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Overruled by Data is the podcast for law firms looking to accelerate their data journey without all the pain points.
Hosted by Tom Baldwin and brought to you by Entegrata, each episode shares real-world stories from law firm leaders who’ve tackled the tough stuff—getting data from all the right places, navigating the AI hype, and scaling operations in a way that doesn’t leave you with a mountain of tech debt.
If you're in a leadership role at a law firm, this show offers valuable insights from those who've been there, sharing what works and what to avoid on your data-driven journey.
[00:00:00] Jason Kennedy: Every marketing leader I've ever talked to is dying to get to ROI. And for some of them, they're trying to be proactive 'cause they know the conversation's coming up. Some marketing leaders, the heat's already on, and leadership is trying to understand, what is it that you guys are doing over there in your marketing department?
[00:00:30] Tom Baldwin: There's an intersection in legal, most people don't think about until something breaks, where marketing strategy meets data architecture, and more firms are quietly wrestling with the same question: What happens to marketing when the firm's data lives in a lakehouse instead of a stack of disconnected systems?
[00:00:47] Tom Baldwin: Well, that's what we're gonna dig in today, data-driven marketing in Big Law as it actually exists, the real CRM landscape across the Am Law 200, headless CRM, and how a marketing leader earns a seat at the table. My guest is the right person to unpack all of this. Jason Kennedy is the founder and CEO of Choate Frazier, which helps professional service marketing leaders scale their operations, technology, and analytics.
[00:01:11] Tom Baldwin: Jason's been a CMO, a marketing ops director at Pillsbury, a consultant, and most recently, part of the data strategy team at Holland & Knight, a marketer who can actually talk to IT, KM, and innovation in their own language. Jason, welcome to Overruled by Data
[00:01:26] Jason Kennedy: Tom, thanks for having me. I'm super excited to get into this
[00:01:29] Tom Baldwin: Yeah, this has been a topic that we, you and I have been talking about for a long time. So before we get into the meat of it, uh, you know, for our listeners, we always like to give, folks an opportunity to kind of share their background, what shapes a lot of the conversation we're gonna have. So in your kind of intersection here, your path runs from CMO to marketing ops to consultant, firmwide data strategy at Holland & Knight.
[00:01:49] Tom Baldwin: What's the through line that connects all those chapters?
[00:01:51] Jason Kennedy: Yeah, good question. And you know, the, the CMO role was in accounting, so I was next door, uh, from a professional services perspective, but the, the struggle was the same. It was the struggle to show value. defining ROI in professional services, where often your service delivery team is also your sales force, that is difficult in the best of times.
[00:02:12] Jason Kennedy: Uh, and when it's not the best of times, there are a variety of pitfalls and open manhole covers that you can step in when it comes to trying to attribute the money you're spending and the effort you're putting in to go to market to actually what's coming in the door from a revenue perspective
[00:02:28] Tom Baldwin: Open manhole covers, that's the right way to describe the things you want to avoid, and hopefully today we'll, we'll cover some of that. You've described your recent focus, with your new business as the intersection of marketing and data. When did that intersection stop being a side interest and become sort of the whole point for you going forward?
[00:02:46] Jason Kennedy: So when I was at my first law firm, I, I went back to school to get my MBA, and one of the first classes I took was Stats for Business. I took statistics in undergrad, but it was
[00:02:56] Jason Kennedy: theoretical, like just formulas, learn to do this, and I, you know, it was awful. I hated it. Uh, I did Stats for Business in grad school, and it opened my third eye to what data can do for a business professional.
[00:03:10] Jason Kennedy: And already being in a law firm, coming from an accounting firm, I just, it, it felt like the gold rush in my mind. I was like, "There is so much opportunity here that, that my own team, my peers, uh, my, my previous firm, we were not unlocking with what's possible here." Now, I got out over my skis pretty early on 'cause I, I wanted to boil the ocean.
[00:03:30] Jason Kennedy: It was like statistical analysis and, and what levers pull to bring in what dollars. We're not quite there even today, seven-ish years later, but it put me on the path to figure out, okay, how can we do this in a way that it actually is effective, to go back to the ROI discussion, that shows what the customer's telling us they want.
[00:03:50] Jason Kennedy: All those fun things you can do with data. It was kind of the Wild West back pre-pandemic, and we're seeing some maturation over time, but that's really what got me started, was just the kind of Wild West view that was in front of me in professional services with marketing data
[00:04:03] Tom Baldwin: So fast-forward, you're at Hollin at night, you are the liaison between data strategy and marketing. Um, working in those two, kind of that intersection, what did that role teach you about how those two worlds talk together or fail to talk?
[00:04:15] Jason Kennedy: You know, that role gave me like a crash course in data architecture, which is one of the reasons why I jumped in with that team. you may know H&K is one of the best teams doing this stuff right now. I put them up against anybody, and I got to ride shotgun along a lot of the early work the data strategy team was doing, including, you know, Lakehouse and embedding AI tools, all the cutting edge stuff.
[00:04:36] Jason Kennedy: all the colleagues around me were working on those things, and it was fascinating. And one thing I realized pretty quickly is I spent several years just trying to learn more about data, and I found out I still knew next to nothing compared to my colleagues in data strategy. And so for somebody who thought he knew something and realized that, like, what's that gap look like for your typical legal marketer that hasn't been nerding out on data, you know, for the last nearly 10 years?
