 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 perform who feel like they might not need this?

Yeah, that's a great question. There's a couple 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 the whatever that they get the insights that they get from the coaching.

Cool. There's a question here in the, 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 get uploaded into the attention platform as the ontology. And then from there we'll start collecting more and more data around your closed ones and closed 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, from the live Stream. You cited 5x coaching efficiency, which is quite a remarkable number. What are the implications of that? Can a manager handle five times more reps or is it that the coaching is five times better?

Yeah, it's the first, right. It's basically that the managers are able to coach a lot, five times more reps. And so that specifically came from a bridge, 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 ways. Right. The first one is seeing that there's a lack of, a lack of touch points within that deal, right? Let's say 7 days, no touch points or 10 days, no touch points. And so helping reps to go in and follow up or have some of that, you know, exact to exact touch point. But also the other thing is deeply understanding what is missing in your 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, 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 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. And so this is how it helps you 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 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 touch points 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 agents, this is another thing that I would say is worth mentioning is that attention 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 in, you know, lost somewhere in space as long as it's connected to something. And so that that one leader was saying that within a few minutes he had prompted the attentions 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 two to three 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 your revenue operating system. And it's one out of many inputs, the other inputs being your emails and 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 have a more and more efficient go to market order.

Nice. Cool. So how can folks here get in touch with you guys if they want their own demo. Jacob. Or they have more questions. What's the best way to connect with.

With the attention team, attention.com or at 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're staying for more sessions@saster aid.com you can see the rest of the schedule for the upcoming sessions. And Anise, Jacob, thank you for the great presentation, the great demo. Really interesting session.

Awesome. Thank you, everyone.

Bye.

All right. Bye, everyone. Thank you. It.