Use a PMM AI agent like a Docebo PMM
Product marketers, you know this specific pain well. You need insights yesterday, but the data is scattered across calls, surveys, dashboards, and a dozen internal docs. It can take weeks, if not months, to collate everything you need, and by then the information you are using may be out of date with current market trends.
Emily Pick’s approach flips that. In our conversation, she shared how she ran a multi-layered messaging gap analysis in two hours using AI agents. Two hours. Emily’s sophisticated knowledge of the tech stack she had available to her as a Product Marketer at Docebo made it possible.
Speed changes everything. When you can get to patterns fast, you stop debating opinions and start making decisions with evidence. At Docebo, a learning platform, Emily has been using AI to compress the messy middle of product marketing. The part where you collect inputs, clean them up, and try to find a narrative in the noise.
The key is not magic prompts. It’s process.
Start With Inputs, Not Tools
Before Emily touches any AI, she gets brutally clear on what data actually matters. For her, that means inputs like win-loss insights, NPS scores, and customer conversation data. This step sounds basic, but it’s where most teams go wrong. If your inputs are fuzzy, your outputs will be too. AI just helps you get to the wrong answer faster.
When the inputs are right, everything downstream gets easier.
Automate the Collection So You Can Think
Once the inputs are defined, Emily leans on automation to do the heavy lifting. She described using setups like insights agents to pull NPS and sentiment without manual digging. The point is not to replace thinking. It’s to protect it.
Instead of spending hours chasing data, she can spend that time doing what PMMs are actually hired for. Making sense of what customers are telling you, spotting gaps in the story, and shaping messaging that the business can use.
Turn Insights Into Narrative, Fast
The real unlock is what happens next. Emily feeds organised insights into a custom AI model to explore narrative shifts and messaging strategies quickly. This is where AI becomes a force multiplier. Not because it writes clever copy, but because it helps you test angles, pressure-test assumptions, and surface patterns you might miss when you’re tired and staring at a spreadsheet.
The result is not a final answer. It’s a faster path to a smarter starting point.
Build the Tool You Wish Existed
Emily also shared something I loved. When the perfect tool did not exist, her organisation built one. They developed an Audience Insights agent that pulls nuanced call data for natural language processing. It’s bespoke, but the principle applies to everyone. You do not need a perfect stack to get value. You need creativity and a clear workflow.
She pointed out that similar outcomes can be achieved with widely available tools like Gong if you’re willing to be inventive about how you structure your inputs and what you measure.
Feedback and Iteration
Emily is also clear that analysis is never the finish line. After she builds a report or recommendation, she circulates it across product, demand gen, and stakeholders to validate and refine. That feedback loop does two things. It improves the work, and it builds trust in the process. People support insights they helped shape.
AI speeds up the work, but iteration makes it land.
Emily’s advice is simple and practical. Start with what you have. Use AI to remove the manual drag. Then advocate for better access and better tooling once you can prove the lift. The efficiency gains are too big to ignore, and the teams who learn this early will move faster, make better decisions, and build stronger messaging.
AI is not replacing product marketing. It’s raising the bar for it.
Messaging Critique: Granola.ai
We also shifted to a messaging critique of Granola.ai, an AI note-taking tool designed for people in back-to-back meetings, with perfectly timestamped notes. Their core message is refreshingly direct: “AI note-taking for people in back-to-back meetings.” No fluff. No vague platform language. You know exactly who it’s for and why it matters.
We also loved their creative “crunched” campaign, a playful nod to Spotify Wrapped. It’s a great example of a brand staying creative whilst still solving a practical problem.
Where Granola.ai can go further is emotional differentiation. In a crowded world of call recording tools, the winning story is not just “we capture notes.” It’s “you can be present.” You can stop multitasking. You can trust that you won’t miss the one decision that matters. That emotional payoff is what turns a useful tool into a must-have.
LINKS:
Messaging Critique:
Granola.ai
https://www.granola.ai/
Connect with Emily:
LinkedIn: https://www.linkedin.com/in/emilypick/
Connect with Elle:
LinkedIn:https://www.linkedin.com/in/elle3izabeth/
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[00:00:00] Elle: AI has changed the game, and our guest today has the perfect example.
[00:00:05] Elle: She used AI to run a multi-layered message gap analysis in silent drum roll, please. Two hours, not two weeks, not two months, two hours. With that, it is my pleasure to have Emily pick on the show, Emily is what some people might say. An accidental marketer who while starting with an education in human physiology, followed opportunities and landed as a leader in product marketing with more than 10 years in SaaS, she has worked through the gamut of Series A through F companies, and now sits in her first enterprise sized public company at Decebo And if you haven't caught one of Emily's viral posts on LinkedIn, let me fill you in. She's known for translating messy cross-functional signals into clear stories that help pmms win, whether that's competitive differentiation, buyer expectations, or [00:01:00] how to use AI to be a stronger PMM. She's so generous with her time and shares practical guidance and real world learnings every week.
[00:01:08] Elle: Emily, it's amazing to have you on the show.
[00:01:10] Emily: L far too kind, first of all. Um, also, uh, I want the audience to know that, uh, l actually is. Spelled out phonetically, how to say Decebo That has been probably the number one thing, uh, since I joined the company. Uh, it's spelled D-O-C-E-B-O. And, uh, the very first time I heard about it, uh, I was actually working for another company where DoDeDocebo was one of our customers, and I was getting ready to interview them for a customer story.
[00:01:39] Emily: And I was chatting with our head of content and I made a comment about how I was gonna be interviewing someone at De sibo, and he was like, absolutely not. You are not allowed, uh, to walk into that conversation and say de sibo. Um, I can tell
[00:01:51] Emily: you
[00:01:52] Elle: God for the kind
[00:01:53] Emily: I was gonna say, that's probably the number one, uh, thing that I've done since I've been here is just, uh, teach people.
[00:01:59] Emily: It's as [00:02:00] my former head of Convent said Debo, uh, so it is an Italian based or Italian founded company. So, uh, great job on the pronunciation. And it actually, uh, was one of the things that I, uh, made me smile when I read our, our notes getting ready for
[00:02:14] Emily: the
[00:02:14] Elle: Thank you. I love it. Yes. Uh, full disclosure. I totally admit, I spelled it out phonetically so that I wouldn't go Dobo DoDecebo
[00:02:26] Emily: I mean, we've heard every, every iteration.
[00:02:28] Elle: I know, but, uh, DoDecebo is really fun to say. So let's start actually by giving the listeners some context. What is DoDecebo exactly?
[00:02:38] Emily: Yeah, so Docebo is a learning platform. We primarily serve enterprise audiences, uh, internal and external. So if you think about there, there is a bit of the connotation of the old school LOMS. Uh, we are taking a step beyond that though. Uh, so we not only are serving your internal audiences, doing things like employee training, [00:03:00] compliance, onboarding, we're also serving external use cases like customer education, partner training, uh, things like franchise training, really the full spectrum of learning across any enterprise.