[00:05:04] Jason Kennedy: So, you know, credit to Glenn and Emily over there at H&K. They built a team with true cross-functional backgrounds, IT, CI, CAM, and marketing for myself and a couple other peers, uh, that already knew the lingo of those various departments, uh, that they were working with and, and could act as those natural translators as these larger initiatives got underway and these various departments got pulled in.
[00:05:26] Jason Kennedy: I think that was a critical piece into making sure this was go as smoothly as it could for something of this size
[00:05:32] Tom Baldwin: We, we always tell, uh, folks we talk to, if you want to see what good looks like for a do-it-yourself, uh, lake house deployment, there is no better example than H&K. Spot on. Spot on. So you mentioned a gap earlier. What made you decide to start Choate Frazier rather than stay in-house? What gaps were you seeing across firms that convinced you this advisory capability was, was a necessary thing?
[00:05:58] Jason Kennedy: Well, first and foremost, I, I love solving problems. Um, you know, starting Show Fraser allowed me to get back into s- solving problems, you know, marketing tech, ops data, with my peers, former customers, people I've worked with. you know, it's a, it's a pretty tight ecosystem as you know. So, we, we know the problems.
[00:06:15] Jason Kennedy: Everybody wears them on their sleeve. It's, it's a, a rare benefit for this industry. Like, we're all pretty open about the problems we're trying to solve, our successes. jumping back then, especially at a time where there's been a lot of consolidation on the service provider front, I mean, you and I both have experienced that firsthand.
[00:06:32] Jason Kennedy: I think there's, uh, you know, there's an opportunity for new, fresh perspectives to come in from the consulting side of things. So that's, where, Show Fraser was born
[00:06:39] Tom Baldwin: Amazing. so let's dive into some of the, things that some of our viewers, particularly folks that are gonna watch this coming from the marketing and BD background that are just now still learning what a lakehouse is. Let's do a quick version for the audience. In plain English, what is a data lakehouse architecture, and why is the legal industry suddenly talking about it?
[00:07:00] Jason Kennedy: here's my working definition, and I want you to pick it apart as the pro. But, quickly, as I tell people, it's a repository holding one governed copy of the truth piped to every system across the firm. And why? It's evident in what I just described. If the one governed copy of the truth, that should get antennas up for every, every chief in the firm.
[00:07:22] Jason Kennedy: They should know immediately the benefits of that compared to the problems they're dealing with today.
[00:07:25] Tom Baldwin: 100%. I love that. It's a super easy way to explain it. I find that you have to use analogies sometimes to, peel back the onion a layer or two. Kelly Whitnall gave, gave me a great example and that I've, I've run with a little bit. she said, "Having a lakehouse is sort of like having, Instacart deliver all the groceries for a meal to your front door.
[00:07:47] Tom Baldwin: Sure, you could go to all these different grocery stores and buy everything, but that takes time, and maybe you'll get it wrong." I just wanna co- I still have to cook the meal. I still have to prepare. I have to have the recipe, but I don't wanna go to get all the ingredients. It's time-consuming. I love that example because that's, that's a, a thing that marketers deal with.
[00:08:04] Tom Baldwin: They're having to go to HR for certain data, finance to some data. If CI doesn't live in their world, they've got to go to CI and the library and something as simple as, you know, a 360-degree view of a client, that report can take way too long. The plain language definition you gave is perfect.
[00:08:20] Tom Baldwin: sometimes, uh, folks will interchange a lakehouse and a warehouse. you know, people will say Fabric when they really just mean Power BI or whatever just lives in an Excel spreadsheet or a SharePoint list. Uh, how do you explain the differences?
[00:08:34] Jason Kennedy: Yeah. It's, I mean, analogies is the way to go, for sure because you can't, for a lot of marketers getting into the architecture stuff you know, you could get some glazed-over eyes pretty quickly. a question for you because you helped me with this once, but the difference between lakehouse and data lake.
[00:08:52] Jason Kennedy: I get that question from marketers at times, when you get that kind of question like, "Hey, I've heard about data lake. It's up on billboards and stuff like that." You're saying lakehouse. What, what's the difference?
[00:09:02] Tom Baldwin: It, it is such a funny thing because the, two terms sound almost identical. and then I'll, I'll give you the clinical definition 'cause at some point you, you have to just say it like it is and not, sugarcoat it. So lakes, data lakes were invented almost 15, 16 years ago, and it was the first sort of way to handle big data, that we were seeing back then for streaming and unstructured data and a good way to apply AI and ML.
[00:09:31] Tom Baldwin: but what the lake could not do that you could do in a warehouse, which is the house part of the-- what we're about to talk about. Warehouses are great for reporting and structured data. Lakes are great for unstructured data, streaming, AI, and ML, but they couldn't do the reporting part. So that sort of governed one source of truth that can power everything you couldn't really do effectively from a lake.
[00:09:53] Tom Baldwin: So the lake house takes the best of a warehouse for reporting and the best of a lake for streaming and AI and ingestion. So you get the data capabilities of a lake and the reporting capabilities of a warehouse. It's like the two had a baby and out came a data lake house
[00:10:09] Jason Kennedy: Nice. Okay. That's helpful. We need to clip that.
