Turn Every Conversation Into Actionable GTM Data

Julia Nimchinski [ 03:33:40 ] So, you know, hey, we’re going to have to, you know, waste your time doing this. Thank you so much, so much, Sebastian, Glenn, please, everyone follow expanding and our amazing experts. And we are transitioning to our next demo. Welcome Sandy from Momentum guys. Thank you. Our next demo is gonna show you how to turn every conversation into actionable GTM data. Hi, Julian. Hi, Justin. Great having you here. Thank you. Very nice to meet you both. Thank you for having us. Sure. Let’s step into the demo and then transition to the questions. Cool. So I’ll start showing screens right now. And use that screen. And excellent. So my demo will go a little bit through Slack, a little bit through Salesforce.

Santiago Suarez Ordonez [ 03:34:45 ] And then I do have some slides that I would love to kind of set the stage on what it is that we actually do. So it’s only five minutes long. So bear with me. What momentum does, we call it Enterprise Listening. The core thesis is anchored on how much value exists in the conversations that are flowing through any go-to-market organization. From customer support and customer success, talking to active customers to sales and as they’re stopping the prospects. So many gold nuggets flow through a half an hour conversation, which is prospects and customers are volunteering, incredible information, their thoughts, what they think about you, your competitors, your product is happening. And the biggest challenge you have is your company struggling to extract it, to tap into it because there’s people in the process.

Santiago Suarez Ordonez [ 03:35:38 ] The way we present the problem is by saying that companies struggle to listen to their customers and take action. Of course, at scale, which is an insurmountable problem. When you think about some of our customers, C-scaler has in the order of thousands of people taking calls every day, tens of thousands of calls every single day, in which useful, relevant stakeholders who have done research on their own product, who have used the product, who depend on the product, who have compared it with competitors. And so I think it’s really important for us to think about that. And so I think it’s really important for us to think about that. Come in and tell them all these insights. Now, how do I get access to it is the question.

[ 03:36:16 ] When you go through people, you end up seeing a lot of bad patterns that start happening. And when I talk to my own personas, my own buyers, this is the kind of insights we get. Their CRM is cluttered with bad data. Their forecasts are inaccurate. Product is complaining that they don’t feel like they know what’s going on in the field about product, what they’re doing, what they’re doing, what their requests are coming. You usually get this challenge of some reps being very loud about what they need, while other reps don’t say anything. So you end up skewing the product towards the loudest reps and the deals that they’re running that day. Then you get caught by surprise with a customer that churns, even though they’ve been telling you all about it for nine months.

[ 03:36:59 ] And then terrible handoffs between the different stakeholders from SDR all the way to customer support once the deal closes. There’s handoffs of data. And all of these is interesting, because they’re not the same. They’re not the same. They’re not the same. They’re not the same. All these problems can be solved if you had a way to extract the right insights at the right time from what your customers have told you. They told you how to forecast a deal, because they tell you if they have a budget and an owner and a timeline. They give you feedback from product. They tell you that they’re disappointed. It’s just hard for that data to drip through the organization effectively. And that is what we solve. Now, how do we solve it? You may ask.

[ 03:37:39 ] We bring AI into it. As it turns out, large language models are an incredible tool to blow through mountains of words. We tap into the recording of all these conversations and we transcribe it. We read everybody’s emails. We check your send us tickets or intercom chats. Every interaction that is happening with your customers and your prospects, it’s analyzed by LLens. And when we detect those very specific insights, you are looking for, then we allow you to build automations to deliver that to the right places. It could be inside a field in Salesforce. It could be in a channel in Slack. It could be in a table in Snowflake. It really depends on what the customer needs for the data, which is act as a pipeline that lets data flow through. So we look at things like automating workflows, improving forecasts, preventing churn amongst many things that our customers find momentum for.

[ 03:38:40 ] If I left you with one last slide, how to think about us. What makes us different? If you have been buying sales tech, you may notice that there are always a dozen or two or three dozen vendors already in place for most companies. And one of our strongest qualities is the fact that we’re not really another product. We don’t require rapid adoption. You don’t need to train your whole team. You don’t need to train your whole team. You don’t need to train your whole team. We’re not a new web app, a new tab that they need to open in their browser and not learn to use. And perhaps more importantly, the value that we deliver to our customers is not limited to the adoption that they’ll be able to drive inside their team.

