Momentum Demo

Julia Nimchinski:
Thanks, and welcome back, Jordan Nettles, Senior Sales Engineer at our favorite tech, Momentum.
Jordan Nettles:
Hello, hello, thanks for having me.
Julia Nimchinski:
Our pleasure, how are you doing? What’s the latest and greatest?
Jordan Nettles:
a lot of, a lot of really interesting things here at Momentum, but I think everyone, here and across our viewer base, I’m sure, are excited for the holiday season where no one picks up the phone, so, we’re just having a good time You know, showing off the tech and getting customers, results with it.
Julia Nimchinski:
For sure, so many speakers mentioned your attack. On the summit, yeah, Mark Roberge, various CMOs, CROs this time around, so super excited to dive into it. And just one question before we do it, Jordan. What’s your top GTM AI prediction for 2026?
Jordan Nettles:
That’s really good. I, I think… I think I might be a couple weeks behind, but I really do see a shift moving from OpenAI and the clause towards Google as kind of the foundation model of choice. It seems like there’s some really strong indicators that that’s the way things are going.
Julia Nimchinski:
Very cool. Well… What you got?
Jordan Nettles:
All right. I’m gonna go ahead and share my screen. I’m gonna walk through, a little bit about Momentum, and we won’t spend more than a couple minutes in slides, but just giving a very high-level overview of the tech, and then we’ll jump into some of the newer technology.
Julia Nimchinski:
Awesome.

Jordan Nettles:
So Momentum, we describe ourselves as AI go-to-market, data orchestration. What that means is that we are specialists for collecting information that fuels your business, right? So the source of that information is certainly from go-to-market. It’s coming from the conversations that your reps are already having every single day, whether it’s a phone call, whether it’s… a conference in Zoom like this one, whether it’s email, certainly doesn’t matter from our perspective, but anywhere you’re meeting your customers. There’s lots of information that can be used in marketing, that can be used in product. In the age of AI, a lot of this actually comes from downstream… it goes into downstream agents that we’re building, right? So think about being able to build automations and build agentic workflows. All of this is downstream of what’s actually happening in the business, and so what Momentum does is sits underneath those conversations. pulls out all the information and sends it where it needs to go. There’s so many applications for where this actually ends up, and we’ll share a couple of those today, but in principle, one of the things that sets us apart from the market really is the infrastructure. So we’ve really heavily invested for the last 5 years, not just in building an automation tool that might take an email or a transcript and, you know, condenses it down and sends it somewhere. But really, building a context engine, right? Anytime I want to learn something about a deal, or a series of deals, or a segment in my pipeline. I’m not just asking it of a conversation, a transcript, like the call we’re having now, but I’m actually asking it of a very refined data set that comes from those transcripts and those emails and the CRM information. All of this is downstream from having very, very concise, clean data, and we’ve invested very heavily in all of the different ways in which we can make that data the most actionable. Last thing I’ll showcase in slides here. obviously, folks jump to, you know, how can we see why we’re losing deals, or how can I make sure that I have good visibility into my reps and how they’re adopting, you know, the SPICE methodology with Winning by Design, but… More than that, we can actually fuel marketing, we can fuel product. All of these other organizations in our business use the information being collected through Momentum, and we’ll showcase just a couple of those examples today and what that looks like. So as I transition here, I’m going to showcase a few of the different areas where information ends up, the types of things you can do with it, the types of things that our customers are doing to be best in class when building with this first-party data set that we’re providing. In Salesforce, this usually looks like something like methodology adoption. Think about understanding, you know, whether or not a rep is actually adhering to something like MedPick or Spiced. When we exist not just as an augment to a seller, but actually a replacement for some of the work they have to do, you can get to some really interesting things. So, if I see that a rep is not giving me decision process on one of their opportunities, or several of their opportunities. Well, in a prior state, it would be because, well, maybe they’re just not putting the information in the system, but I actually know that this is an enablement issue, because Momentum, were there to be decision process discussed, is actually collecting this information. It’s sending it into this area, it’s sending it to Snowflake, it’s sending it to Salesforce, so now I know, okay, there’s a gap, and it’s not in, are we putting the information in our systems, it’s actually in the deal itself. And we can help actually bolster pipeline by making sure we’re not missing out on these types of opportunities. But then downstream of this, a lot of our customers are automating things like their opportunity stages. So think about having, you know, your deal framework, you know, 15 entry and exit criteria points, all being listened to by Momentum to make sure that we’re actually collecting, the different components to be in the right stage. And so if a rep has called something in Stage 6 or Stage 7 that, you know, according to your own deal process, shouldn’t be in Stage 2 or Stage 3 even. well, we can call them on that, and we can say, hey, by the way, you haven’t started legal, you’re not multi-threaded, you haven’t locked in a POV kind of use case scoping document. And you can send that to the rep, you can send that to the manager, and this just means that when you actually go to call a number at the end of your quarter, it’s going to be significantly more accurate, because it’s based on your own methodology, because momentum underneath the hood is actually listening to all these different, points of your conversations, and updating everything accordingly. Now, there’s lots of downstream data that you can collect. Think about handoff notes for account executives to CS, and giving CS the information they need, and professional services what they need to operate. Or think about just getting data. Numbers, dates, pick lists. I can tell you every single time that a competitor has come up, and the date that they came up, and if that customer is vendor-locked with them until a certain time. And all of this I can only do because Momentum’s listening to all these conversations and collecting all this behind the scenes.