[00:03:12] Emily: So, if you are getting ready to, uh, onboard into a new company, there's a strong possibility you've got to chabo and you don't know it.
[00:03:20] Elle: How fun. Yeah. With my experience at Cisco, I know they were like huge in courses and certifications and like thousands and thousands and thousands of IT professionals and developers and software architects would always go in and, um, have to go through some kind of learning platform. So. Very cool. thanks for the background.
[00:03:41] Elle: So the topic of today's episode is about using AI for a message gap analysis. And I feel like this is such an important one with all of the tools that are available to us, and I feel like a lot of pmms out there don't know just how to navigate that. So for the first segment of our [00:04:00] show, I wanna start with that as our case study and how you used AI to uncover that, gap in your own messaging. tell me more about what was going on at DoDecebo when you kicked off this analysis, where you realized that like this analysis had to happen.
[00:04:14] Emily: Yes, so I actually have only been at Debo two, two and a half, close to three months now. so still pretty fresh into the org, still fresh. This is my first time in the learning and development space, so lots to learn. Um, and we'll talk about how AI has been truly helpful in that regard as well. Um, but just from a situational context, uh, our CRO, he has a former mentor that he likes to bring in to come in and do a full end-to-end pipeline analysis.
[00:04:43] Emily: Where are we sourcing our leads from? How are they moving through the pipe? Also, how are they moving through on the backend? All of our lead gen systems really a, a truly inclusive process where
[00:04:53] Elle: That's really cool.
[00:04:54] Emily: Yeah, it's, it's pretty great. Um, the takeaways so far have been pretty dynamic. Uh, but [00:05:00] one of the things that she really found is that, uh, we had a specific segment that was closing at a rate six to 7% lower than our benchmark.
[00:05:07] Emily: And so one of the things that we had talked about was, okay, where are we really seeing this start to fall off in the pipe? And it really was after that demo stage. So the hypothesis that we came up with from that is that, hey, we must not be telling the market the right message, and we must not be meeting their expert expectations when they do come into a demo with us.
[00:05:31] Emily: So. That's the task that I needed to investigate. What we needed to understand was, hey, when this segment comes in, what are their expectations when we win opportunities? Why is that? Why is it that we lose? Does it change based on the personas that are involved? Does it change based on the size of the company or the industry?
[00:05:52] Emily: And most importantly, what has happened in the past, uh, with our product and where are we heading in the future so that we know where we can [00:06:00] lean into or out of based on the output of these other investigations?
[00:06:04] Elle: so basically you have this segment that you realized was not performing at the same level as all of the other segments, and the task at hand was like, Hey, let's go figure out what the heck is going on with all of the leads that are falling off for this one particular segment. You, obviously you used ai, right? To go forward and do some of that investigation to figure out what the heck is happening with all of, with this segment and why isn't it performing well.
[00:06:31] Elle: So, while we're kind of going back in time and talking through what was happening at Decebo like, let's actually, pretend for a moment that you've now started. We're, we're in the future and you've now started at a new job and the same kind of signal is happening. You've got a segment that's underperforming, two expectations, but you have the playbook now.
[00:06:52] Elle: So let's talk about it. What, like, what does step one look like now that you've identified that signal? that you gotta go do some [00:07:00] exploration?
[00:07:00] Emily: first and foremost, I'm going to identify what are the inputs that I need to do the analysis. some things have not changed from the before times from the pre AI times. We know, uh, we need to have our win-loss data. We know that we need to have access to things like NPS scores, so we can track how things have changed over time, which direction we're trending, uh, just to see if we can validate, uh, some of those assumptions that we're making.
[00:07:28] Emily: Um, we also want to be able to kind of pull insights from our customer conversations. I think we all, uh, at this point have had access to some sort of call recording tool. Um, we use Gong here at Debo. In the before times, I would've gone through, I would've probably done a keyword search. I would've tried to, uh, pull together any insights that I could from conversations that I thought were relevant, download the transcripts, uh, just really be able to pull together those insights.
[00:07:58] Emily: Uh,
[00:07:58] Elle: Yeah. That's like a full [00:08:00] day's work right there. Then you haven't even, you haven't even, and you haven't even done the analysis yet. You're just
[00:08:05] Emily: that's, yeah, I was gonna say like. There's, uh, some things we'll talk about as we get into this, but, um, just the advancements of technology. Um, for context, this request came to me around noon on a Friday, and I signed off around 2:00 PM having it, uh, fully done and out the door to my boss for first rounds of reviews.
[00:08:24] Emily: Uh, so a little different, uh, than the way it used to be. Um, but I also think just as far as trying to pull together insights as far as what are trends within the industry. I mean, I, I think about, uh, where do we source our insights from? Now, obviously, we go to analyst reports and we go to those trusted voices within the space.
[00:08:46] Emily: If there's an influencer on LinkedIn or a creator that we have a lot of respect for, maybe that's someone whose insights we go try to, uh, pull from, or Reddit we're seeing now, especially with chat, GPT and all the [00:09:00] LLMs, they're really surfacing Reddit and having that like. I guess almost unfiltered voice of, of the market coming through.
[00:09:08] Emily: Uh, so things like G two, things like Gartner, peer Insights, those are all places that we would be going after just to pull together, Hey, how are people talking about us online? And then also what are their expectations for the industry and moving forward. Um, so speaking of amount of time to go through for that, I mean, I don't know.
[00:09:28] Emily: I don't know even today, like how often are you trying to find a stat for a sales deck or something,
[00:09:34] Elle: Oh yeah.
[00:09:35] Emily: I need this stat. Do we have any stats on this thing? And like, you'll go through how many hoops to jump through and be like, oh, I have finally found this Forester report. Oh, it's gated for $3,000.
[00:09:47] Emily: I
[00:09:47] Emily: cannot,
[00:09:48] Elle: Yep, yep,
[00:09:49] Emily: there. Um, so the, the time that it would take previously.
[00:09:56] Elle: It's un
[00:09:56] Emily: intense. So that, and that's just the information [00:10:00] gathering stage. We haven't even gotten into the analysis yet. So step one, find all of the data that you need based on the ask.
[00:10:07] Emily: So
[00:10:07] Emily: pre ai, I would've done all of this.
[00:10:11] Elle: Yes, totally. Exactly. And that alone would've taken maybe a few weeks, hopefully less. there's, identifying what you need, and that's like obviously a quick list, but actually like gathering the information can take a long time if things are gated, as you said, if it just, like, you gotta chase people down, you gotta, it's just, it just takes a long time to gather that information.
[00:10:31] Elle: Um, okay, so step one is define your inputs. What step two.
[00:10:36] Emily: Oh man, step two is actually start performing some of the analysis, or maybe not even actually that yet, because now we're taking those insights and we're gonna curate them, collate them together in a way that's going to be usable for the analysis. So for me, in this situation, I am incredibly lucky to have DVAs Tech stack, uh, at my disposal.