[00:10:13] Tom Baldwin: I get that question a lot. I don't know if it's the most, the single highest question I get like on this topic, but it's up there
[00:10:19] Tom Baldwin: Okay, so we've just covered the differences between, a lake, data lake, and a lakehouse. Why does all this matter to a marketer specifically, as opposed to someone in finance or IT?
[00:10:31] Jason Kennedy: I think the biggest thing, and, marketers may not realize it, is, the typical hub for where marketing data comes in and out is the CRM usually, right? Activities, mailing lists, maybe you're tracking opportunities in there, maybe even using it doubling as an experience database, you know, whatever that looks like.
[00:10:51] Jason Kennedy: But the i- issue with traditional CRM is it's, it effectively becomes a second copy of the truth, right? Which becomes, a bear to manage over the long haul. Like, you start to separate from what the actual source of truth is, right? Having something like Lakehouse where you've got something governing the golden record as you guys talk about, those sorts of things, that even though it's not, like, super sexy and it's, it's something that's gonna jump out right immediately at your typical marketer, being able to not have to deal with a lot of data quality questions or, duplicate contact issues or mailing list malfeasance, all these typical, like, thorn, the various thorns in marketer sides, a lot of it can be, remediated or outright, abolished with using something like this kind of architecture instead of what they've been doing in the past.
[00:11:42] Tom Baldwin: in our experience, I find that once the marketing and BD team kind of understands the power of the lake house, they become the biggest consumer of it. Because if you think about the kinds of queries that folks in those roles get, they don't actually own most of the data they need access to. They don't own the finance data, so they don't have kind of unfettered access to it.
[00:12:02] Tom Baldwin: They don't own HR data. they may or may not own CI. they certainly likely don't own the library or any kind of news research. So they're always having to grab data. They're kind of waiting for everybody else, to give them information. And from our standpoint, the real unlock for folks in these roles is that all that data that you are running around grabbing from other people is now readily available to you.
[00:12:25] Tom Baldwin: It's governed. You know it's the right data. It's all been blessed by the right people. Uh, you can trust it, and that sort of frantic, "Oh my gosh, this partner's going on a pitch and forgot to tell me,"and I need to pull everything together, and I don't have enough time."
[00:12:38] Tom Baldwin: All those fire drills that are never gonna go away become more manageable. and so we, we find marketing and BD folks are oftentimes the biggest consumers once the thing gets up and running. Switching gears here to kind of thinking about the state of being data-driven, what that looks like inside Big Law.
[00:12:54] Tom Baldwin: What does being data-driven in marketing actually look like at a firm right now versus what gets talked about, at conference panels with talking heads?
[00:13:03] Jason Kennedy: Yeah, in many cases, it's a night fight to get the data that you need to solve just ad hoc business problems, the stuff you just talked about, right? Like, they, they would love to be a consumer, right? Because marketing's job is to read the tea leaves and make a recommendation, right? Like, what am I seeing in front of me?
[00:13:19] Jason Kennedy: What is the, what is, our captured market, our client base telling us? What is the addressable market writ large telling us? What do we have resource-wise? Let's mash all that together and we've got a plan, right? with professional service marketing, it was the same in accounting, getting access to a lot of key pieces of data, typically financial data, pricing data, that can be quite a chore.
[00:13:39] Jason Kennedy: I've got my own hilarious stories of hitting brick walls trying to get that stuff in the past. And, you know, try to find the objectives, no matter where they are or how small, you end up finding, digging for objectives where you know you have the data so that you can actually report on its effectiveness.
[00:13:57] Jason Kennedy: That's not a great way, feeling around in the dark, to show how marketing is contributing to the bottom line of the firm. But sometimes that's how you, we see these firms or these teams working is saying, "Okay, I know that we've got access to the data to answer XYZ, so this is, we'll talk about this.
[00:14:14] Jason Kennedy: This'll be on the front page of our annual marketing report. This'll be where we commit resources. We'll carve out, you know, we'll bring in a, a third-party vendor platform to help us support this." And it becomes looking for whatever fire to put out instead of being truly strategic about how we should committing resources across the firm.
[00:14:31] Tom Baldwin: So next question on kind of data-driven state in, in marketing. You've mentioned several times Power BI sort of proliferating. It's everywhere. but there's still a ton of spreadsheets lurking underneath. There's a ton of out-of-the-box reporting from individual siloed systems. Why do you think that's persisted?
[00:14:50] Tom Baldwin: And also talk about why that's so inefficient.
[00:14:54] Jason Kennedy: Well, I, I think it's persistent 'cause it's easy to get going. Uh, most firms we work with, not all of them, have some level of Power BI access. Power BI Desktop's free to download if you can actually get it, you know, get it on your machine, get IT blessing, um, and you're off and running. And the quickest way to get a dashboard off the ground is with one, two, three spreadsheets acting as your database tables underneath.
[00:15:17] Jason Kennedy: So to go back to, you know, what we were just talking about, if you can't get access to a live feed or a particular view or whatever the case may be, you s- if you can get an export, you know, a stag export of that data or maybe some related data that you can maybe draw some inferences from but can't actually, you know, point to directly, that's what you're gonna do.
