Agent-Led Revenue Execution in Real Time

03:56:04.130 –> 03:56:09.280
Julia Nimchinski: And we are transitioning to our demo part in a second.

04:41:47.130 –> 04:41:49.959
Julia Nimchinski: Hi! Welcome to the show, Jonathan.

1459
04:41:49.960 –> 04:41:50.680
Jonathan Kvarfordt: Julia.

1460
04:41:50.680 –> 04:41:51.640
Julia Nimchinski: Hi! Again!

1461
04:41:51.770 –> 04:41:52.830
Julia Nimchinski: Join.

1462
04:41:53.350 –> 04:41:53.950
Jordan Nettles: Hi! How are we?

1463
04:41:53.950 –> 04:41:59.250
Jonathan Kvarfordt: I brought. I brought the brains with me. I brought the brains and and Superman. I call him Superman, but we brought the brains with me.

1464
04:41:59.370 –> 04:42:00.649
Jonathan Kvarfordt: we ready to go.

1465
04:42:00.650 –> 04:42:01.810
Julia Nimchinski: Let’s roll.

1466
04:42:02.440 –> 04:42:19.950
Jonathan Kvarfordt: Let’s do it. Okay? So I’m gonna go through some slides. I think it’s good to kind of show some stuff. And then the majority of us. I’m gonna leave time for Jordan to go through an actual demo and show some stuff. So what we are. My name is Jonathan. This is Jordan. I’m the head of growth for momentum. Jordan, you are. What’s your official title now? Super solutions expert leader.

1467
04:42:20.400 –> 04:42:21.740
Jonathan Kvarfordt: sales engineer here at momentum.

1468
04:42:22.790 –> 04:42:34.659
Jonathan Kvarfordt: senior super senior, and we we do what’s called enterprise listening orchestrated. When we talk about what? What is that like and give you some overview of stuff? I promise I’ll take no longer than 5 min, and Jordan need you to time me?

1469
04:42:34.740 –> 04:42:54.670
Jonathan Kvarfordt: All right. So what we hear in the marketplace, or what we’re seeing is a couple of main problems that all stem to this one thing, which is, as you well know, being on this webinar today, humans is the data source. This seems to be the crux of a lot of problems when you have a conversation or email that has 4 to 8,000 words and then only 25 of those get to the Crm or gets to a slack message, or whatever

1470
04:42:54.670 –> 04:43:08.190
Jonathan Kvarfordt: obviously, that causes issues like C-suite sales, leaders enablement and product and marketing do not have the information they need. And what we’re seeing is that’s creating what we’re calling the execution crisis where 3 main problems happen, which is, there’s Efficiency Gap.

1471
04:43:08.190 –> 04:43:28.460
Jonathan Kvarfordt: Anyone who is in sales or Cs knows this. But you’re asked to do a lot of tasks as well as a lot of data entry, which means a lot of your time from Forrester, Gartner, Deloitte, Mckinsey, anywhere you look, is like wasted on non revenue generating tasks. And then a 3% of the insights are just missed because of, obviously, it lives in someone’s brain versus in somewhere you can access.

1472
04:43:28.460 –> 04:43:50.559
Jonathan Kvarfordt: And that’s the second problem is that data is siloed either in someone’s brain or it’s in a tool like gong and gmail and salesforce, all of which are amazing tools. They’re just not readily available to be, you know, cross communicate different data points. And then, lastly, as a result of this, you have inside analysis slots which means leaders, enablement revops. Whoever even reps themselves do not have what they need to really execute on what’s actually happening.

1473
04:43:50.910 –> 04:44:07.480
Jonathan Kvarfordt: So we believe there’s 3 pillars that kind of make a big difference with this. I’m going to get into this by showing how what we have right now with the legacy tools again, there’s nothing wrong with these tools I use all of these. I’m a big fan of make and zapier with automations. I’m a huge fan of the tools on the left with conversation intelligence. I’ve bought and rolled out all of them.