[00:10:58] Emily: So, first and [00:11:00] foremost, we have automations already set up that all of our NPS scores are automatically being
[00:11:05] Elle: You are so lucky
[00:11:06] Emily: So lucky, uh, like all of our NPS scores are automatically surfaced within a Slack channel. I was able to create a glean agent again. We also have glean, uh, at our disposal, which is huge.
[00:11:19] Emily: Uh, I was able to create an agent in Glean to pull those insights and look at, hey, over the past six months, how have we seen trends in sentiment, um, relative to this segment and the message we're trying to solve for? How, how has that changed over time? have we seen positive or negative improvement in these different areas?
[00:11:39] Emily: Um, what has changed that people maybe had feedback for us from six months ago. Versus how did it look three months ago versus how does it look today? So understanding how that sentiment is changing based on how we've been evolving our roadmap and product. Uh, then going and pulling all of our win-loss data.
[00:11:57] Emily: We have a wonderful competitive [00:12:00] intelligence manager here. Uh, his name is Ben Cherry and he came to us from Clue, which also helps explain why our clue instance is, uh,
[00:12:07] Elle: so
[00:12:08] Emily: top notch and out of this
[00:12:09] Elle: out, Ben. Great job.
[00:12:11] Emily: Ben is incredible. We love Ben. Uh, but he also just incredible record keeping as far as he actually goes out and performs the individual, um, interviews with customers that we have won or lost.
[00:12:25] Emily: Uh, and the way that he has it segmented the data. I can see, uh, exactly which segment, which personas were involved, which use cases. Like I, I can slice and dice that data in so many different ways. Um, then I have a tool that is. Beyond my expectations as a PMM, uh, I kind of brag about it all the time. Our demand operations team built us an agent using, I think it's called Unity Apps, but we call it our Audience Insights agent.
[00:12:54] Emily: And what it does is it looks across all of our gong calls. so based [00:13:00] on that Salesforce data, how it's all, uh, kind of organized in there, again, being able to slice and dice, but before, as you would've had to go through and individually identify different calls, now I can just do a natural language query and say, Hey, I'm looking for this segment, this industry, these personas.
[00:13:21] Emily: I wanna know what they were looking for when they came in. I wanna know how they reacted to our demo. I want to know what kinds of questions they were asking. I wanna know whether or not we were able to adequately answer those questions, and I wanna know if they moved on to the next stage. I try not to say game changer very often because I feel like it got pretty cliche there for a
[00:13:41] Emily: while. But this is a, a, legitimate game changer.
[00:13:44] Elle: It truly is.
[00:13:46] Emily: I know Gong recently launched their AI builder, I think it's called. I haven't had a chance to
[00:13:52] Elle: They've got something. I've seen
[00:13:53] Emily: yeah, you've gotta have a full seat, which unfortunately I do not have. but the audience insights agent [00:14:00] really alleviates any concern there. Pretty much anything from that I can also do within the Audience Insights agent. And we also have it attached to all of our like benchmark data and our value trees and our testimonials. but I was able to pull all of that data and then, uh, the final stage before I started the analysis was I was able to go in and I created, um, a doc with parameters for perplexity. And luckily, because I've been a newer hire, another new hire on the team, uh, she and I have been working on a doc that outlines who are our influencers in the space, who are the analysts that we trust, who are the, uh, voices that we listen to?
[00:14:40] Emily: Where do we go for information in Reddit? Um, all sorts of different channels. we really just outline the sub stacks we listen to, or we, we watch the podcast, we listen to, And that was actually great as far as giving perplexity parameters. When I said, Hey, I wanna understand the state of the industry.
[00:14:57] Emily: I wanna understand what people expect in [00:15:00] this segment. I wanna understand what they're trying to solve for, and where they think that the entire industry on a whole is falling down today.
[00:15:07] Elle: the first thing that I wanna dig into is that there a call out between your step one and step two. So in step one you were gathering the insights and then step two was, um, oh, sorry, sorry, sorry. I mean, step one was defining the insights and then step two is gathering the insights.
[00:15:28] Elle: And part of that gathering, it sounds like, was like cleaning and organizing the data as well, which is a step that I kind of forgot
[00:15:34] Elle: about actually. when you're like synthesizing like different types of data, but from different sources. And then you're trying to put it all together.
[00:15:43] Elle: It's a huge headache and hassle to do it. I mean, you have like, what else is there to do it? Well, no, you can use ai.
[00:15:51] Emily: but with AI you also have to watch out for the hallucinations, which is why I'm glad you brought this up
[00:15:56] Emily: because when I did post about this on LinkedIn, that was one of the number one [00:16:00] questions, which was, how are you controlling for hallucinations across this Process?
[00:16:03] Emily: Process? Completely fair and valid because hallucinations are inherent to ai, especially LLM models. and so I had to do spot check throughout. I was spot checking. I was, one of the things is that, uh, between Glean our audience Insights Agent Perplexity, one of the great things that they do is just the automatic audit trail. So being able to link out and see, hey, where did this stack come from?
[00:16:30] Emily: Where did this quote come from? with audience Insights, we can go and look at where, where specifically within what call did you pull this information out of? So, um, that was also a part of the process. I was, I was telling a friend the other day because, she also works at Dachabo and we were talking about the audience insights agent and how much we love it, and we're like, oh.
[00:16:50] Emily: Sometimes though it takes. A while to pull, uh, some of those insights, which is fair. It's cross-referencing a ton of data, but I was like, Hey, it was actually great. 'cause [00:17:00] in this project, in that in between downtime is when I was like, okay, I've put this query into the audience insights agent. It's gonna get me this stuff.
[00:17:08] Emily: Eventually in the interim, I'm going to review the things that I pulled out of this other information. I'm gonna do a spot check and just make sure that everything in here is coming out and there's nothing that I'm like, Ooh, big questions, or Ooh, red flag. Like, Hey, here's a stat. I'm gonna click on where it came from.
[00:17:25] Emily: And how many times does this happen? Uh, even in the best, best L LLMs where you click it and you're like, Hey, this stat isn't anywhere on the page. Where did you come up with this? luckily I did not run into a ton of that, or at least nothing where I was like, oh, this is going to completely derail, uh,
[00:17:45] Emily: this project.
[00:17:46] Emily: So.
[00:17:46] Elle: alarming.
[00:17:47] Emily: A lot of spot checks. I had a few people who were, did leave comments and were like, why didn't you run this as one giant agent? And I mean, I would love to number one, but also [00:18:00] I will say from a confidence in the inputs that we ultimately use to analyze, I feel more confident in them than I would have if I had allowed every single thing to run via an individual agent. hopefully that changes one day where hallucinations won't be the problem that they are right now. But from a, is this data reliable? Is this, are these insights reliable? You really do have to go through and spot check.
[00:18:30] Elle: Yeah. Which, when you think about it, I mean, don't you always proofread a paper or didn't you always proofread a paper before you turned it in in school? Right.
[00:18:39] Elle: Like you would never, you would never not proofread something and double check that your sources are accurate. And you know, that's something that I think like really high performers do anyway.