[00:15:39] Jason Kennedy: Power BI gives you that ability because you have that access right there in front of you. It's wildly inefficient, but it's an ability to say we've got something versus fighting the usual, you know, political battles to get access to the data that you actually need to report on effectiveness or, or ineffectiveness
[00:15:58] Tom Baldwin: So speaking of political battles, is the problem mostly technology process or is it people and incentives? And when I say people and incentives, politics plays a big part in that
[00:16:10] Jason Kennedy: Yeah, I think inertia is, is part of that.
[00:16:12] Jason Kennedy: for a l- you know, random CMO to go to their COO and say, " Hey, you know what? I could really use our, our pricing data, our leverage, leverage data, even just block billings across top X clients. I need that on the aggregate to do some real analysis."
[00:16:29] Jason Kennedy: Some firms just say, "Okay, well, let's get going." Some firms, you got to have 100 meetings to get there. Some firms, the door shuts immediately in your face. I think that is the core of what we see. I mean, the technology's there. These are very advanced firms from a technology standpoint. They have access to the tools, and you know, as we can see with our, our more innovative firms, they can hire in the resources to manage and, build these tools within.
[00:16:52] Jason Kennedy: So I, I don't think it's necessarily a people issue, definitely not a, a technology issue writ large yet. I think it is just that kind of legacy inertia that's like, "This is mine. I will handle it. I'll run my reports. You go run your reports and leave me alone."
[00:17:07] Tom Baldwin: when you think about, the gaps between what marketing leaders think their data can do and what it actually can do today, where do you think those exist?
[00:17:15] Jason Kennedy: know, I, I think the big thing, and we've talked a little bit about this already, is, uh, the, the ROI bit. So a lot, every marketing leader I've ever talked to is dying to get to ROI. And for some of them, they're trying to be proactive 'cause they know the conversation's coming up. Some marketing leaders, the, the heat's already on, and leadership is trying to understand, what is it that you guys are doing over there in your marketing department to...
[00:17:40] Jason Kennedy: And so if you don't have a full picture of what your, what your marketing efforts are doing, this goes all the way back to the beginning, right? Like, your sales staff is also your service delivery folks. that gap right there between marketing and that model is where the ROI discussion breaks down traditionally.
[00:17:59] Jason Kennedy: I think with the advent of lakehouse architecture and being able to democratize a lot of this data with, with the right governance in place, you can close that gap enough that you can actually start to say, if not direct, pull this lever, we bring this many dollars in, at least you've got a general sense of, these are the kind of activities we're doing to generate interest, which ultimately generates revenue, right?
[00:18:21] Jason Kennedy: And for a typical legal marketing team, that's light years ahead of where they are today. So I think that's the dream state, is to get to the point where we can say, "Hey, look, these are the kinds of initiatives we need to be focusing on, where we need to be spending our money, because we know we have a greater chance of success over here on the other side, even if we don't have direct attribution yet."
[00:18:42] Tom Baldwin: whenever I talk to firms, I often reference Heidi Gardner's work on data-driven cross-selling. and it's one of the best examples of, you know, if you have all this data available, um, the things you can do from Like, let, let me help you be more surgical on your go-to-market.
[00:18:59] Tom Baldwin: LikeI was at a firm, I won't say which one, that we were convinced we could get more litigation work from JPMC 'cause they were our biggest client, but they used us for one practice area. So the naive part of leadership thought, "Well, we have fif- just call it 15 practice areas. We're only doing one or two things, so that's a white space of 13.
[00:19:16] Tom Baldwin: There's a lot of data that would suggest that maybe there's a reason why they only use you for one or two things, and you going down that path with them is gonna yield, you know, a bad outcome.
[00:19:25] Tom Baldwin: Or I've got Coke and Pepsi that on the surface they look like they're the same. They're both $10 they're both the same level of profitability, let's say. But you dig down a layer and like Coke has more relationships.
[00:19:36] Tom Baldwin: They attend more of our webinars. They come and meet with us in the office more often. We send and receive more email. We have more phone calls. We have more engagement. They pay their bills faster. as opposed to Pepsi that is, you know, when you dig down a layer, all of a sudden you're like, "Well, yeah, I mean, if, if I have limited opportunities to take swings at cross-selling, I'm gonna put more in Coke 'cause underneath it shows us there's a greater chance, right?
[00:20:00] Tom Baldwin: Statistically of getting that work." how would, how do you see Heidi's work, you know, or, or firms should lean into Heidi's work, as they think about being more data-driven, showing ROI, showing value?
[00:20:11] Jason Kennedy: Yeah. Heidi talks a lot about that in Smart Collaboration, great book. And I think it's marketing has to be part of that discussion, has to be in the circle discussing that. And in today's world, so much of marketing's time is spent fighting inefficient battles, right? Like defending data quality or actually doing the admin work on keeping data right, um, stewardship, those sorts of things.
[00:20:36] Jason Kennedy: It doesn't leave enough time for them to be strategic and to be re- proactive enough to reach across and say, "Hey, let me sit down with you and let's, let's talk about this. I have this data, we have the relationship data, we have the partnership data, we have engagement data, we have..." You know, even if you want to pull in like, "Hey, we've got, rolling 12, rolling 36 financials.
