‍A conversation between David Patterson-Cole, Co-Founder of Datalane, and Amanda Lam, Senior Manager of Strategy & RevOps at Gloss Genius, recorded live at Vertex 2026.

About the Speakers

David Patterson-Cole is the Co-Founder of Datalane, the go-to-market data layer for companies selling to the local economy. Datalane works with large enterprises and fast-growing vertical AI companies to accelerate revenue through account scoring, campaigns, and rep efficiency initiatives.

Amanda Lam is a Senior Manager of Strategy and RevOps at Gloss Genius, an all-in-one platform in the beauty and wellness space handling booking and payments with over $100 million in revenue. When Amanda joined two years ago, the sales team had just 2 reps — today they have 48, with plans to double again by year-end.

Summary

Gloss Genius grew to $100M+ in revenue on inbound alone but when the company bet on going upmarket and into new verticals, inbound couldn't keep up and scaling ad spend just inflated CAC. The shift to outbound exposed a cascade of challenges: gatekeepers instead of decision-makers, messy third-party data, and reps context-switching across too many verticals. To fix it, Amanda and her team invested in data foundations: entity resolution, TAM visibility, and CRM integrity before layering on ML-powered scoring and AI-generated local social proof for outbound openers. The final and hardest piece: getting a rapidly growing sales team (2 reps to 48 in two years) to actually trust and use the systems instead of defaulting to manual research.

"With sole entrepreneurs and small teams, these are intuitively lower ACV, which means for us to hit revenue targets, higher volume is needed. We knew that we couldn't rely on inbound for our revenue targets moving forward as a business." - Amanda Lam

Biggest Takeaways

1. The inbound-to-outbound shift is a strategic decision, not a tactical one

Gloss Genius didn’t start outbound because inbound was broken. Inbound was working beautifully 100% of leads were decision-makers who came through the demo form knowing exactly what problem they wanted to solve. The shift happened because the company made a strategic bet to go upmarket and into new verticals, and inbound couldn’t deliver the volume needed for those higher-ACV segments. This is a pattern that repeats across industries: a strategic initiative (new verticals, upmarket expansion, new product launch) forces a go-to-market change.

2. The pilot is where you learn what’s actually hard

When Gloss Genius ran its first outbound pilot with broad criteria (2–10 employee teams across existing verticals and competitors), three problems surfaced immediately:

· Gatekeepers: Unlike micro-SMBs where the owner’s cell number is on Google Maps, larger businesses have receptionists and general staff picking up the phone.

· Context switching: Reps couldn’t build confidence when they were bouncing between med spa talk tracks and nail salon talk tracks call after call.

· Data chaos: Third-party data delivered via CSV meant hundreds of thousands of rows, laptop crashes, and low confidence in accuracy.

These aren’t theoretical risks they’re the actual friction points that slow down outbound programs across every vertical.

3. Entity resolution is the answer to the problem

In industries like beauty, wellness, home services, and restaurants, the fundamental data challenge is: who is who? Businesses use GlossGenius.com subdomains instead of their own domains. Their “business email” is a Gmail account. They don’t have LinkedIn profiles. There’s no clean unique identifier to anchor records on. Layer on franchise structures, holding groups, and PE roll-ups, and you get duplicate accounts, ownership disputes, and commission headaches. As Amanda put it: “Setting the foundation for data integrity is super important before you scale.”

4. The scoring framework that actually works

Gloss Genius built a two-pronged approach:

· Top-down: Product marketing identifies verticals and competitors where win rates are high → deploy specialty AEs to test those bets.

· Bottoms-up: Combine Datalane TAM data with Gloss Genius historical data → feed ML models that predict (a) revenue opportunity per account and (b) likelihood to convert.

The ML model unlocked non-obvious insights, like discovering that a high-revenue solo account could be more valuable than a low-revenue team — something that simple rule-based logic (just filter by team size) would miss entirely.

5. Change management is where most companies stall

The final chapter and arguably the most important one. With 48 reps and growing, Gloss Genius is navigating the gap between what technology enables and what humans can absorb. AI and data foundations can generate campaigns at incredible speed, but sellers still need to internalize talk tracks, trust the data in their CRM, and resist the urge to do redundant manual research. David shared that across multiple customers, they’ve proven that additional manual research (when the right data is already in the CRM) does not improve conversion rates. But proving it isn’t enough the change management to make reps comfortable working off CRM data alone takes sustained effort over six months.

