From Chaos to Clarity: How to Get Your Marketing Data in Order

Intro VO  00:06

Welcome to revenue rehab, your one stop destination for collective solutions to the biggest challenges faced by marketing leaders today. Now head on over to the couch, make yourself comfortable and get ready to change the way you approach revenue. Leading your recovery is modern marketer, author, speaker and Chief Operating Officer at Tegrita Brandi Starr

Brandi Starr  00:34

Hello, hello, hello and welcome to another episode of revenue rehab. I am your host, Brandi Starr and we have another amazing episode for you today. I am joined by Steve law chance Steve is an internationally recognized speaker for time entrepreneur and author of the book marketing for product lead growth, become a company leader through credibility and empathy. He teaches growth leaders to accelerate success by engaging their entire organization and customer base. Welcome to revenue rehab. Steve, your session begins now.

Steve LaChance  01:14

Thank you so much for having me. Looking forward to it.

Brandi Starr  01:16

I am excited to have you here. And there's so many things that we could talk about. But before we jump into that, I like to break the ice with a little Woosah moment that I called buzzword. banishment. So what buzzword would you like to get rid of forever?

Steve LaChance  01:36

Oh, man, I have several. Can I give you like two or three? Two? All right. Sure. The first one that I would love to banish forever is fractional. Enter favorite chief office suite, right, whether that's fractional CMO, fractional CRO, fractional, fractional, fractional, I just really don't like it. I remember trying to find a word to describe myself when I first left corporate America and was building my first business. And I was just so afraid of the word freelance or contractor or interim or pick another word. And lately we've settled on fractional, I understand that it's very useful in terms of, you know, trying to describe what you do without saying part time. Because that's kind of what it is. And people are afraid of that. But I just really wish we could stop being I'm not a fractional CMO. I'm just,

Brandi Starr  02:35

yeah, it's kind of like it is really part time because you still bring your whole self and your whole experience. So you're not just giving them a piece of you, it's just a smaller component of your time,

Steve LaChance  02:50

precisely I'm bringing, and whoever else is also maybe a self described fraction, or whatever they're bringing the breadth and depth of their experience, to very complicated problems, not components of ourselves, or just a piece of who we were at a previous job or who we are today. But rather, you know, you get the most focused 25 to 30 hours of my week, as opposed to 40 to 80. Depending on what you know, situation, your typical company culture works under, for better or worse.

Brandi Starr  03:21

Love it, I will not use the word fractional. Give me your second one.

Steve LaChance  03:25

The second one is, I don't know if it's a buzzword so much as a phrase. But anytime, like a meeting is going to end early. I feel like that's a moment to celebrate. But people tend to say things like, Oh, well, I'll give you some time back or, you know, I'll give you 10 minutes back. And we should all be like respectful to the fact that time is our own right. And it belongs to other people. But like we're not giving other people their time. We're just all able to reclaim a piece of our own day, like you, you know, I would never Jain myself to give you 10 minutes of your life back. That's just very rude. Like that one

Brandi Starr  04:11

I'm gonna have to work on because I can say I am bad about saying give you some time back. And I absolutely love memes. And it makes me think of that Maxine Waters meme where she's like, Reclaiming my time.

Steve LaChance  04:26

So yeah, and I know it comes from a place of like, it comes from a place of positivity, and like, oh, you know, it is a gift, we get it back kind of thing. But probably part of what led me initially leave a fortune 500 companies strategic marketing role and start my own business, as a lot of people are doing now was that I've just never personally liked the idea that anyone else would ever think that they owned my time or had the authority to give it back to me. But I'm happy to take it I suppose. So I promise you Should fractional or give time back work their way into any conversation. I won't get upset by any means. But if you were to ask me as you did, if I get rid of a couple there at the top of my list,

Brandi Starr  05:11

well, we're at least going to try to put those box those in the box for the next half hour or so. So now that we've gotten that off our chest, tell me what brings you to revenue rehab today,