Now, how we actually collect this information is super straightforward. All we’re doing is we’re saying where we want to send the information. And when we want it to send. So if I’m creating a handoff note, I might have something like, great, every time my account team changes, my CSM moves on and takes over a new account or series of accounts. or my customer becomes a customer for the first time, we’ll look back at the calls and the email exchanges and perform kind of an analysis based on my own criteria, where I’m looking at information from the CRM, and I’m looking at information from you know, these calls and these emails, and this data set that Momentum’s put together. And so all I’m doing is pushing a couple of buttons and writing a prompt, and then now I have this information being delivered at scale. Right? Thousands of opportunities. You know, the RAMP team last quarter had something like 21,000 fields filled out by Momentum automatically. Just think about the time that their reps weren’t spending pushing this information to Salesforce, but actually spending the time on the phone closing deals. And moving to operate against, against their deals. One more thing here, quickly, to show, there’s so many different areas. Momentum typically is, you know, likes to describe as giving the right slice of information to the right team. And so, you know, in this case, it’s something as simple as, well, I want to write a handoff note, or I want to send information into my CRM so that, you know, sales managers and enablement have a good idea of what’s going on, or we understand closed loss reasons. But there’s other times where I have higher order challenges in my business. You know, tell a CRO how our qualification is. Tell a CRO, you know, how we are at discovery. For these, we use something deeper, called Deep Research. This is something we just, have had about a month and a half with our customers testing out an alpha. And think of Deep Research as the ability to go in and ask any question. With a high order value in our business. Why are we winning deals? Why are we losing deals? What product gaps are we experiencing? What are our competitors doing that, you know, isn’t bubbling up? And so here, I can just ask Momentum. In conversational language, you know, help build me a detailed drop-off analysis. And tell me why we’re losing deals at certain stages. And it’s gonna put together a research plan where it goes through and it performs all these calculations based on the CRM data and this different information that’s coming out of You know, these calls and these email exchanges, these text threads. And it’s gonna give me insight. It’s gonna tell me not just hey, you’ve lost this many deals in building pipeline, or this is how many deals were closed lost in discovery. It’s actually going to give me a breakdown. It’s going to tell me, actually, you lost… In Discovery, you lost 18 deals to timing. Makes perfect sense. That’s, you know, maybe not the most actual insight, but what is… Is in delivering proposal, we’re running into things like pricing being an issue. Now, pricing is something that usually should take place before we’re even submitting that proposal. This is something actionable that a CRO can go and take and say, okay, great, make sure before we actually send a proposal, we’re doing preliminary pricing, we’re understanding budget, we’re getting to these higher order traits. Now, underneath this, there’s several things I can do with this information. So, one, I can schedule a report once a week that, automatically delivers this kinds of insight. in a PDF, into Slack with breakdowns of, hey, here’s why you’re losing at this stage, and, you know, here’s the different components that could really make a difference. You know, it looks like you’re single-threaded in 19% of these closed-loss deals, and this is why this is an issue. Or, if I’m kind of in more of a RevOps persona, and I want to focus not on, not just on the ability to drill down into a chart that would go to a CRO, maybe I want to get in the numbers. And so, let’s say I want to look at something like, you know, how am I qualifying? I can give this big, meaty prompt in this, in this case, where I’m really trying to understand a very deep challenge, and just say, hey, rate my ban… help me understand budget authority need and timeline. How are we doing in the field? What are we seeing? And so one of our customers actually did this. And I can actually download this as a CSV, and I can go and I can see the reasoning for every single opportunity that my team has worked in a quarter. Could be hundreds, could be thousands. See exactly why it was rated poorly, why it was rated well, what’s going well, getting a full, deep analysis of each opportunity. One of our customers was having a major drop-off point in Stage 2. And so what they did using Momentum was they effectively looked back at something like an entire quarter of data, hundreds of opportunities, to understand which deals were falling out. And they had the idea that, you know, maybe this is qualification related, but What we were ultimately able to do using Momentum was give them the exact deals that were causing problems, so they’re able to look for deals that were lost in Stage 2, filter to only deals that were above $100K, and then were lost subsequently. And then look, did we talk about budget? Did we talk about authority? Right? If we’re talking to someone who doesn’t actually have the ability to make a purchase, is it something we should be putting in the pipeline as $100,000 plus? And we found something like 20 different opportunities that all were flagged as, hey, these are major problems, major bottlenecks that are dropping off that are making it really, really hard to forecast. And they were able to find the exact team, the exact opportunities, the exact reps, and able to go into all of that detail. Only of that’s… all of that’s only really possible when you’re able to marry, kind of, the conversational intel mixed with the CRM information and perform some of this… perform some of this magic.