"Setting the foundation for data integrity is super important before you scale, and it's something you really want to make sure is  correct before you ramp up hiring." - Amanda Lam

Three Things to Remember‍

A foundation four teams build on and a workflow RevOps can run alone

1. If you're launching your first outbound motion: Start broad, but start small. A pilot team of a few reps will surface the real friction points gatekeepers, talk track overload, data quality before you scale headcount into those problems. Let the pilot inform your ICP criteria, not the other way around.

2. The data foundation is one of the most important and most challenging stages. If you're drowning in messy data: Invest in entity resolution and data integrity before you hire more reps.

3. Change management determines whether any of it actually works. If you have the data but reps aren't using it: This is a change management problem, not a data problem. Prove with actual A/B test that additional manual research doesn't improve conversion when the CRM data is right. Then commit to six months of sustained enablement to make the new behavior stick. Technology ships fast; human adoption doesn't

"Entity resolution is basically who is who. In our specific industry our target audience doesn't have a data point that can serve as a unique identifier. Many of our businesses don't have unique domains or phones or emails or LinkedIn in the same way that other enterprise B2B SaaS companies do." - Amanda Lam

Watch the Full Talk

Full Transcript

Amanda: Awesome. Okay, so for you guys who do not know what Gloss Genius is, we are an all in one platform in the beauty and wellness space. We handle things like booking and payments and we do over 100 million in revenue. A little bit about how fast our team is growing. So when I joined the company two years ago, we only had two sales reps. As of this month we have 48 and plans to 2x that once again by the end of the year.

David: Awesome. I’ll keep the Datalane background short. You can think about us as the go to market data layer for companies selling to the local economy. And we work with many of the largest enterprises and fastest growing vertical AI companies ultimately to accelerate their revenue. And we do that through account scoring, campaigns, rep efficiency and many other initiatives. During this talk though, I want to focus on two things. One is kind of moderating the fascinating Gloss Genius case study and then second, sharing some kind of broader generalizations of how I’ve seen this apply to other industries since the shape of the problem is often very similar, but the specific details are different. Okay, but before getting into that, we want to make the session as useful as possible for everyone here. Thank you to those of you who’ve already submitted questions. You can see some of those on the slide already. I think there’s kind of two main themes that I’ve seen so far. One is this discussion on kind of the pros and cons of named accounts versus the territory approach. And second is custom data points specific to each vertical. We’ll definitely cover some of those throughout the talk. And then we’re also going to save time at the end for questions. But I just want to pause here. Is there anything else that’s top of mind for people in the room or questions that like to kind of share our front to make sure we cover them? No questions. Okay, well, we’ll leave time at the end as well, but we’ll kind of jump into the Gloss Genius story then. And the one last piece of framing I just want to set up front is a very useful framework for thinking about local go to market. Kind of encapsulates everything we’re going to talk about today, and that’s what accounts to work and how to work them. What accounts to work is mapping your TAM, ICP scoring campaign, CRM cleanup, everything in that realm. And then how to work them is the channels you’re choosing, the messaging rep efficiency. This is really all encompassing. We want to approach this talk in a chronological fashion, but as we go through, we tie things back to this overall framework. But maybe to start, Amanda, be helpful, just give people the very quick overview of the areas we’re going to be covering today.

Amanda: Yeah, I really like the way that this is laid out. Not only is it chronological, that’s really emblematic of the different chapters that we went in. This discovery of how to do outbound. These are the chapters. I’m really excited to go into it. It’s really emblematic of kind of the evolution of our outbound program. We’ll start with what was the kind of status quo when I first joined the company? How we piloted outbound for the first time, and then from there, kind of setting the foundation for data and then scaling the operations as we scale the team.

David: Awesome. Let’s dive into the inbound era. I have a bunch of questions here, but maybe one piece of context for everyone. Where were these inbound leads coming from?

Amanda: Yeah, so two years ago, when I joined the company, we were doing 100% inbound. Our company had a really strong loyalty fan base, and it was really strong on the performance marketing side and word of mouth. And so I’d say those are the two main channels from where we were getting inbound leads.

David: Got it. So performance marketing, word of mouth, and then, what were you noticing about these leads? What was true with kind of the inbound leads? What were the good parts and the bad parts of that?