[00:18:51] Elle: And listen, I'll take, spend an extra hour, you know, proofreading or checking for
[00:18:57] Emily: Right. Versus this
[00:18:59] Elle: [00:19:00] three months,
[00:19:00] Emily: I was gonna say like, the amount of time to track down all of these individual resources and insights on my own, and that's, that's pretending that you can sit down and actually work a full day and dedicate a full day of time. Like when do you ever get to do that? Mm.
[00:19:18] Elle: Exactly. It's rare. Very cool. I have another question for you about, and this is, this may be difficult to answer, but I'm thinking again, like, imagine yourself, you're at a new company now. We're in the future. You're at a new company and a very similar situation has popped up.
[00:19:35] Elle: You're, you're, you're recalling back on this playbook. Uh, maybe tools have evolved a little bit, but maybe not, let's say for the sake of argument they have not. Um, what if you don't have an audience insights app or tool or whatever, the one that was built internally? What if you don't have that?
[00:19:51] Emily: I mean, I think we have come a long ways with tools like Gong. I came from Clary. We had, uh, a very similar tech to [00:20:00] their call recording or call intelligence software called Copilot. one thing that is great, maybe they're not fully at the level of sophistication that I would need for this type of.
[00:20:09] Emily: Cross call analysis, but they do have things that you can do, like set up trackers, you can set up keyword trackers, you can set up, um,
[00:20:20] Emily: it's, it's not quite, I was
[00:20:21] Emily: gonna say, episode on that topic.
[00:20:25] Emily: at me leading the conversation. Um,
[00:20:29] Emily: or they're,
[00:20:30] Emily: I guess, lagging the conversation. But, um,
[00:20:33] Elle: okay, so there's the trackers that
[00:20:35] Emily: yes, there's, there's absolutely workarounds and they will be more manual, but you can get really the same level of output, or at very least you can get the outputs that you need to perform your own individual analysis or to use a tool like chatt, Claude, Gemini, whomever it may be. you can get the transcripts, pull them, and then analyze them in [00:21:00] another LLM,
[00:21:01] Elle: Yeah. Okay. So you're still using ai. It might take a little bit longer,
[00:21:05] Emily: right.
[00:21:06] Elle: but
[00:21:07] Emily: But you can still do it.
[00:21:09] Elle: three weeks. Yeah,
[00:21:10] Emily: Yes. It just is gonna, it's gonna, like I said, it's gonna, that manual tracker, they're, they're good. They're not great. I'll be very transparent about that. Um, especially relative to the degree of granularity that I'm able to get into with this specific agent. But again, incredibly privileged to work with a team who's fully, uh, dedicated to building
[00:21:31] Emily: those
[00:21:32] Emily: kinds of tools for us. Um, but
[00:21:34] Emily: yes, there are,
[00:21:35] Emily: there are workarounds.
[00:21:37] Elle: Set. The way to set
[00:21:37] Emily: They're gonna, they're gonna ruin me. They're gonna ruin me. I'm gonna go somewhere else one day, and it's gonna be like, I'm gonna feel so dumb. I'm gonna be like, what do you mean? What do you mean I can't just automate this?
[00:21:51] Elle: okay, so step one is define the inputs. Step two is getting access to the data, cleaning the data, what's next?
[00:21:58] Emily: All right, so [00:22:00] finally, once we have all of this, it is getting into the actual analysis and kind of alluded to it earlier. You can just use a chat, GPT, create your own custom GPT or a gem or, I'm not sure what you call them in Claude, but, um, kind of use your, your tool of choice, uh, just as far as creating a custom GPT.
[00:22:19] Emily: So, one of the things I did was I consolidated all of the insights from across all of these different sources into a single doc that I fed to a custom GPT that I was able to use to provide the relevant context for the discussion at hand. Um, we were very fortunate when I worked at Clary as well to have, uh, someone who was an expert in chat, GBT come in and talk to us about best practices. we got some really great insights about prompting and about as you're setting these things up. Um, really making sure that you have super tight, clear prompts up front, uh, because of how quickly it loses its memory [00:23:00] across, uh, a lot of your conversations.
[00:23:01] Emily: So, um, I, I went through and I followed some of the best practices that we had been given, um, just as far as how to prompt this thing. Uh, and then I kind of went through and just started asking questions about all of the data that we had. Like, I didn't go into it knowing exactly what I was looking for and exactly.
[00:23:20] Emily: I knew that I wanted to create some sort of audit. I knew that I wanted to have an output at the end that was recommendations for how to update our messaging in this segment. And I knew that I wanted to be able to put it into a format that was gonna be consumable, in particular by our demand gen and our S-D-R-B-D-R team because they are the primary source for how we generate that top of funnel pipeline.
[00:23:47] Elle: you evolve that as you are working with the data. And I know we chatted a little bit about this as we were preparing for this conversation, but I see a strong comparison to even like when we would do [00:24:00] analysis before we ever had ai, sometimes you would go in, and I'm just throwing this out there, like we get a combination of qualitative and quantitative information, but like if you're jumping into a spreadsheet that has like thousands of rows of data or hundreds or whatever, like sometimes you, you just start manipulating it and as you're, you get your hands on it, then you start like, oh, well what happens if I look at it from this angle and what happens if I do this and what happens if I play with it like that?
[00:24:25] Elle: And I feel like it's really analogous to like when you're molding clay or I don't know if you've ever mixed paint before, but when you're mixing paint with some of the most basic colors, like, well, what happens if I add a little bit more blue or a little bit
[00:24:38] Emily: And sometimes you go a little too far in the wrong direction, you're like, oh, not that,
[00:24:42] Emily: um, because that absolutely happens. Um.
[00:24:45] Elle: We'll pull up Bob Ross and be like, make this a happy tree here. Like,
[00:24:49] Emily: Yes.
[00:24:51] Elle: okay, so once you've done the analysis, like what happens then?
[00:24:56] Emily: Yes. So once I got all of the output that I was looking for, [00:25:00] which was essentially a, um, full audit of, of our messaging across this segment, um, of how we were positioning today, how much that actually resonated with the expectations that our prospects had coming in. if that was being reflected in the questions that they were asking, the things they wanted to see, really the pains that they expressed during discovery are these things that. we're actually connecting with them. Um, and we did find some disconnects. So I, it was, uh, not a fruitless effort. Um, certainly there were some things where we were like, oh, okay, that's actually not our product strength today. Um, it might be in the future based on where we know our roadmap is going. Um, but for what we can do to today and how far ahead we're positioning, let's maybe not lean into some of these directions because that's actually, say, one of our competitors, uh, differentiators.
[00:25:50] Emily: So we don't wanna go head to head on something that we know we are not the strongest in. So instead, how do we pivot the conversations so that we are talking about the things [00:26:00] where we lead and that we know that they care about. So, um, ultimately what I pulled together was a document with updated messaging with updated.