1474
04:44:07.620 –> 04:44:32.500
Jonathan Kvarfordt: The challenge is that with Ci tools, classically, they don’t currently structure data and trigger action, they do a really good job of analyzing calls, but then it just sits there and doesn’t take any action, do anything with it with Zapier and make and and all these other agent builders, which are amazing. They are anyone who’s tried to do this across from organization. They’re a little bit fragile. You have to keep up with the changes of the models, and then you have to all the changes of like your salesforce, changing daily because of new

1475
04:44:32.500 –> 04:44:42.740
Jonathan Kvarfordt: field or whatever process change. And then as enablement, I’ve seen how people tried to get this. So people can do things better or faster, and that fails because humans are busy trying to talk to customers.

1476
04:44:43.400 –> 04:45:10.089
Jonathan Kvarfordt: So we think there’s 3 pillars. The 1st one is having a 1st party data structured provider, and it’s clean usable. And more importantly, it takes to the weight off of the the reps to be able to focus on the conversation rather than focusing on data entry or menial process tasks, and then being able to take those and automatically communicate that across the entire organization from leaders down to the reps. So the right signal gets to the right person at the right time. That’s clean and consistent and unbiased.

1477
04:45:10.170 –> 04:45:34.209
Jonathan Kvarfordt: as anyone knows in leadership. Bad news travels slow, and obviously, if you have something like a churn or a deals in risk. You want to know about that sooner than later, not to get someone in trouble, but to support them and make sure you can save it and make a good thing out of it. And then, lastly, is orchestration, which is the thing I’ve been geeking out lately. Forrester just released a report about this. This is what momentum has been doing for years around orchestrating AI agents and automation to take action for you. So again can take some weight off of the rep

1478
04:45:34.220 –> 04:45:54.109
Jonathan Kvarfordt: to do this. And this is what makes up momentum. And this is kind of what I want to set up Jordan for is making sure this is a holistic view of making sure all these data points are connected. And then you have benefits like this, where Revops gets more forecasting, accuracy, enabling gets methodology adoption. There’s churn, risk, or upsell notices. There’s all sorts of benefits that come as a result

1479
04:45:54.140 –> 04:46:05.049
Jonathan Kvarfordt: that can help with all these different, I’m gonna zip through these real quick, all these different use cases per team. So there’s so much data that sits inside the conversations that once it’s structured and clean, can have a lot of benefits for teams.

1480
04:46:05.190 –> 04:46:10.140
Jonathan Kvarfordt: With that being said. Look at that 3 min, 4 min. Jordan’s turn bam.

04:46:10.140 –> 04:46:32.930
Jordan Nettles: Right on the money. Thank you, Jonathan. I’m going to go ahead and showcase a couple of things. And the 1st of them actually is a bit about the momentum infrastructure, because I think that’s 1 of the most agentic parts of the platform, because it’s going to be ultimately what we derive value from and everything else. As we know, junk in junk out. And so with momentum, we actually treat the data as the ultimate product.

1482
04:46:32.980 –> 04:46:57.880
Jordan Nettles: So you’re having these customer interactions on calls, emails and service tickets, and so on. And what we typically see in the marketplace, or what we have in the last couple of years is sort of a path towards. I take these Vectors of Comms, and I run them through an Llm. And then I get my output, and luckily for momentum, we’ve been doing this for several years now. And so we actually have what we call a Gtm data refinery. So think about

1483
04:46:57.880 –> 04:47:11.959
Jordan Nettles: for every communication that’s coming into our systems running through a standard process of input normalization, applying rag applying custom context and applying multiple stages of Qa validation.

1484
04:47:11.960 –> 04:47:31.990
Jordan Nettles: Now, what that means is every single conversation that is go to market focus is going to be something like something like each other. So think about many calls are going to have objections are going to have incoming praise and product, and whatever that looks like in terms of your go to market cycle. And what momentum does is actually

1485
04:47:31.990 –> 04:47:58.189
Jordan Nettles: for each conversation piece, pull out these pieces of context and then run them through several different cleaning processes in the back end to make sure that whenever we perform any sort of process like Jonathan’s talking about, whenever we want to flag, churn, risk, or whenever we want to flag product, feedback or upsell potential. We’re not just saying, Hey, here’s a transcript. Did someone talk about upsell potential? We’re actually looking at the individual data points that’s coming out. So

1486
04:47:58.190 –> 04:48:11.619
Jordan Nettles: this lets us do 2 things in the context of an agent world. You know, when we consider ourselves a true data platform, that means that we’re extracting these insights, transforming them in different layers and loading them into the right spots.