Julia Nimchinski:
Love it, Jordan. I’m still reading your book that you just recently released, and you’re this truly AI-native disruptive company. I’m curious, our community is mainly VPs and apps, mainly GTM leaders, sales, marketing, CS. What would be your recommendation, top use case, that everybody needs just to, you know, operationalize in 2026?
Jordan Nettles:
It’s a great question. Why you’re losing deals, on one end of the spectrum, and then at the very beginning of the spectrum, something similar. Talk to me about qualification and discovery. Right, so if you understand how I can make my bucket not be leaky, right, only let the right deals into the pipeline, and at the very end, if those right deals are still losing, why they’re losing? And then you can really get from both ends of the spectrum, ways to increase conversion.
Julia Nimchinski:
Really cool. And… In terms of 2025, What was your biggest surprise? With customers, use cases, case studies?
Jordan Nettles:
Yeah. Very interesting. I think the appetite for AI that works has been on a steady rise for the last several years, and every year you think there’s no way next year there’s going to be even more AI literacy, even more folks looking in the marketplace. And every single quarter, more CIOs, more CROs, not just technical folks, but truly business champions are out there looking for AI that works. And it’s really cool to see, and something that keeps getting better and better.
Julia Nimchinski:
Couple of more down-to-earth questions here from folks. Regarding adoption, so… how quickly do actually, you know, teams implement it and start actually using it? That’s…
Jordan Nettles:
Yeah.
Julia Nimchinski:
People are asking.
Jordan Nettles:
Great question. So, typically, the rollout period lasts between 2 and 6 weeks. There’s some nuance in that, for sure, in that, one of the ways in which we differ from a lot of the market is we actually position ourselves more as infrastructure than a UX layer. So, everything is being sent in Slack. being sent in Salesforce, inside of Microsoft Teams, Rather than come into our platform and spend a bunch of time clicking around. Now, there’s certainly a CI component, and that’s a little bit different, but for a lot of the use cases we’re talking about today, where send the CRO a roll-up of deals that were moved last week, or let RevOps get at some of this data they’ve never been able to access before. These are all use cases you can go live with in a week or two, because there’s no enablement, there’s no training, and Momentum is really existing as the infrastructure, sending the right data to the right places in the org. So it’s nuanced, but, 2 to 6 weeks, I’d say.
Julia Nimchinski:
Awesome, and one more question here. What does Momentum reveal about a deal that teams typically don’t see until it’s too late?
Jordan Nettles:
Hmm… That is a great question, and my favorite thing about the tool is that it is so flexible to your organization, in that there are certain red flags to your org that wouldn’t be red flags to mine. And so, I’ll give you an example. The owner.com team, they sell the S&B restaurants. One of the things that they listen for explicitly is if they hear the restaurant is under construction.
Julia Nimchinski:
Now, in my industry, that would mean nothing, but in theirs.
Jordan Nettles:
If a restaurant’s under construction and they sell restaurant tech to them, that’s going to be a customer who’s… that construction timeline is going to push their adoption of the tool is going to push, and they’re going to churn in two months. So they don’t even sell to that organization. So anytime you can map a use case like that, that is so specific to your org that you know drives enterprise value if you’re listening for and actioning on, those are the best things that Momentum identifies, because no one else in the market’s going to identify, because they’re so unique to your business.
Julia Nimchinski:
Do you have… do you help identifying those type of signals, or how does.
Jordan Nettles:
Yeah. Yeah, definitely. I mean, certainly could pull out, you know, 10 or 15 different examples like that, but ultimately what happens whenever you’re running through, like, a sales evaluation with us is a use case consultation. And so we’ll just… we’ll give a similar demo, much more in length to what we just showcased, but then we’ll go into, okay, great, now you know we can pull out whatever data you want. Let’s talk about what data is actually valuable, right? We don’t want to just throw AI at our go-to-market org. We want to understand the problems, and we want to map, okay, how do we get the right information and surface that to the right person? And that’s going to be different in every org, and Momentum’s very consultative in making sure that we get that information to the right people.
Julia Nimchinski:
Love it. What’s the best next step here, Jordan?
Jordan Nettles:
momentum.io. I would schedule a demo for sure. Also, feel free to reach out to me on LinkedIn, happy to facilitate a combo or get something going.
Julia Nimchinski:
Awesome. We’ll share your profile, and… That’s a wrap for Day 2, and join us tomorrow, everybody watching. We have the CEO of Gong, the CEO of Otter, Jasper, the VP of Strategy and Product at Adobe. We have a panel focused on cutting-edge sales tech. CEO panel, and a VC Panel on Trends and predictions for 2026 with Insight Partners, FredPoint, and lots of others. So, see you tomorrow!