Santiago Suarez Ordonez [ 03:39:28 ] Momentum lives underneath your team and it lives underneath your tech stack. We integrate with everything you’ve got going on. It could be Send us tickets. It could be Chorus call recordings or Gong call recordings or Gmail emails. Everything flows through Momentum automatically without anybody clicking a button. The Momentum applies all its extractions automatically. And then the data gets placed in Salesforce or Slack without anybody being involved. And this means we reliably deliver this data. The key insight we have is we believe that humans in the process of collecting this data, they are a liability. They’re a bug in the system. They’re not a feature. So we’re doing everything in our power to cut the people from the process and they are happy. Reps don’t want to do admin.

Santiago Suarez Ordonez [ 03:40:17 ] They don’t want to take notes. They don’t want to update Salesforce. And they don’t want to have to interact with another product. So we sell Momentum as a data pipeline and not as a new product. That’s a pitch. Now I’ll take you into the demo. I appreciate you bearing with me there. We will start with, uh, Salesforce. I’ll show you a picture of what life can look like once this is put in place, then I’ll take you into how this is set up, um, from an admin standpoint, but I’ll open an example opportunity in a CRM. And if you’ve ever looked at Salesforce and opportunities, you either received a deal from somebody who closed it, or you looked at a forecast on a deal and you dive into trying to understand more.

Santiago Suarez Ordonez [ 03:41:03 ] I’m pretty sure that you’ve never seen the picture you’re looking at right now. This picture is a picture of a sales force. I’m sure you’ve never seen the picture you’re looking at right now. This picture includes real data, thorough notes captured on every specific quality and aspect of this deal. What is their decision-making process? What pain point are we addressing for them? What impact can our product have? Uh, you know, what metrics, who’s the economic buyer? What is the paper process to buy or who are the champions? All of this information is rarely captured by reps because they just don’t have the time. They’re spending their day taking calls, jumping from one to the other. They’re spending their day taking calls, jumping from one to the other.

Santiago Suarez Ordonez [ 03:41:37 ] And they’re not really able to sit down and write four-paragraph answers for every single insight in the CRM, no matter how useful those insights could be to the people that come right after them. Every single call the momentum analyzes also generates a note. So a pretty thorough note of every interaction is captured. It almost feels like pedestrian to be talking about AI note-taking in 2024. But in reality, if you do it right and you place the notes in the right place, and you allow for a high degree of customization on what those notes could have, it can be incredibly powerful for any team of any size. So that’s what it looks like in Salesforce. I’ll real quick jump into what it looks like in Slack. I know we’re getting close to time.

Santiago Suarez Ordonez [ 03:42:26 ] In the world of Slack, it’s all about messages. So we have channels where POC calls or discovery calls are pasted on the same node that we post into the CRM is captured here, which means the team is going to be able to do the same thing. So we’re going to be able to do the same thing. So we’re going to be able to do the same thing. So we’re going to engage with it. They can read it without having to jump into Salesforce. Momentum is also sharing all the fields that it extracted and it wrote into the CRM so I can review them and make any corrections. It also suggests me a beautiful follow-up email to send to my customer based on the conversation. And I can open it right in my email to send over.

Santiago Suarez Ordonez [ 03:42:57 ] One click, I tweet the draft, and then I send it. And then perhaps more interesting is: I can come into Slack and ask a question about the call. Like, hey, Momentum, what’s going on? What’s going on? What’s going on? Momentum, what was the general sentiment from the call? And get a good answer based on what was discussed. The sentiment was positive and collaborative. Jeff expressed interest in how Momentum can address their challenges with pipeline visibility. Real-time answers, right on Slack, not having to switch tools, which is very powerful. And then I guess for my last few minutes before I run out of time for the demo, I’ll show you how this is set up. Because this is not a prebuilt, everybody gets the same kind of output tooling.

Santiago Suarez Ordonez [ 03:43:40 ] You go into Momentum and it’s built on the concept of prompts. LLMs will analyze your calls and you have to prompt LLMs to tell them where you want to put the data, like a Salesforce field, and what data you want it to extract. So in this case, you can see an extraction we put in place where we told Momentum that we want to capture the close loss reason for a deal that got close loss. When a deal gets close loss, we want Momentum to analyze every single interaction we had with that customer and give us reasons on why we lost it. This is amazing. The quality that you get out of this analysis versus a human sitting down on a deal that just lost is dramatic.

Santiago Suarez Ordonez [ 03:44:21 ] So this is how you set it up for something like close loss. On a similar vein, we would be wanting to identify, for example, a deal that has potential for expansion. So I would ask Momentum, hey, has the customer explicitly or implicitly indicated interest in acquiring additional seats or purchasing new features not included in the current tier? If the answer is yes, then you come to Momentum and tell it, hey, I want you to fire off this Slack message. I want it to include expansion, potential details, what are the next steps on the call. And then I want it to be delivered to a channel like the Sales Channel. You can, of course, push this to a person. You can push it to.