Steve LaChance  05:24

oh, data. If you could give me another word advantage would be big data, but data specifically around its importance, its value. Its misapplication, its misuse, and the chaos or the sort of, you know, lack of purpose around a lot of the data spheres and the data lakes and the big data malls and the large language models that marketers are often forced to play and swim in utilize and leverage and how there is a path forward and how I think the when it comes to data, probably the biggest problem is actually using it for a purpose and having the right types, as opposed to say, finding data, for example. So taking that very chaotic statement, as I just mentioned, and trying to turn into something useful, which through your help we're actually going to do today, hopefully, that is

Brandi Starr  06:23

awesome. And I'm excited to talk about data. It's a big, big problem with big data. And you know, before we dive in, I believe in setting intentions, it gives us focus, it gives us purpose, and most important, it gives our audience and understanding of what they should expect from our discussion today. So for those heads of marketing, that are listening, what is your intent, or your best hope, for this discussion,

Steve LaChance  06:51

my best hope for this discussion is listeners are going to go through a bit of a transformation where today they are feeling perhaps overwhelmed, perhaps very frustrated, perhaps even defensive, when it comes to their role, its relationship with data, the purpose of data, and how you can use it to communicate proactive and positive changes within your organization, versus say, a more defensive and reactive role that people have with their relationships with data today. And so, you know, through that transformation, like to cover a number of items, right, like, you know, what's good data and bad data, the sources of it, the purposes of it, the type of it, its value over time. And, you know, ideally, I hope nobody's actively scribbling notes down right now. Because they can rewind, listen again, if they'd like. But you'll walk away from this or, you know, take the headset off and feel like you've got, you know, a number of steps, a number of concrete things you can go do with your team or on your own or with your leadership, tomorrow, the next day and the day after, to really, you know, bring the data that marketing interfaces with, into a world of again, positive and proactive change in your in your organization.

Brandi Starr  08:12

I love it. And I'm looking forward to diving in, you know, as a consultant, I talked to different organizations, and you know, they come to us with different challenges. And you know, some like you wrote the book on product, lead growth, some take a plg approach, some ABM, some are using verticals or personas, you know, everybody has their way that they want to go to market and how they're going to target their audience. And no matter what the strategy is, the thing that sits below it, in my opinion that enables that strategy to be successful is the data that you have to actually support it, you know, if it's plg strategy, and you have no idea how people are tapping into the product, or you know, engaging with a trial, then how do you convert if it's ABM, you know, all those sorts of things. And so it also feels like there is still this, like, the fact that data is a problem and is a mess is just kind of unknown. And it's like, yeah, you know, it's problem everywhere. And so I guess sort of like the start with like, why does it seem like nobody and I won't say nobody, because I know you've done it, but that most people just kind of accept that, like, the data is a mess. I try to work around it as best I can.

Steve LaChance  09:30

Yeah. I've got a number of suspicions around why that's the case. The first is every and everyone who's ever consulted or anyone who's ever had two different jobs in their life could tell their friends and colleagues. Every situation is unique, even if there are templates and even if there are things about problem A or company A to Company B that may rhyme with one another, every every organization's sort of running their own course here. And so what It means is, there's different data streams, right? There's different data repositories, there's different data maintenance policies. And then let's not even talk about data maintenance implementation as opposed to those policies. But I think the make the biggest problem is that in a perpetually accelerating economy, where we can interact with each other more faster from greater distances, at better speeds, the world around us is manufacturing data faster than we could ever process it. The entire idea behind machine learning, and it's some of its, you know, artificial intelligence or AI. outputs, are attempting to like be the facsimile of the human brain to process data. Because we're just we've we've swallowed the the notion that we're creating so much of it, surely no human being could ever keep up with it. And so if you're in marketing, you're typically in a position where you're just, you've got a number of fire hoses coming at you from everywhere. And one of the first solutions that people really started building out from time immemorial, at least marketing Memorial is been, like, how do we attribute activities? How do we create leading indicators? Or how do we devise those from what data we have? Like? How do we take things from the past, and use it to predict the future so that ultimately, we can attribute it to revenue? Right marketing is tasked with driving the revenue at the organization? Yes. Other things to brand elevation, thought leadership, if we could manage that one, that'd be cool, too. 

Brandi Starr  11:41

But ultimately, it's been banished a few times.