Amanda: Yeah, well, it was 100% decision makers and to be clear what that means is when people would come in, they would come in on our marketing site through our inbound demo form. And I was looking at our inbound demo form submissions the other day, and we have a question on the form that literally says, how can we help you? And so an example of a recent response is something like, hey, I’m using Square. I don’t love that all of the features are add ons. And I need a platform that supports payroll reporting, et cetera. And so that’s a decision maker. That’s someone who knows what they want, they know the problem, they know the gaps, and they’re actively looking for a solution. So in that way, our inbound leads were quite clean decision makers. The owner is the business and the ICP finds you.

David: Yeah. And if somebody spends a lot of time in outbound, that seems very ideal and kind of begs the question, why change anything?

Amanda: Yeah. So with inbound, there’s kind of this plateau that you’ll eventually hit. So our ICP were originally solo entrepreneurs, and then we kind of evolved naturally to hitting the small teams market. But with sole entrepreneurs and small teams, these are intuitively lower ACV, which means for us to hit revenue targets, higher volume is needed. So that was the main constraint. And for that reason, we knew that we couldn’t rely on inbound for our revenue targets moving forward as a business.

David: And was the thought like new verticals upmarket, were those kind of the areas of the strategic decision to resolve that?

Amanda: Yeah, I would say that there are two core strategic business decisions that were made and we wanted to go up market, like you said, and across different verticals. And so we weren’t able to hit those up market and new vertical targets by just relying on people who are organically coming into us.

David: Yeah, that makes sense. And it’s very similar to the general theme of a strategic initiative that is critical for the company. And then to achieve that, there’s a required go to market change because the current business is obviously oriented around how the current business operates. And then you, by making this change, you kind of have the ability to control your destiny. So we see this, whether it’s new verticals, up market, new product launches, all of those different areas. So why don’t we get into what specifically changed if we move to the next slide here. And I know from our work together that you guys ultimately landed on outbound. But just before getting into that, why not just kind of scale this performance marketing engine, which you have a super strong function internally? Why not just focus that on upmarket accounts? Did you try that and did it not work?

Amanda: Yeah, that’s a great question. So when we were focused on solos and small teams, performance marketing was our bread and butter, specifically Meta, Facebook and Instagram. And so the main thing here is when you go up market, it requires casting a larger net for targeting. So every time we increased Meta spend on upmarket campaigns, our CAC would jump up. So, for example, this is illustrative, but we would spend 50k on Meta ad spend per month at a CAC of $100 per sales demo. If we wanted to increase the volume of team acquisitions from performance marketing by 4xing the spend, we actually found that 4xing spend to generate 4x the volume also 4x’d the cost of customer acquisition. So really at the end of the day, in evaluating the cost efficiency, we found it to be an extremely harder metric to control when trying to target larger accounts with performance marketing.

David: It’s probably also that you were actively excluding them before and still with the campaigns you were running, they weren’t coming in in high enough volume for you to use this as the primary channel to achieve the upmarket goals. And then as you alluded to, the classic diminishing returns as you scale up the volume. Okay, so performance marketing wouldn’t work and you turn to outbound. I’m a big proponent in kind of each of these new channels — just testing and learning as quickly as possible. How did you set up the initial tests?

Amanda: Yeah, so in the initial pilot, we had a pilot team of a few reps to kind of test outbound. And we didn’t want to get overly granular with the first pilot. And so we had really broad criteria that we wanted to test with teams where employee count was between two to 10 people. And then we used the same verticals and the same competitors as the dropdowns in our demo forum. So kind of started really broad, figured out if there are patterns or nuances with the pilot. And then from there we figured we could get more granular, but started broad at first.

David: Got it. So that was kind of the setup. And then what did you see when you actually had the team start making these calls? This is usually the point where people run into and start uncovering all the issues, which is why you run the pilots. But what did you see when you started running those tests?

Amanda: Yeah, I would say, in contrast with the first slide where we have this nice clean 100% decision maker, we are starting to run into gatekeepers. And for those of you who are not familiar with that term, that means we’re picking up and talking to general staff at the company, maybe receptionists, people who did not have decision making power and having that business change their software. And so that was the main thing that we were running into. And I’d also say that first point that I brought up, that we started really broad, there were so many different verticals and competitors and different permutations that we were going after. The reps were continuously needing to context switch between different talk tracks that our enablement team had built out. So let’s say they were selling why Gloss Genius wins with med spas. The next call, they had to pivot to hair salons and nail salons. And from a change management perspective, it’s really difficult for them to get specific training that really enabled them to build confidence on why Gloss Genius wins with various accounts.