[00:26:10] Emily: Use case needs, uh, who the personas were, what the KPIs that they are generally metriced on. Um, what are our value drivers associated with each of those? Um, how do we message those things? And then how does that change by industry or vertical?
[00:26:27] Emily: Um, and
[00:26:28] Elle: all of that within two hours.
[00:26:29] Emily: two hours, uh, yes, my boss, I shipped it to my boss at the end of the, like at the end of the, the workday because, uh, I'm in Pacific time zone and everybody else is in Eastern.
[00:26:39] Emily: And I was like,
[00:26:40] Elle: amazing.
[00:26:41] Emily: I was like, here you go. I was like, tell me if it's garbage or not. I've been here like a month and a half at this point a month. Um, so, and my first time again in the industry, so I was like,
[00:26:51] Elle: Yeah,
[00:26:52] Emily: I'm, I'm going based on what I think I know, but I need somebody who's, who's a little bit more seasoned, [00:27:00] uh, to tell me if the things that I think I know are correct.
[00:27:02] Emily: He was like, this is not the level of depth I expected or the timeframe we expected. We, we thought you were gonna be stuck on this for an entire week. Um, and so I, I handed that off to him for validation. We also validated with a few of our key stakeholders across the business that, Hey, these are things that we would talk about.
[00:27:22] Emily: These are things that we hear about from our customers. This is where we are strong and where we get the best reaction. Um, and then we are able to feed that back, uh, again toward BDR and our demand gen teams. And we are pretty early in the process, but we are starting to update a lot of our ads, a lot of our top of funnel messaging, uh, just around these value props and these pain points and these, uh, kind of solutions focused messaging that we put together.
[00:27:48] Emily: So, um, the output, or at least the results as far as the, the quantitative metrics on the results of this DVD, but, uh, really the time saving from A-A-P-M-M [00:28:00] perspective. Uh. Incredible.
[00:28:02] Elle: can I just say, you mentioned this and I don't know, maybe you're just kinda like speaking off the cuff a little bit, but like your manager saying, or your boss saying, yeah, I thought you'd get back to us in a week. I'm like, A week without any of these tools. Try a month like
[00:28:17] Emily: uh, yeah, no, this was, we thought a week with the tools, like we did
[00:28:21] Emily: not think, but again, so much hinged on. Having the right information, having the right tools in the tech stack, and then being able to, and this is a, a great, I saw somebody post about this, uh, on LinkedIn recently, where they were like, Hey, it's really great that we have the tools.
[00:28:37] Emily: How do we start training the systems thinking for how do we tie these tools together and use them in a way that we're A, leveraging their strengths? And B, we are coming up with kind of a trusted output because a lot of people may not seem, may not think to combine, you know, the, the four or five different tools [00:29:00] that I was immediately like, Ooh, I know I have this, I know I have this.
[00:29:04] Emily: I know this is what it's good for. I know this is how I'm going to weave this together so that I can get all of that data I need for the analysis. And then to even be able to be like, oh, I should make sure that I have these custom prompts set and that I have a custom GPT set up with all of the context immediately woven in.
[00:29:22] Elle: with experience, a seasoned PMM can figure this out, right? Because, and I say that because with the experience, you know exactly what the journey should look like. And when I say journey, I mean like the journey of like, like your playbook that you just spelled out for us. you know exactly what inputs you need. You know exactly what the questions you need to ask regardless of whatever tools you have at your disposal. You as an experienced seasoned PMM know how to do the analysis to get the information that, or to get the result that you need or to answer the question at hand.
[00:29:56] Elle: Right? And for your case it was, you know, do we [00:30:00] have a messaging gap for this particular segment and. Then the other experience that comes into play is just experience with the tools. So now you know what tools are out there because you've educated yourself and you've, you know, played around with them.
[00:30:15] Elle: You've gotten, also gotten some trainings, which you're lucky to have. I think that's kind of what it comes down to, at least for now, in terms
[00:30:22] Emily: Yeah, I agree. I think also one thing it comes down to is truly exploring the tools in your stack and not just thinking about them from a surface level. Um, a good example is, um, it's one of my absolute favorite tools. I actually have a friend who works there as an ae. We work together at Clary. he and I will send text messages, just kind of bouncing ideas off of one another as far as what are things that we could do.
[00:30:53] Emily: When most people think about Glean, they think about enterprise search behind the firewall. and that is like truly my number one [00:31:00] use case. It's funny, my friend Harjap, he's like, oh, it's got so many other use cases. I'm like, yeah, I know that, but this is the one that I use 90% of my day. Um, but like the ability to set up agents in Glean, I think is one of the biggest differentiators and some of the most.
[00:31:16] Emily: Savvy with AI pmms that I know have been really diving into these tools beyond those like surface level use cases. Like we have a tool called One Up that we use. Uh, our, our SE team uses it for RFPs. and they, they fully manage it. Uh, and it's, it's a wonderful tool that we can also use and that I think people don't always think about what other tech you may have in your company that you could get a seat in, because they're secondary use cases that are incredible for pmms.
[00:31:53] Elle: yeah. And that was part of your step two process and suggestion, right? When you were saying to [00:32:00] identify the, um, ident, like, so your first step was to identify the inputs, but then the second was to understand, like accessing the data, but you said to understand the tools and to ask whoever, ask your employer, like, what tools do I have available for this?
[00:32:16] Elle: Or, and if you don't wanna, if that's too broad of a question, maybe it's like, you know, what categories, like you as the pm m should know what all, who all the players are in those particular categories. Like for example, call recording and ask, do we have a gong or a Clary or X, Y, Z?
[00:32:31] Emily: Yes. And that was a lot of times it's, I'll get like a random Okta notification that will be like, you have been added to this tool. And I'm like, well, tell me more.
[00:32:41] Emily: Uh,
[00:32:42] Elle: Ooh, what's that one? Yeah, exactly.
[00:32:44] Emily: Exactly. Like even little things like, I'm like, oh, you gave me Tableau access.
[00:32:48] Elle: Yeah. Look what's know how I'm gonna slice and dice that data?
[00:32:53] Emily: You don't even know I'm gonna be using all this.
[00:32:58] Elle: it's so funny. [00:33:00] Uh, okay, so back to your process. Um, I think you might have gotten into this already, but you now you have this beautiful report and then is the next step to like shop it around. You just sent, sounds like you just sent it off straight to your boss, obviously right away. But then is there a, a, like a process or part of the step that you would say is to like, meet with the stakeholders, like with demand gen?
[00:33:21] Elle: Did you actually like, physically get in like a virtual room and talk about it?
[00:33:24] Emily: we shopped it around and said, Hey, uh, we also, uh, met with some of our, our friends over on the sales side of the house, uh, and also on the product team, uh, just to be like, Hey. Do these hit, does this resonate? Also, this is the direction that we're going as a company, so let's hope it resonates.