1487
04:48:11.630 –> 04:48:25.169
Jordan Nettles: And all of this ultimately lets us get at like I said, 2 things in the agent landscape. One is being able to pre-build some of your agents inside the momentum platform itself. So I’ll walk through a couple of those today. But

1488
04:48:25.540 –> 04:48:29.780
Jordan Nettles: I want to touch high level on the other pattern that we’re seeing emerge

1489
04:48:29.870 –> 04:48:54.099
Jordan Nettles: when you have the data being the 1st class citizen, the 1st class product. You actually have high quality information to train your agents. So everyone that we’re talking to in the mid market and up has someone on a go to market engineer or data science and data science teams who all want to be able to leverage high quality, go to market data. But when they try to import

1490
04:48:54.120 –> 04:49:11.100
Jordan Nettles: through this process we’re talking about before it’s archaic. It becomes very, very difficult to parse through all that information. And so, by having the enrichment of the service tickets and having the enrichment of the Crm. And having the enrichment of all of these custom data points along the way. Ultimately, what they’re able to do is train their agents

1491
04:49:11.100 –> 04:49:25.990
Jordan Nettles: right? They’re able to see the exact data set that they need and perform whatever tasks their team is wanting to. So a bit of a dichotomy in terms of how we see momentum being used. But we’ll cover some of the in-house modalities today.

1492
04:49:26.010 –> 04:49:51.830
Jordan Nettles: So one very, very common modality is something we call customer retention agent. So think about listening, having your ear to the floor for any sort of negative interaction that’s happening in your organization and flagging, that, as it happens so for us, we get to have the AI make a few decisions right? So if the customer success.

1493
04:49:52.316 –> 04:49:59.283
Jordan Nettles: managers managing an account that might be $20,000. Maybe this is something that we’re sending directly to

1494
04:49:59.900 –> 04:50:19.750
Jordan Nettles: a Csm. And maybe their 1st line manager. But as we go up market as we start to see someone who’s a $250,000 account. When we actually send this message, it’s going to contain different contents that are more specialized to that 3rd Level manager. Maybe a Vp of Cs or a Vp of sales. And what’s going to happen is

1495
04:50:20.551 –> 04:50:40.279
Jordan Nettles: we can take a couple of actions. One. The AI is going to give us any sort of context that it finds relevant. So think about that being something like you know context about the vectors of communication. So if it’s a ticket, if it’s a call pulling in all that information and summarizing it in a neat way, so that everyone who’s reviewing can get up to speed

1496
04:50:40.740 –> 04:50:44.710
Jordan Nettles: to any sort of direct or indirect

1497
04:50:44.960 –> 04:51:06.240
Jordan Nettles: mentions of why they’re upset ways. We can rectify the situation, you know. Objection handling, maybe by the rep. If it’s on the Presale side, anything we want to do to really focus in on what is valuable for someone who’s trying to work the account and ultimately save the account and drive customer sentiment. And then, next, we have access to other systems.

1498
04:51:06.240 –> 04:51:18.459
Jordan Nettles: Right? So, like, Jonathan was saying, sort of the the pillar of getting this to the right place at the right time. We have the ability to perform salesforce actions. So one very common version of this is, if

1499
04:51:18.460 –> 04:51:37.340
Jordan Nettles: the AI. Rates their their sentiment on a 1 out of 5, and the account is above $100,000. We actually create a case for the customer with those exact details that we’re highlighting being reflected. In that case. So not just are we saying, Hey, by the way, someone’s upset, we’re actually driving action.