David: The campaigns, eventually you can set up so that they’re working a set — this week is all this campaign, the next week, etc. But both of those make sense and I actually want to drill into the first point on gatekeepers because this is actually really important. It’s an industry-specific nuance. If you’re selling to the smallest businesses in a number of different industries, whether it’s the beauty and wellness space or home services, if you’re selling to the micro SMBs, there is not a decision maker connect problem. If you call a landscaper and the company is just a guy in a truck, when you call the number on Google Maps, the number on Google Maps is the owner’s cell number. At that point in time there’s not actually a decision maker connect problem. But then when you start going up market this changes. Different problems will emerge not only for different industries but also for different segments you’re focused on. And it’s really important not to just apply a general framework of “let’s get decision maker direct numbers for everything” because it won’t actually be beneficial. But okay, those two points make a ton of sense and that’s kind of the sales side of it. What was your life like at this point on the RevOps side?

Amanda: Yeah. So I will say when you go outbound the natural acceptance that you have is that the data is going to get a lot messier instead of people self-reporting. You’re relying on third party data and hoping that it’s accurate and correct. And so when we first launched this pilot, we were working with an agency who would provide us with this third party data via CSVs. And you also know with outbound that it needs to be super high volume in order to generate volume at the end of the funnel. And so me personally and my team, we were working in CSVs with hundreds of thousands of rows. I have a MacBook, my computer kept on crashing every time I tried to run these transformations in the CSVs. And so I would say the data hygiene, operationalizing it is very, very difficult when we’re working with these static CSV files.

David: Yeah, and I’ve seen this so many times. I’ve also crashed my laptop so many times. But this transition from CSVs — you can use intermediate steps like Row Zero. Eventually you get to the proper data warehouse setup, the ETL, reverse ETL systems between CRMs and data warehouse. We can talk about the architecture later too. But yes, this is a common problem. And it sounds like getting that data foundation set up was kind of one of the first areas you tackled. Both fix the sales problems and also the RevOps problems. Can you talk me through how you thought about the data foundation?

Amanda: Yeah. From a data lens, there are two main things that we’re looking for. One, we needed higher data integrity and fidelity in order to do the immediate term needs, which were things like segment ICP accounts, build territories and build pipelines. Those are things that we need in the short term and really immediately to feed this outbound sales motion.

David: And what were the specific data points that you were looking at?

Amanda: Yeah, of course. So the key things that we needed were DM context, verticals, team size and kind of full visibility into what was out there. I would say those are fundamental things that we needed to be accurate.

David: Okay, got it. And then that kind of powered the data integrity piece for the ongoing campaigns. And then you mentioned there’s a second area as well.

Amanda: Yeah. So this second was more of a medium to long-term strategic play. But it’s really this idea of TAM — what was out there. Not to feed my books for this week or this quarter or this half, but what were the strategic areas in terms of segmentation that the company wanted to expand into? Things like what’s out there for expansion into non-ICP? What was our runway size per segment? Having that bird’s eye view into what was out there in the market was this dual angle — data integrity for the immediate term, but this bird’s eye view for strategic long-term objectives for the company.

David: Yeah. And that strategic point is underrated. One of the things I always love to dig into is what actually changes once you have that strategic picture. Because usually some of the long held assumptions in the company end up changing and then that has a resulting effect on hiring, which verticals you’re focusing on, how you’re focusing on them. Was there something specific that changed for you?

Amanda: Yeah. So for our company, our strategic big bets are we really want to go into med spas and we really want to go into large teams, for example multi-location. And so our product is still catching up to us wanting to go into those segments. So it doesn’t make sense for us to deploy tens and tens of teams into those segments where we know the product is catching up. But what having this TAM visibility enabled us to do is create these pilot teams, the specialty AEs — we’re able to test into things like med spa and large teams in multi-location by having the TAM of what was out there and building these tiger teams to go after them.

David: Yeah, the specialization point is always a debate within companies, especially within vertical tech companies. But that’s a great example of one of the common things that change when you actually can see your entire TAM. So I can cover the first point. Second point, entity resolution — it’s a complicated word. I’m curious, what does it actually mean to you, Amanda?

Amanda: Yeah, so in essence this is the hardest question for us to answer. But entity resolution is basically who is who. In our specific industry our target audience doesn’t have a data point that can serve as a unique identifier. Many of our businesses don’t have unique domains or phones or emails or LinkedIn in the same way that other enterprise B2B SaaS companies do.

David: And I just want to dig into that for just one second because the domain point is interesting and counterintuitive. Gloss Genius is such a good example of that, right? You have customers that use GlossGenius.com as their domain, obviously a specific subdomain. But is that something you’ve seen — these different aggregators or Facebook or no domain that cause these problems?