[00:33:42] Emily: Um, but yes, we, we got in the room, we got their, their blessing. we really didn't even make too many tweaks, like pretty minor, which is fantastic. Um, so once we got that approved, now it's, it's with the demand gen team. And like I said there, it's starting the [00:34:00] process of updating our ads, of getting, uh, some other top of funnel, uh, assets out the door, uh, that are kind of associated with this.
[00:34:10] Emily: And we know that it's gonna be a while, typically before, uh, you start to see any results off of that. But, um, we are hoping that as we influence those ops as they come in, we'll see a bit more qualified buyer.
[00:34:22] Elle: Yeah. So would you say that that's the last step is just to validate after you, so the, the validation actually isn't necessarily the blessing from your stakeholders. It's like the feedback from the market. Like, okay, well now let's test it.
[00:34:36] Emily: this is the thing, right? Like marketing kind of never dies. It's never done.
[00:34:40] Elle: for us? Never done.
[00:34:41] Emily: it's never done. Um, you, it might be off your plate for a minute, but, um,
[00:34:46] Elle: A campaign will end, but the story is
[00:34:49] Emily: It would,
[00:34:49] Emily: it will not. Yeah, exactly.
[00:34:50] Emily: And we are going to continue and, and we'll see like what worked, what didn't work.
[00:34:57] Emily: Realistically, we changed nothing else in [00:35:00] this process, aside from the message. So if you're, you're looking at an AB test, the before versus the after, we should get some pretty solid results. Obviously everything is not completely same, same, but, We will continue to see as the results come in, once we start seeing those results, we'll start tweaking, we'll start optimizing.
[00:35:21] Emily: We'll probably start this process all over again.
[00:35:24] Elle: yeah. You can use that agent and, uh, the, the process with all your tools that you've already followed.
[00:35:31] Emily: yeah, I, it's, now that I know how to do it, um, Hey, who knows? Maybe we'll get it down to an hour and 45 minutes instead of two hours.
[00:35:40] Elle: Right. You just keep, keep getting it faster and faster. I love it. It's so, it's so modern. PMM.
[00:35:48] Emily: every day I am in awe. I'm simultaneously terrified of ai and so in awe, it's so, um, I, I hate it for so many reasons
[00:35:56] Emily: and
[00:35:56] Emily: love it
[00:35:57] Elle: and cool. I know. I think [00:36:00] you are not a, you're in company in those feelings. For sure. Um, okay. Well this is my last question for you before we move on to the next segment of the show. Uh, what advice do you have for a product marketer who's trying to build an AI agent or like leverage some of these tools specifically to do like a message gap analysis?
[00:36:22] Emily: Just start, like I, I, I loved your clay analogy. Sometimes you don't know where it's going until, until it starts going, um, simultaneously, uh, don't be afraid to advocate for yourself, to get, uh, a seat if there is a tech within your stack that you want to use. Um, I, I think that there is immense value here, and if you can take it and prove the case of, Hey, this is something that we can use to, you know, think of, I think of that increase of six to 7%, even if we got it down to three to 4%, what [00:37:00] does that 3% look like?
[00:37:02] Emily: In terms of value, what does that 3% of additional revenue look like for this company? I, I think being able to frame it in the types of outcomes that you're
[00:37:11] Emily: trying to drive can help you get access to the tooling that you need. But like I said, also just dive in, just get going. the longer you wait to learn how to use these tools, the longer you wait, how to use ai.
[00:37:25] Emily: The further behind you get somebody else who is learning how to do
[00:37:28] Emily: this
[00:37:29] Emily: already. Uh, and that's, that's not to be like foreboding, it's just the way the market's moving. like I said, I recently joined a Chabo in the interview process. Uh, I was talking to a couple other companies at the time as well, and the number one question everybody asked was, how are you using ai? this isn't just something that can help you today. It's something that can help you with your career down the line.
[00:37:52] Elle: absolutely. I love the way that you phrased that and it can sound really daunting to, for someone who's [00:38:00] never done it before or seen how it's done to, Hey, go build an AI agent. It's like, what?
[00:38:06] Emily: a glean is, I won't say it's shockingly easy, but they have so much documentation and their team is so wonderful. If you can find a contact there, if you can speak with your, if you can get your team to connect with the CSM for your account. like, like we had multiple, like we had the glean CMO come and chat with us at Clary, um, which isn't to say they'll, they'll pop in for everybody, but, um, they, they saw the value in marketing teams building their own agents and they have plenty of people on staff who are willing, and I I'm sure that's the same for pretty much any of your, your AI partners.
[00:38:43] Elle: Yeah. And what I think what I'll compare it to is like with any fear that you have, you've built it up in your head to be bigger than it actually is. So
[00:38:55] Emily: Also, if your agent doesn't work, it's just gonna pop off and say,
[00:38:59] Elle: [00:39:00] exactly, that's what I was gonna say. What's the worst that could happen? Oh, that didn't work.
[00:39:03] Emily: Yeah,
[00:39:04] Elle: It's fine. It's okay. Try again.
[00:39:07] Emily: yeah, yeah. You're like, oh no. Now I need to submit a ticket somewhere 'cause I don't know what I'm doing. Whatever.
[00:39:13] Elle: whatever. Yeah, that's fine. Career is not over and you still have time left in the day 'cause it's AI and it only took an hour. I'm like,
[00:39:21] Emily: Right. Exactly. Hmm.
[00:39:23] Elle: okay. Well this was so fun. Thank you so much for sharing such an amazing use case and um, I'm so excited for the validation for you and the message gap. I hope, I hope it delivers some great results.
[00:39:36] Elle: alright, now it's time for the next segment of our show. This is the messaging critique. It's so fun. It's where we as product marketing experts get to analyze real world messaging. And the fun part is, Emily, as my guest, you get to pick the company that we critique.
[00:39:53] Elle: So before we get started, I'm just gonna list a few, ground rules for anybody who may be new to this segment, um, or new to the [00:40:00] show. First, Emily is going to share the company that we're going to talk about. And um, for this one, I try to. Try to focus on a company where either we have, uh, we are the customer or we know the ICP really well, because it wouldn't be fair to critique messaging on a customer with an ICP that we don't even know.
[00:40:22] Elle: Like I could never critique, like cybersecurity. A cybersecurity
[00:40:25] Elle: product. 'cause I don't really know that use case. Like, not a way. Yeah. Emily, you're gonna tell me something that you're loving about it. Something you wish that PMM would've done differently, and then we're just going to creatively talk about it, um, in ways that that PMM can take the narrative to the next level. so without further ado, do you wanna share the company that we are critiquing today?
[00:40:45] Emily: I want us to critique with Love Granola. Um, so they are an AI note taker and, um, I was really introduced to, to them since I've joined at Chabo. And I no longer know what [00:41:00] my days would look like without granola. Um,
[00:41:02] Emily: really listening in on most every call. Truly, it's, um, also to be, be perfectly honest, I, I, again, I work, I'm in the Pacific time zone.
[00:41:12] Emily: All of my team is in the eastern time zone. Um, and then also the, all of my product team is in Italy. So, uh, from a hours perspective, I tend to be a bit behind and I knew, uh, coming in at some point I was gonna sleep through a 6:00 AM meeting. Uh, and that
[00:41:27] Elle: Oh, it happens.