[00:20:56] Jason Kennedy: We can pull th- this together and paint a really complete picture of what's going on with Coke and with Pepsi. And not only am I gonna tell you why Coke's a good idea, I'm also gonna tell you why Pepsi's a bad idea." Lawyers are very thirsty for that, even at the top ranks. But a lot of teams are locked up right now in just solving the day-to-day data problems.
[00:21:16] Jason Kennedy: And if we can streamline a lot of that from an architecture standpoint, and if we can automate a lot of that stuff away, then you've got some resources starting to open up for the marketing side where they can be proactive to say, "I see this is going on. We've seen some data, again, here in the tea leaves that we think is valuable.
[00:21:34] Jason Kennedy: Let's sit down and talk about it." It's real hard to do that today
[00:21:37] Tom Baldwin: Yeah. And it's even harder if the right partner is pushing on you going to market with Pepsi, it's hard to say no because you don't have data to support a reason why. Like that last comment you made, I'm gonna tell you why Pepsi's not a good use of time. It's very difficult, to have that conversation with an influential partner when they're really pressing hard at...
[00:21:56] Tom Baldwin: Because they went to law school with the GC at Pepsi or something, right?
[00:21:59] Jason Kennedy: and you can say, you know, the, the things I've counseled folks in previous engagements is sometimes you still lose that battle. Sometimes the partner wins and you have to go put that RFP together. But if you've got as much data as you're, you know you could put together to say, "Hey, this isn't the right path," you've staked your claim, you've made the case, you've got the data to back it up in case you're pressed.
[00:22:21] Jason Kennedy: And if the answer's still, "Don't care, go do it," then that's what you got to do sometimes. But that's a lot easier than being like, I don't think we should. I don't have anything to back it up, but I don't think that's the right play." That doesn't get you anywhere
[00:22:33] Tom Baldwin: Yeah. We just, um, a-announced a connector for Lead Forensics, and someone asked me what the use cases were for that firm to use Lead Forensics. And one of the use cases they talked about was starting to get much better telemetry on, okay, the minute we submit an RFP, what's the traffic from that prospect or client like?
[00:22:53] Tom Baldwin: And over time, can we start to see trends where we know we're gonna win this RFP because statistically speaking, when a company visits the site a certain amount of time after the RFP is submitted, or they stay a certain length of time, or they make so many return visits, the indicators tell us that that amount of engagement likely is gonna lead to a yes versus a no.
[00:23:13] Tom Baldwin: Like, that's a pretty interesting, very nuanced use case for website traffic data. I just thought that was fascinating that they went down to that level of depth.
[00:23:22] Jason Kennedy: there's a, there's a, a lead forensics competitor. I, I sat in on a call with them not long ago, and they pitched a, a, another very unique case where they said, "Hey, you know what? For, you know, give us, you know, a list of like 25 of your peer firms and we'll log their IP addresses, and we'll start monitoring when those IPs hit your partner's pages on the website."
[00:23:44] Jason Kennedy: And so after time, you know, we have to see this happen, that turn out. We can start building, like a, a lateral move kind of red flag, like with a certain level of statistical confidence. If an IP address from one firm hits X bio X to Y number of times, then you got a greater than 80% chance that they're on the way out.
[00:24:02] Jason Kennedy: this is a great example where having a lakehouse enriches that data tenfold. So we actually have another use case like that, and we fold in trending. Jason Kennedy has these kind of, general month-to-month trends. He bills this time this fast. He opens this many matters.
[00:24:18] Tom Baldwin: He comes into the office this often. He sends and receives this many emails. He adds this many documents to the document management system. If you see a drop in all those coupled with website traffic, all of a sudden it's a very strong indicator of something similar to like a client flight risk. "Oh, this client's not paying their bills as fast.
[00:24:38] Tom Baldwin: They're not opening as many matters. They're not attending our webinars as often. They're not coming to our website as often." a great example of where a lakehouse really takes a single data point and then supplements it with 10 more to just bolster whatever use case you have.
[00:24:52] Tom Baldwin: You've got all that extra context to support a position
[00:24:57] Jason Kennedy: And as a BD director, like instead of having to get that frantic email on Friday afternoon where it's like, big partner has just announced they're leaving, we need to figure out what's going out the door with them, who can we call? And they spend their whole weekend putting together the playbook. Like with some, some indicators ahead of time, you can say, "I've got the playbook ready.
[00:25:16] Jason Kennedy: I've got some pieces in play. If this is real, I've got the pieces already in place so I can s- we can start moving kind of in, in motion and not doing a cold start when I get that frantic email from the practice leader."
[00:25:28] Tom Baldwin: So we've talked about some interesting use cases. Let's, let's kind of dig into that a bit more. What's the CRM landscape look like across the AM Law 200? You know, you've pulled together some CRM distribution data on roughly 136 firms in the AM Law 200. Without giving away, uh, the crown jewels, what does that landscape look like at a high level right now?