Amanda: Yeah, that’s exactly right. So if you’re in the business of selling websites, for example, relying on customers who want your product to have an existing website is this loop that’s not going to feed itself. And so website’s a great example. And then our users, their emails, their business emails are also things like Gmail, Yahoo, Hotmail. And so deduping on that basis just doesn’t and will never make sense for our industry specifically.

David: Yeah, and this is a common one — HubSpot’s annoying rules on requiring specific emails, but half the contacts you have won’t have emails on them. This is something we see in terms of problems for the specific data points. And then one other one to call out as well is across industries there’s also these hierarchy complexities. Whether that’s franchise holding groups, how do they relate to the corporate structures, private equity roll-ups, hospitality groups. Each vertical has kind of their own flavor of this, but it makes it even more complicated. You start off by not having a specific data point to isolate an individual entity, and then the hierarchy structure across entities makes it even more complicated. It is one thing that’s really important to get right because if you don’t handle the entity resolution, you’re going to have multiple teams working the same account and just kind of a complete disaster there.

Amanda: Yeah, and I’ll chime in there. Working the same accounts is especially sensitive in sales because when you’re working with sales, there’s a lot of sensitivity around ownership and credit impact and how reps are paid out. So setting the foundation for data integrity is super important before you scale, and it’s something you really want to make sure is correct before you ramp up hiring.

David: Cool. So once you do have that data foundation in place, you get the entity resolution, you have the TAM visibility. How did you think about scoring?

Amanda: Yes. So I’m really excited about this. I feel like we had a very creative approach with scoring. And so it was twofold. One was top down and the other was bottoms up. So top down is your traditional formal hypothesis and test into it. We have a really great product marketing team, and they have an idea in the TAM where we have high win rates in regards to verticals and competitors. And so it’s kind of like deploying these specialty reps in areas where we know we want to win or where we believe that we have the confidence to win. The second is bottoms up. And so this is a really interesting thing that we’re enabled to do with having TAM data — we combine the TAM data with our company historical data to really refine who we should go after by feeding the data into these ML models. And so, in partnership with our data team, our data science team, I was able to help stand up two different models. One was an account score, which predicted the revenue opportunity of accounts in the market, and the second was a lead score, which predicted that account’s likelihood to convert. So the way that we talk about it at the leadership level is that when the opportunity in the market is larger than the capacity of our sales team, our efficiency is really dependent on weeding out the noise and figuring out who exactly we want to go after.

David: Yeah. And honestly, that framework you use — the top-down, bottoms-up approach — is really best in class. If I were to generalize this, it applies to every business. You’re looking at the value of the company you’re going after — LTV is the objective way to measure it — and then the likelihood to convert as the second piece. Likelihood to convert can further break down into: does the account have a problem you solve, triggers, and also reachability, which is very important in local too. But you have this framework of LTV and likely to convert, which is really important for building the bottoms-up model. And then you need to be combining the TAM with the historical data, building lookalikes. I think you guys are really doing it in a best-in-class way. And then I just wanted to give one other example. In the retail space, different products are optimized for different segments of the industry. One of the complexities is inventory management. We work with a few companies where their product outperforms on complex inventory situations. So the metrics they’re tracking are things like how many SKUs does the business have, do they have items in clearance, do they have items going out of stock. Those tend to be the features that get plugged into the model. If you don’t have the right features that matter for the business, that’s where the top-down hypotheses also come in. Each industry is going to have a different set of features, but the general approach can be applied. Okay, let’s get into some of the specific examples. What were you able to do with the data?

Amanda: Yes. So this is one specific example that I’m excited to share. Our sales team for many months has been telling us that selling locality — pitching local Gloss Genius businesses geographically close to the accounts that they’re working — has always been valuable. But there was no way for them to do this at scale. And so what I was able to do is I built scripts using the Anthropic API that cross-references the geos of current Gloss Genius customers with accounts in the Datalane TAM dataset. And so each account executive was able to get a personalized snippet for the accounts that they were going after of Gloss Genius businesses local to them. Local businesses buy on local proof. We’ve always said that word of mouth was a really strong channel for Gloss Genius. And so this tactic was just a way to build this dynamic outbound opener into a way that was scalable for the entire team.