[00:41:29] Emily: It happened yesterday. So,
[00:41:31] Emily: um,
[00:41:32] Elle: to all of us. Trust me, there's not a human being on the face of the earth that, that, where
[00:41:37] Emily: I I realized my mistake when I was like eating a bagel at 6 45 and I was like, looking at my calendar and I was like, hold on. There was a meeting 45 minutes ago, uh, and
[00:41:48] Emily: I was like, Ooh, that was, I should have been a part of that. Um, and so I kind of went to my team Slack channel. I was like, Hey, did anybody record that call?
[00:41:55] Emily: And they were like, no, but we have the granola notes. And so, um, sent me the notes.
[00:41:59] Emily: [00:42:00] I didn't, I didn't need a recording. I got everything that I needed out of
[00:42:03] Emily: the
[00:42:03] Elle: That's
[00:42:03] Elle: awesome. That's awesome. Okay, so for those of you who wanna follow along, we're going to granola.ai. That's G-R-A-N-O-L a.ai. Uh, okay. So now that we have a good sense of what the product is, walk us through their messaging. What's, what's standing out to you?
[00:42:21] Emily: I love just how simple and straightforward their messages, um, having come from. The rev tech industry, and everybody has some version of a call recorder these days. Um, to be perfectly frank, it's pretty commoditized. there's so many options to
[00:42:41] Emily: choose from. Um, and what I, I love about granola is that they're literally like, Hey, we're an AI note taker for people in back to back meetings. I don't need any more information than that. That's exactly the scenario that I am in. Uh, and that is exactly what I need help with. I have so many back to back [00:43:00] calls. I think also, um, one thing I really value when I am in calls, when I'm in a customer call, when I'm in a call with my product team, I like to be present in the moment, and I like to be able to ask questions without being distracted by taking my notes over to the side of the screen.
[00:43:14] Emily: Um, every so often I will, when it's something super important where I'm like, Ooh, I really need that nuance or that, that context. But for the most part, um, I love that with granola, I can be present in my meetings. And I, I think that that's something that's actually underrated that people don't think about.
[00:43:34] Emily: And if I were kind of angling the message at granola, I think that there are some very more emotionally resonant messages that you can add because a, I love. I'm not also, I mean, like I, I do generally have a harder time critiquing other, uh, other product marketers outputs just because, um, like we said, we don't have the full context.
[00:43:59] Emily: We don't know what they're [00:44:00] being asked to do. We don't know what, what their, uh, mandates have been from leadership. And I think for what they have on the site today, it's great. I think that they have options to deepen it. Uh, and I think that there are some of those, like I said, emotionally resonant angles that they could take around things like, Hey, be present in your meetings. I think that's where, when I look at what they have today, I am like, yes, But also
[00:44:24] Emily: why you versus a competitor? Because again, everyone seems to have, uh, some sort of call recorder, some sort of note taker. What is it about granolas that is special or unique?
[00:44:37] Elle: I think that's such a clever tip. Um, hitting in on the emotional aspect of it, I'm imagining like, some, as someone who's also been in like back to back meetings, how I want to be present and how d how some of those meetings, it's hardly hard to like context switch as well. so often when I'm in a meeting, I'll have missed the last one be if it's like a recurring meeting because I had [00:45:00] a different meeting that I had to attend instead.
[00:45:02] Elle: So if it's like, oh, I didn't have time to watch the recording because that was, if I watch a recording, then I might, that's like having another meeting. And if I already have back to back meetings, then I need a meeting to watch the meeting recording. And it's just like, basically, right? 'cause I have to block off my calendar so that I actually have time to watch the recording and yeah, I can like scan through the notes.
[00:45:23] Emily: like, there are so many great angles that I feel like they could play with in their messaging. Like what are the scenarios in which you would be very thankful to have a tool like this? I think of, we're obviously thinking it from a of the lens of the PMM, but what is it from the lens of the sales guy who, or the sales gal, the sales, they, uh, whoever it is that, um, like they're in back to back meetings.
[00:45:49] Emily: What are they missing? What are those key components? I think about if I'm gonna go into a conversation I'm in back to backs with different prospects like. I have forgotten, or, [00:46:00] oh, this is a great one. Think about like a recruiter. Think about a recruiter who is, or a hiring manager who is out there trying
[00:46:07] Elle: yes.
[00:46:08] Emily: track of all these people, right?
[00:46:12] Emily: Like how do you I will say one of my favorite things and like, I don't know if, if everybody's seen like the granola unwrapped, their kind of, the, their take
[00:46:20] Emily: on the Spotify Yeah.
[00:46:22] Emily: Crunch.
[00:46:22] Emily: That's what it's
[00:46:22] Elle: their, on their homepage. On
[00:46:24] Elle: their homepage
[00:46:24] Elle: they say
[00:46:25] Elle: Celebrate your 2025 with crunched,
[00:46:27] Emily: is again, such great. I'm like, please continue to enjoy.
[00:46:32] Emily: Like, I think one of the things that makes me so sad for a lot of the enterprise companies out there is the way that you lose creativity because you must sound buttoned up and professional. And I love that granola gets to keep the creativity. You get to have crunched, uh, like wonderful, how fantastic that you get to play with things and be like that unique. I think that when I see my notes from granola, they are shockingly accurate.
[00:46:59] Elle: [00:47:00] they summarize conversations so that it's more human and less like robotic.
[00:47:06] Emily: yeah, I feel like granola really understands the context of the conversations and understands the sentiment and intonations and, really where we pointing the emphasis of these conversations, which I haven't necessarily seen in a lot of other tools.
[00:47:22] Emily: So I was, I was just gonna say if they like, really like start pulling more from the, like, I think they have a wall of like testimonials, which is great. But like, I think these are the kind of stories that you, you don't necessarily drag out of people from a LinkedIn post or from a Twitter post or whatever.
[00:47:39] Emily: Um, I, I think that they have an opportunity to like really tee in on how are the users using you today and how is that special? Or what is it that makes it uniquely relevant to their day?
[00:47:53] Elle: I totally agree with you. Yeah. I mean, overall they do such a great job telling their story. I mean, like even [00:48:00] on their website, how they show kind of like the before and after and the way they talk about how it works and, um, you know, basically they do a really good job of show don't tell or, or actually, which I prefer show and tell.
[00:48:14] Elle: 'cause I think in marketing you have to do show and tell, gotta make it like stupid easy for your customers to know what
[00:48:20] Elle: you, you don't wanna leave, you don't wanna, your customers do. the work, you know?
[00:48:22] Emily: yes. And I think there's also like, if so many companies are so eager to expand and become platforms and like when that happens is when your messaging tends to get diluted. And I think that's one of the perks of solely focusing on. You know, call notes today. Um, who knows?