1500
04:51:37.340 –> 04:51:56.779
Jordan Nettles: right? And that’s where momentum typically fills in with with our in-house agents is, we take the data which is our ultimate product. But then, for anything that we’re building in terms of agentic workflow and automation, it’s always going to be about taking the action and protecting the revenue, growing the revenue. Whatever is going to help the Gtm work succeed. That’s where momentum plugs in

1501
04:51:57.730 –> 04:52:24.719
Jordan Nettles: One more example. I’ll show here of of the types of things we do with this 1st party data. Think about being able to, as a leader, understand, across a swath of a week’s worth of calls. Maybe this is 70 calls what is happening in the field. So if I’m a Vp of sales or a Cro. I’m going to care about what’s happening in early stage deals as it relates to identifying a clear problem

1502
04:52:24.920 –> 04:52:30.090
Jordan Nettles: and making sure that we have a a champion identified so that we can solve that problem through the champion internally.

1503
04:52:30.230 –> 04:52:51.410
Jordan Nettles: But then, as we get late stage, maybe I’m focused more on something like, do we have access to an economic buyer? Are we picking up on risks? Are we getting happy years and thinking this is going to close? Or are we actually moving in a direction towards picking this information up? So what momentum is doing in this situation is it’s reviewing all of the different interactions that my customer base is having.

1504
04:52:51.630 –> 04:52:59.039
Jordan Nettles: And it’s going through. And it’s using that 1st party data set to collect the most relevant information to answer my Cro’s questions.

1505
04:52:59.240 –> 04:53:16.870
Jordan Nettles: And so it’s going to come through. And it’s going to devise, not just answers to the direct questions. But there’s also space for indirect questions. What are the aggregate trends that we’re seeing across the entire pipeline. What are we seeing? That’s not just, you know, sort of a different paradigm, for this is what questions am I not asking

1506
04:53:16.970 –> 04:53:28.629
Jordan Nettles: and letting the AI go ahead and parse that information because it’s already done the work of aggregating and siloing off the right data set for the problem, and then it’s going to go through and

1507
04:53:28.650 –> 04:53:52.700
Jordan Nettles: piece together the correct recommendations and next steps and high level inconsistencies. Think about seeing summary stats or running close loss analysis, any sort of high level information that I want the AI to pick up on. That’s what momentum is able to do using that data harvesting that we’ve been talking about for the last half decade. So, Jonathan, anything anything else to cover before

1508
04:53:52.910 –> 04:53:54.140
Jordan Nettles: before we close.

1509
04:53:55.385 –> 04:53:56.959
Jonathan Kvarfordt: No, 1st off. Thank you.

1510
04:53:57.160 –> 04:54:13.330
Jonathan Kvarfordt: This is why I brought you, because you did much better than I would, which is great, and 2. 1 of our customers said that we think about the concept of democratizing the data and what you can do with it. Not only can you take and load up your Crm or slack, or whatever communication you want to have, or your system of record.

1511
04:54:13.330 –> 04:54:28.850
Jonathan Kvarfordt: But because you have good, clean data like you said, the the case created in salesforce is one example of many like another. Example is, our AI can recognize when exit criteria has been met, and either serve a button up to the person in slack and saying, Hey, do you want to move stages? Or it’ll just do it for them.

1512
04:54:28.850 –> 04:54:42.920
Jonathan Kvarfordt: So it kind of depends on how the organization feels around the interactions between agents. But there’s a lot of things you can do just automatically in the background as well as put the human in the loop and let people interact with it as they’d like to, which is a lot of fun. So the the cooling momentum.

1513
04:54:43.120 –> 04:54:49.540
Jonathan Kvarfordt: there’s just so many flexible things you can do with it. It’s just really asking what’s the most important thing for your team to move the dial. And that’s where we focus.

1514
04:54:50.230 –> 04:54:51.620
Jonathan Kvarfordt: So there you go.

1515
04:54:52.722 –> 04:55:00.700
Julia Nimchinski: Yeah, thank you. So so much. Jordan and Jonathan, I’m just curious. What is I mean? I guess both of you actually

1516
04:55:01.570 –> 04:55:05.830
Julia Nimchinski: what’s your top favorite use case for Cxs.