[00:25:49] Jason Kennedy: Yeah. So that's, that's about two-thirds of, of the Am Law 200. I mean, of those interactions, still roughly half the market. I mean, 30 years in, still the incumbent. DealCloud's claimed about another fifth of that group. you know, the enterprise horizontal Salesforce, which has, I think, at least two, um, legal specific players now in ClientLogic and ClientVerse, are out there in the place, as well as Salesforce out of the box.
[00:26:16] Jason Kennedy: Dynamics, the same thing. You got Legal 360, you got Peppermint, you got Dynamics out of the box. So, uh, we kind of bunched all those together just to make it easier to read at a, at a high level. so between those two, that's another fifth. Um, then you got your purpose-built newcomers, um, Nexl, Tree. they're small, but they're real, uh, lower small digits, distribution.
[00:26:37] Jason Kennedy: I think, for Nexl there's a couple HubSpot players. There's even, I know of a couple of firms in Am Law 200 that distinctly have no CRM. I think what, you know, the middle 100, I think 50 to 150, you see a lot of those non-traditional players. Up in the top 50, it's your heavy hitters, right? It's, it's Interaction, it's DealCloud, and probably Salesforce, not as much on the dynamic side.
[00:26:58] Jason Kennedy: it's changed a lot. I think I evaluated CRM in 2018, and it was kind of Salesforce, DealCloud or, Interaction. the fact that there's a lot more options out today, I think is a boon, for law firms. And, and there's always another one around the corner. So I mean, ask me again tomorrow, there may be something else to talk about
[00:27:15] Tom Baldwin: And as you look at firms as they go through that evaluation cycle, how are they actually sorting themselves across those different options?
[00:27:21] Jason Kennedy: Oh, that's a good question. So, you know, sometimes it's based on vibes. What am I hearing? I've talked to my peers. I'm in my, you know, CMO roundtables. They're using this. I mean, a, a lot of firms like to, do peer modeling, like, "Hey, here's, here's our similar kind of profile firm." And that could be based on size, could be revenue, could be some other, type of criteria, maybe leading practice areas or something along those lines.
[00:27:49] Jason Kennedy: And they s- they, again, this is a very collegial business and everybody is, is happy to trade ideas, and that happens a lot, particularly on the CRM front. So you hear trends, like for example, for those like middle 200-- middle 100 that we talked about, a lot of folks are asking about Nexl, a lot of folks are asking about HubSpot.
[00:28:06] Jason Kennedy: at the higher level, there's, you know, the, typical questions like, "Should we keep interaction? Go to cloud only? Um, what's going on with DealCloud?" And then the, the ever-present, "We should really look at Salesforce," which is, uh, as loaded a question as it gets. it's a lot of, uh, looking over neighbors' fences, and I, I don't say that disparagingly.
[00:28:26] Jason Kennedy: It's, it's good to know that de-risks this a little bit, knowing that like you've got similar firms that do the same kind of work that seem to have found some success with a particular platform. that's a, that's a great anchor when you're a CMO trying to pitch, you know, a six-figure change to your, your marketing committee
[00:28:41] Tom Baldwin: It, it's interesting because these are big projects, whether you upgrade or, or migrate to a new platform, um, they're very expensive. And you mentioned ROI earlier, and there's lots of narrative around the ROI for CRM and how it's adopted. and then a couple firms that I've recently spoken with are looking at the option of going headless.
[00:29:05] Tom Baldwin: So maybe you could talk to our listeners about that a little bit
[00:29:09] Jason Kennedy: Well, why, like, if you could, I wanna, like, from your perspective, 'cause you told me about headless CRM. define for us what, what headless CRM is from your perspective
[00:29:18] Tom Baldwin: Um, head-headless systems are not, you know, again, not, not a s-shock here. They're not new, but they're new to legal. the basic premise of a headless system is I have a very rich content layer, and I write my own UI on top of it, and that layer can be swapped out, as needed, but the content and the data are the, the goal that kind of stays persistent.
[00:29:40] Tom Baldwin: Going back to our food analogy, it's sort of like saying, "I'm gonna keep the kitchen the same, but maybe I switch from, um, a fine dining experience to a, you know, a, different type of experience." And so the front of the house I can swap out pretty easily, but the, guts of the, uh, the restaurant, the kitchen stays the same.
[00:29:58] Tom Baldwin: we see that a lot in content management systems. There's many headless examples of that. and again, outside of legal, this is, this is not a new thing. but the idea being that, hey, if I subscribe to the notion that most of the content I get is not gonna be, uh, entered by users, and I'm getting it through, for CRM, I'm getting it through an ERM platform and some other, data from other systems.
[00:30:20] Tom Baldwin: if I have that data in one place, what am I using the front end for? It's reporting. Well, do I wanna be bound by the reports that come from X system, or do I wanna have more flexibility and write reports however I want? chat with your data is becoming a, a bigger and bigger thing. Do I wanna be bound by the AI native to an individual system, and then all of a sudden I have to have an AI tool for my experience system, my CRM system, all these other things?
[00:30:46] Tom Baldwin: Or do I have my data in one place and I throw AI on top of it? those are the conversations in, again, it's early, early days. Um, I don't know any firms that have done it yet, but firms are thinking about it, uh, and talking about it, which is, I think, pretty cool.