[00:48:42] Emily: I would say who knows how they're gonna evolve in the future. I see they've got some agents and workflows in the works. Um, but I'm like, when I go to Granola's website, I know what they do immediately sometimes the simple message gets overridden [00:49:00] in favor of
[00:49:00] Emily: buzzwords. Um,
[00:49:02] Emily: and
[00:49:02] Elle: Everything's unleashed and unlocked and it's, you know, powered by something.
[00:49:09] Emily: they, they tell you a lot without telling you anything. And I feel like I went to granola and I was like, yes. AI powered note taking for people in back to back beatings. Thank you. Done. Stupid Yeah. Done. Easy. I get it. Yeah, I know. I love it. Well, uh, great job, granola, pmms. We love your narrative
[00:49:29] Elle: and your storytelling. We're big fans. Yes. Great job. Uh, all right, so Emily, one thing I like to make space for and this podcast is a moment of gratitude because in product marketing we never get to where we are alone.
[00:49:40] Elle: We're always stealing each other's playbooks and iterating on them and learning from each other, and we're all better for it. So, uh, before we wrap up, I just wanna say a quick thank you for taking time to do this episode with me. It does take prep work beyond this conversation, and that's you [00:50:00] being generous with your time and willing to share with the PMM community.
[00:50:03] Elle: So thank you so much for one, just being a badass and then two being willing to share it.
[00:50:09] Emily: Well, obviously thank you for creating a space for pmms to share their stories. I know you and I chatted when we finished it up with one of our prep calls the other day. you know, how do people learn? How, how do we pass on, um, things over generation is through storytelling, right? And like, one of the reasons I got started posting on LinkedIn in the first place is because I was a founding PMM at a series a startup, and I just wanted to know if I was doing things right or if people had better ways.
[00:50:41] Emily: And I really hadn't found a place where I was able to do that to that point. And LinkedIn became that place. And I love seeing. Other people's stories. And I love podcasts like yours where people are coming in and they're telling you, I tried this this way, it did or [00:51:00] didn't work.
[00:51:00] Emily: Um,
[00:51:01] Emily: and, and being able to like really share the learnings and kind of be, Hey, here's how I failed.
[00:51:07] Emily: So you don't have to, it's that it's that building in public, but really for your own career.
[00:51:11] Elle: I love it. Yeah. It's my favorite part of being a part of the PMM community. Everyone is so willing. Share and teach. It's just so lovely to be here.
[00:51:22] Elle: who has brought you to this point in your career? Who are
[00:51:25] Elle: the
[00:51:26] Emily: me, I like literally just wrote, uh, a LinkedIn post recently. About the people who've shaped my career as a PMM. Uh, and I am very fortunate to have worked with some incredible leaders, um, people who have, uh, kind of given me the, the real talk I've needed when I was early on in my career and still trying to figure out what to do.
[00:51:48] Emily: The people who have helped guide me, who've given me best practices, resources. Um, so I want to first, uh, sorry. It's gonna be a list. first
[00:51:56] Emily: and foremost, uh, David Tigar and [00:52:00] Taad, uh, they were my PMM leaders at a little company at Series B, uh, became series C while I was there. Uh, startup called Data World.
[00:52:08] Emily: Um. Really, I had been a solo PMM before that and had not had direct guidance on how to become a PMM. And those two were the ones that really gave me the, the first foundation. Um, I transitioned from content marketing into PMM and Tazy really gave me a kick in the pants where she was like, Hey, you're trying to be a content marketer with a product focus and you need to become a real product marketer.
[00:52:32] Emily: And I remember just being a little bit dumbfounded that day, but then I was like, she's right. Like I gotta, there's a lot more that I can and should be doing, uh, rather than just writing content. So, um, very thankful for them.
[00:52:45] Emily: Uh, then Sonia Maori, she is actually currently the director of product Marketing over at Skill Jar, which is one of de Chabos, uh, competitors to agree.
[00:52:54] Emily: So it's been fun, um, now that we, now
[00:52:57] Emily: that we
[00:52:57] Emily: get to compete against each other. But, [00:53:00] um, Sonya was my, uh, VP of PMM at Clearbit, and we only gotta work together for a short time, but. No one has probably given me more playbooks and frameworks than Sonia. And she had a lot of belief in me and a lot of, uh, like freedom.
[00:53:18] Emily: She gave me a lot of freedom, but also gave me a lot of, uh, guidelines to work within. So Uh, my friend Maggie, she, she started out as my boss, uh, at Rattle. we worked together very long there.
[00:53:29] Emily: Either I might be the angel of death, I'm not sure. Uh, but, uh, after she left, uh, she became one of my really close friends and mentors that I've been able to bounce things off of. Um, so we're, we're still in touch. We worked together for maybe two months, well, she lives in The Bahamas, which is not a bad connection to have.
[00:53:46] Emily: So, uh, I've been able to go visit her, uh, there and then also.
[00:53:51] Emily: just at Clary I had, uh, the opportunity to work under quite a few different PMM leaders that super appreciative of Julian Sage, uh, [00:54:00] Ryan Beum, Ava Covell, April Rossa. Um, each of them gave me something that I have walked away with and I am so appreciative of all of them.
[00:54:09] Emily: And we are who we are because of the people who put their faith in us and teach us and train us. And now I'm currently reporting into Ben Bataglia at De Chabo, and I love Ben. He is wonderful. He is an incredible advocate. He, again, somebody who gives me a lot of freedom and also isn't afraid to gimme feedback.
[00:54:28] Emily: Uh, so I, I think like sometimes you get bosses that just kind of pat you on the back and tell you to go on your way. And he is not
[00:54:35] Emily: that person. He will absolutely be like, Hey, I think we should do this this way. And I'm like, great. Cool. Let's do it. Um, but. Always the people who I've been very fortunate to have leaders who push me to be better.
[00:54:47] Emily: And
[00:54:48] Emily: I, I just have to say better.
[00:54:49] Elle: I know I've said this a few times, but you are so lucky
[00:54:53] Emily: I, it truly, like, I put that list together for that LinkedIn post and I was like, damn, I had some great leaders.
[00:54:59] Elle: [00:55:00] Yeah.
[00:55:00] Emily: I am an amalgamation of all of them.
[00:55:03] Emily: So, uh, that's, that's how I got here today. And I wouldn't be here without them.
[00:55:07] Elle: Oh, I love that. Yeah. And big hug and shout out to all those amazing PMM leaders and friends. Um, and I know we've mentioned LinkedIn a few times now as well. Is that the best place for our listeners to find you? I,
[00:55:21] Emily: That would be, I am like, not on any other socials, which is weird to be like, LinkedIn is my only social media, um, and TikTok, but nobody follows each other on TikTok. TikTok is for aimless, scrolling of strangers. Um, so yes, LinkedIn would be, LinkedIn would be the place.
[00:55:37] Elle: I love it. Well, thank you again, Emily. This has been such a wonderful conversation. Um, and thank you PMM listeners for coming on this adventure with us today. I hope this episode leaves you with inspiration to take in the next step of your own journey.