1517
04:55:06.470 –> 04:55:08.800
Jonathan Kvarfordt: Oh, Jordan, for sure. I know he got this one.

1518
04:55:10.287 –> 04:55:23.400
Jordan Nettles: I love one because we have access to all of the different, you know, years and years of data. I personally love being able to go back and find something very straightforward, like.

1519
04:55:23.400 –> 04:55:48.379
Jordan Nettles: what is a competitor’s contract end date? Or what did we pitch this customer 2 years ago? And how has the experience been as we attacked that problem? So it’s 1 thing to look forward and say, Hey, here’s what happened this week. It’s another to look through 3 years of that data set and pull out the things that are most interesting, one in a tactical way, like understanding who I can retarget, because now I have access to

1520
04:55:48.380 –> 04:56:05.829
Jordan Nettles: that information of you know what was mentioned 3 years ago. But 2 really understanding sort of like you’re saying the customer success experience as as we’ve sort of looked to address the problem. And and really seeing the full journey is what ultimately is, is the most fun for for me to be a part of.

1521
04:56:06.760 –> 04:56:13.379
Julia Nimchinski: Really love the tech. We had Mark Roberts yesterday, the retention agent. I don’t think I ever saw

1522
04:56:13.710 –> 04:56:18.550
Julia Nimchinski: retention agent. Do you have any competitors in that area?

1523
04:56:19.790 –> 04:56:22.509
Jordan Nettles: Yeah. So, Jonathan, you got it.

1524
04:56:22.970 –> 04:56:25.990
Jonathan Kvarfordt: Oh, no, that you’re talking with customer retention specifically.

1525
04:56:26.570 –> 04:56:27.220
Julia Nimchinski: Yeah.

1526
04:56:27.940 –> 04:56:52.590
Jonathan Kvarfordt: There. There’s automations, but there’s not an as far as I know. If there is, they could very well be. I don’t know. There’s not a lot. There’s not a lot in the customer retention side. However, most of our customers that’s 1 of their main use cases, and there’s 2 sides. One is, we have an agent that will analyze. They’ll go back through all the calls, or let’s say you have 10 calls over an opportunity and auto create a handoff note and then send that over to Cs. They can have that and number 2.

1527
04:56:52.660 –> 04:57:09.499
Jonathan Kvarfordt: half. Our customers use it for retention, not only for churn, risk, and analysis, but also for general sentiment analysis. So it’ll score the calls, and then also, like, send either signals to saying we’re good, or it’ll send a signal saying, Hey, we got a chance to upsell, so it’s both directions what’s going bad on also what’s going right?

1528
04:57:09.500 –> 04:57:27.009
Jonathan Kvarfordt: And then we can suck out all the different sections and saying, what’s coming next is when we have what’s called essential clips, where the agent will auto clip the videos and saying, not only do we know this, but here’s your 30 second clip of when the Csm. Did a killer job of presenting the new products bam! There you go so like, it’s good going into a really really cool place. But

1529
04:57:27.090 –> 04:57:28.659
Jonathan Kvarfordt: does that answer your question hopefully?

1530
04:57:29.400 –> 04:57:37.909
Julia Nimchinski: Absolutely. And before we transition to our next demo, just I don’t know what’s what’s the best next step for everybody’s watching.

1531
04:57:38.320 –> 04:57:41.200
Jonathan Kvarfordt: Come, get a demo with Jordan, man, that’s what that’s what you should do.

1532
04:57:41.380 –> 04:58:01.530
Jonathan Kvarfordt: No, but for real we. We would love to have be in a consideration for what you’re going through. We do have the prompt library. So if no one has not accessed that yet, please go there. It’s free. It’s 200 plus prompts in the prompt library. In fact, I’m happy to send it to you, Julie. You can send it out and then come, come, talk to us. Let’s let’s do a transformation session. See what we can do to help your team kind of take it to the next level.

1533
04:58:02.640 –> 04:58:05.830
Julia Nimchinski: Awesome. Thank you so much. Again, Jonathan Jordan.

1534
04:58:06.240 –> 04:58:06.660
Jordan Nettles: Thank you.