[00:31:01] Jason Kennedy: is this something that would, could be built on top of Lakehouse architecture or does it become like, the Integrata platform, does it become part of the platform itself?
[00:31:09] Tom Baldwin: We would not build it. Firms would, would build that on their own using any number of different mechanisms to do that. That's kind of the, the cool thing about it is, anything from like a Replit front end, you know, if you want to vibe code something, to if you have like hardcore developers, you want to write it in .NET or Svelte or something.
[00:31:26] Tom Baldwin: You know, if your, if your firm's big enough to have that capability, you can build your own front ends. And more and more, what's happening in the ecosystem, writing front ends now is not that big a deal anymore. Uh, and, and that's the part actually, when you talk about vibe coding and things of that sort, the front-end part is a little bit easier to scale than the back-end data layer.
[00:31:46] Tom Baldwin: That's where all the orchestration, security, governance, control, that's the thing that needs to be kind of enterprise grade. The front-end stuff, as long as you've got single sign-on, some other things, I'm not saying that's easy, but it's a lot le-less of a barrier to entry to have an enterprise or close to enterprise-wide front end than back end.
[00:32:03] Tom Baldwin: Where does a firm's CRM choice make the lake house easier, and where does it make it harder?
[00:32:10] Jason Kennedy: Oh man, I fr- from, uh, putting my marketing user hat on and what I know of lakehouse architecture, I don't see any big issues between one or the other. I mean, every one of these tools I know have some layer of, of API access at, at a bare minimum. But from your perspective on, on the architecture side, is there anything that you guys in your experience that you've seen that says, "Ah, that's a gotcha that we weren't expecting from the CRM side"?
[00:32:39] Tom Baldwin: Yeah. you know, we've got connectors to Interaction, Salesforce, Nexl, Dynamics, Foundation, you know, a lot of tools that firms use. And the API, I think just for firms that are thinking about this, you, you have to be cautious about extracting data via an API. Um, A, because there's throttling and rate limiting that happen that you don't think about.
[00:33:03] Tom Baldwin: your team is used to grabbing data from a SQL database, it's sort of everybody understands and knows SQL. You can get at it. It's on-prem. Usually, uh, the entire schema's kind of available to you to look at and pull what you want out. With an API, you can have it chunked out over a bunch of different endpoints and, you know, you have to have an endpoint for this, an endpoint for that.
[00:33:23] Tom Baldwin: The data you really want lives across a number of different endpoints. Um, some vendors today are actually kind of limiting the amount of data you can pull out of it. So the other big, uh, downside unfortunately with a lot of these new cloud products is you're not getting as much width of data from the API that you could from the legacy SQL system.
[00:33:43] Tom Baldwin: And so firms are sort of thinking, "My gosh, I'm paying all this money to move to the cloud with the intent of having more data available, and I'm actually getting less." I would say those are just considerations. Not to say you shouldn't go to the cloud, but you have to be kind of thoughtful and, do some research on, on the product you're buying and, and, and really dig into those questions around API and content access.
[00:34:04] Jason Kennedy: And we haven't even, I mean, it's not the core to the, what we're talking about today, but we haven't even really gotten into the AI bit of this, right? Like, so how do we use AI to answer business questions? Which if, you know, if you're asking financials and timecards and those sorts of things, like a lot of firms are throwing, you know, a few bucks here and there, right, at, at th- those kind of initiatives.
[00:34:26] Jason Kennedy: but all of a sudden when you've got to tie in stuff for like that last minute taxi report you brought up earlier, right? Or if you've got to throw some, some engagement data for an event that just popped off, marketing teams, most of them anyway, are not set up now where they just like, "Oh, that's something that we can run through Herbie or even a Copilot or something like that."
[00:34:43] Jason Kennedy: 'Cause we're not in there yet. We'll be the last ones in
[00:34:45] Tom Baldwin: you know, we could spend two hours on AI, on lots of different topics. But the, the one thing I would say that, along with data sprawl that we kind of let run rampant in legal and all these data silos, you're quickly gonna see AI sprawl. you know, I was talking with, a CIO at a firm, and he was looking at a cost management solution.
[00:35:05] Tom Baldwin: I won't name what one it was. But just the AI add-on was, like, 70 grand a year. And he said, "You know, the problem with that is I've got 40 or 50 apps that kind of fall in the same size and scale as that cost management solution. And now I have to license each one 'cause someone's gonna want it. Uh, it's expensive when you aggregate it out.
[00:35:25] Tom Baldwin: I don't have any visibility over, like, who can access what in each system." in the MarTech stack, you've got half a dozen, at minimum, big iron systems that are gonna have their own AI tools. So all of a sudden you take 30 to 60 grand per, application. And now I've got several hundred thousand dollars in siloed AI tools that don't talk to one another.
[00:35:45] Tom Baldwin: So Jason, to your question, like, most of the AI answers I want require data from more than one place. and it definitely, I, I'll just say very selfishly, it's one of the big, you know, kind of drivers that firms are looking at putting a lakehouse in place to solve for, is I, I don't want a siloed-- I already know what siloed data feels like.
[00:36:03] Tom Baldwin: I don't want a siloed AI stack. And that's where the market's heading.