David: I love it. And I’ve seen this social proof idea work really well across a ton of different companies. Once you have the data foundation set up, it’s relatively easy to spin up campaigns like this and you can test so many different ideas. How does it work with just social briefs? Can we reach out to existing customers and get them to refer, for maybe not direct competitors to their businesses, but related complementary industries like a nail salon and a hair salon? You can run these campaigns very quickly and this is where it gets fun after you do the hard work of setting up the data foundations.

David: So we’ve talked through a lot of the journey, a lot of the challenges, a lot of the changes you’ve had to make. It’s often hard to make those changes without having an objective standard that you’re measuring by. Nobody gets this right the first time. Even after the first time with the pilots, they get everything wrong and learn a ton. Then once you set up the data foundation, there’s still so much learning that happens in the first six months. So getting the metrics right is super important. What are the metrics that you guys focus on at Gloss Genius?

Amanda: Yeah, I really like the way that you frame that. As you’re launching new pilots and motions, things are very subject to change and iteration. One thing in our business that doesn’t change is activation. An activated paid subscriber generates revenue for the company and that will always be true. The way that our team approaches it is everything is backwards planned from activations upwards. We have things like the win rate, demos booked, the amount of decision makers we connect with in TAM. Something I want to stress here is that now that we have multi-channel — inbound, marketing qualified leads, cold outbound leads — there are so many different entry points of how to get into this funnel. But something that’s a key observation for the team is that once we get accounts to that third stage, demos booked, things really start to normalize in terms of down-funnel conversion. For outbound specifically, it’s really getting those people to that demo booked stage that is the key difference maker in our outbound sales motion.

David: One thing I’ll add is it’s really important to have this funnel because there are going to be more demos booked than there are activations. And when you’re building the ML models, you need as large of a sample as possible. So you’re going to work more off these upper funnel metrics, which are still very predictive. It’s really helpful to have that full funnel view. The one other thing I’ll call out is the definition piece. It’s very worthwhile to have a consistent definition internally, otherwise it’s impossible to use the historical data. It’s also worthwhile to understand industry definitions. The reason it’s helpful is because you can go not just off historical benchmarks, but also industry best-in-class benchmarks, to identify the points in the funnel that are underperforming and where you should focus your time.

David: I think the last thing we wanted to cover is that this isn’t just a technology change. We’ve talked about the theoretical — how do you build the optimal state — but none of that matters if it doesn’t get put into practice. Talk me through the change management element.

Amanda: Yeah. I would say this is kind of the current chapter or the final chapter that we’re in right now. And I think it’s really important to recognize that our sellers are human. So in the early stages of piloting and iterating, we could move as fast as the technology was moving, which is quite fast in the era of AI. We’d have the data foundations, the systems in place and we could deploy and retest and reiterate. At our current headcount — 2xing the team over and over again, now we have 48 reps and we’re trying to get to 100 — really having the ability to have a perspective on change management from a human’s perspective has been one of the biggest challenges and next evolution of our motion. With AI and all this data, we can send tens and tens of concurrent campaigns out all at the same time. But from an enablement perspective, trying to reconcile that with the human perspective and having the memory and the change management, the talk tracks for them as sellers to catch up, is something we’re trying to figure out in this hypergrowth stage.

David: Yeah, it’s so important because without this none of it becomes reality. You don’t see the gains. Another related change management piece around rep research — for a long time we’ve worked to get the right data into our customers’ CRMs so the reps don’t have to spend time doing manual research. But there’s been this change management element where the reps still feel like they want to default to doing their own research, even if you can prove that the data they find isn’t different. And we’ve had to run a bunch of tests to prove that if you have the right data in the CRM, doing additional research does not improve conversion rate. That’s really important because then you have to make the change management stick where reps don’t do additional research. It’s nice in theory to say we’re going to save rep time in research by putting the data in the CRM, but then if the reps still do the manual research, you don’t save any of that time. So change management is critical to actually seeing the results from all these different projects. This is an iterative process — you don’t immediately see 20% revenue lift the month after you roll out data foundations. You have to do the change management as well, and this is a six-month commitment to getting the results.

David: Cool. So maybe just to recap quickly, since we covered a lot of ground, three main points. One — why are we doing all this in the first place? There’s often a strategic initiative or change that requires a new go-to-market motion. Two — one of the most important stages is getting that data foundation that sets everything up. It’s also one of the most challenging ones. And three — I don’t want to underplay the change management side. As we talked about, it’s extremely important. Thank you all for taking the time to join us. And Amanda, thank you for sharing the Gloss Genius story.

Amanda: Thank you.