[00:36:07] Jason Kennedy: Uh, you know, I thinkwe're getting closer and closer. for my, marketing brethren, this kind of architecture, a lot of folks have heard me talk about it. I think we're getting to that point where we can start looking at how marketing is influencing outcomes.
[00:36:22] Jason Kennedy: Again, even if we're not direct attribution, which is the dream state, we're, we're getting to the point where we can sit and say, "Hey, marketing is actually making an impact in these areas." Even if it's not a dollar for dollar kind of evaluation, we can at least say, especially come budget time, which is coming up soon, " Here's where we think we need to make some solid investment in technology, in people, in third party," whatever that looks like, because we know it's gonna have an impact on what the firm's trying to achieve for next year and beyond
[00:36:52] Tom Baldwin: I always like to end these with some practical advice for our listeners. a theme we constantly kind of revisit on, on our show is a lot of firms freeze up or they feel like their data's not good enough because they don't have the perfect data or data strategy before they do anything.
[00:37:08] Tom Baldwin: For marketing leaders specifically, what would be your version of just get started?
[00:37:13] Jason Kennedy: Yeah. One of my favorite things to do, and I've had a lot of people ask me about this recently, is segment your data. Let's talk about like just in the CRM context, right? Like my first firm, we had a CRM with 450,000 contacts in it, and we were dealing with, data quality issues across all of them. We were trying to solve for relationship scores across all the contacts, and that was a, chore, and we could never stay out ahead of it.
[00:37:39] Jason Kennedy: But thinking about ways to cut up your contact data, like maybe just by job title or maybe it's by top X clients. Maybe you're doing regional expansion, so you wanna do base geogra- geographically. There's all sorts of different ways you can cut up your data to make it much more manageable, you know, for data quality issues, for migration issues, for reporting, for analysis, for strategic issues, whatever that looks like, finding ways to cut up your data, and you can do that with a spreadsheet export.
[00:38:06] Jason Kennedy: That's something you guys can do immediately when you're done listening to this wonderful podcast, is get in there, get an export, pick some sort of criteria, and start cutting it up
[00:38:14] Tom Baldwin: So great segue. You know, the concept of the lake house sounds great, and I'm sure, uh, lots of folks look at it and go, "That sounds awesome," but that's gonna take a long time. budget season's right, you know, coming up. This is really probably something I, I won't even be able to take a, peek at until next year.
[00:38:31] Tom Baldwin: What's one small high-impact thing they could do the next quarter to get better with their data?
[00:38:37] Jason Kennedy: it's a question I ask a lot in my engagements. Even in mid-year, it's okay to ask this question: what are you trying to achieve? what are your goals for the marketing department for this year? Not just, "Hey, better attorney service," and, you know, whatever.
[00:38:49] Jason Kennedy: Like, what are the things you're being held accountable to as a, as a CMO, as a marketing ops director, as a BD manager, whatever? And then find the data that helps you tell that story, right? And sometimes that story's not gonna be good. I think that's something just as important. It's great to celebrate the wins and point at all the cool stuff, but it's just as important in a leadership position to look and say, "Hey, we're trying to achieve this before the year's up.
[00:39:12] Jason Kennedy: The data tells us that we're not doing so hot in that area." And that's just as powerful as the good news 'cause it allows you to see, okay, where are we falling short? And we've still got half a year left. Where can we make some adjustments? So look for that North Star. A lot of teams kind of get, get lost in the details.
[00:39:30] Jason Kennedy: Find your North Star and then pull in the data that helps you tell the story about if you're headed in the right direction
[00:39:35] Tom Baldwin: Great segue to the last question. So how do you advise leaders to balance those tactical wins against longer-term platform plays, uh, so they don't end up with shelfware or a stalled initiative?
[00:39:46] Jason Kennedy: You know, it's, you have to evaluate and, and continuous improvement. I mean, that, that sounds like real cliché stuff, but you can't be anchored to any of your tools, any of your processes, even like, especially today, even your skill sets. So understanding like, okay, we've, we have this large initiative, CRM rollout, for instance.
[00:40:06] Jason Kennedy: We've got these milestones in place. We're hitting these. This is great. We're moving along, but at the firm at large, we've got some other stuff going on that's kind of changing the direction of what we wanna do strategically in marketing. So we need to sit, and it's okay to sit midstream and say, Are we building this CRM tool to meet what the firm at large needs at this point?
[00:40:26] Jason Kennedy: Do we need to stop and take a beat and reevaluate? Do we need to just change our implementation strategy? Do we need to change what data we're pulling in or what third-party data we need to bring in?" There's a million questions that can kind of come up with that, but having the discipline to say constant evaluation, continuous self-improvement, again, that sounds so cliché, but you boil that down to like an enterprise project rollout, that can save you a lot of grief in the long run
[00:40:49] Tom Baldwin: Jason, that's awesome. Thank you so much for sharing your perspective today. For our listeners, I hope this conversation reinforces a theme we come back to often on Overruled by Data. You don't need a perfect strategy to begin. Start building your data foundation now and let the strategy grow alongside it.
[00:41:07] Tom Baldwin: Make sure marketing has a seat at the table from day one. Well, that's it for this episode. If you enjoyed the conversation, hit that subscribe button so you never miss another one. Thanks for listening, and we'll see you next time on Overruled by Data