Steve LaChance  11:43

Excellent. I'm so pumped. We can get a whole team together for it. But yeah, I mean, it's, it's just a huge problem, where again, the data intake and creation is only accelerating. And so it's, it feels like one of those moments in life where, so you show up and you realize I'll never be able to solve this problem. So I'm just going to have to live with it. If I could use probably a not great metaphor or analogy, here's like, you can't change the oceans, tides. And everybody feels like they're in that. So you might as well learn to navigate and try not to drown in the meantime.

Brandi Starr  12:17

That's a great, great analogy. Because yeah, that is exactly what I see. And where people do try to tackle the chaos. And the output, even before you get to the ultimate end, is always so significant in terms of how the organization can operate from attribution and figuring out what's working and knowing where to deploy your resources, to even just being able to orchestrate better communications for your audience, using the data that is available. And so it's like even those incremental steps like before we get to the ultimate goal, it's like those incremental steps, just put you lightyears ahead. And this is why I think that this is such an important topic. And so I know that you have successfully done this, I'd like to, like hear what's been your experience? And then we can kind of dive into some of the steps and how people can do this as well.

Steve LaChance  13:25

Certainly, so the the, the case study that I have for you today is an organization that has entirely inbound right there, there are no outbound sales calls taking place. It's all you know, traditional marketing activities in the form of, you know, a digital and an online space content creation, out of doors, signage, you know, that kind of thing, where visitors anyone interested in coming to work with the organization or to purchase products and services comes through that website. Right. And so, in terms of a data collection standpoint, the net is actually exceptionally wide. You know, if you ever come to the website, we've got a cookie on you right? We're tracking you we're we're retargeting ads at you that kind of thing. So we're making a lot of decisions, because about how to communicate with you, the visitor the potential customer. Before you ever really raise your hand and say, I'm interested in taking the next step further in meeting with a, in this case designer or meeting with a representative of the company itself. And this process that I'm describing today takes place over the course of about seven years. However, the front end heavy lift in the first few months set success up for month, two successes, you know, q2, year two successes and sort of that timescale so we can talk you through really like, what did we do first? What did we do second, what did we stop doing that kind of deal but contextually just wanted everyone to be aware that I'm not speaking to a chaos to, to chaos to revenue path here that involves a lot of tracking down the right person in the right. Decision position on the right buying committee at the right exec enterprise level organization, this was very much an inbound strategy, if that helps in terms of context.

Brandi Starr  15:26

No, it does. And so, what was the catalyst that triggered you to actually start going down this path? The, you know, that final? Okay. You know, a lot of people just say it is what it is, it's like, it's not what it is like, it's gonna be something different. Like, how did you recognize that, like, that was the right time to start that journey?

Steve LaChance  15:49

Certainly. So it started with a series of conversations. And it's probably the most powerful thing that you as a human being can do today. And tomorrow, this afternoon, you know, don't pause the podcast, don't pause the show. But as soon as we're done, go do this kind of thing, where you just start having conversations within the organization. So the catalyst for me was, was having a conversation with some of the leadership, I think we're having lunch even actually. And I just asked a question and said, you know, Well, where did the deals come from? These are big ticket items, I should say, we're talking about half a million to $3 million purchases at any one time. So this is not, you know, typically, that's squarely in the realm of an ABM and an attack mode, and a very tailored approach. And so I started asking questions like, Where does where does the deal come from? How do we know that a client is going to be a perfect fit for us? How do we say no to clients? What do we know about their needs before we have the first meeting? And it wasn't that I was getting a lot of blank looks, I was just getting a lot of answers. That sounded like sort of the provenance of the data question, which is, well, this is the way we've been doing it. We started putting, you know, ads in this publication, and it had its own URL. So we were able to use you know that to get to the website. And then visits to the website. It was, I don't want to say unsophisticated, but it was some more entry level stuff in terms of just what's happening before people raise their hand and come in the door, and realizing that said, Okay, well, how does that where does that information live? How do we know who to talk to where do we look at in terms of a deal or a pipeline? Then I just watched everybody's face light up, suddenly, they seemed very excited to talk because they were like, Oh, we've got all that information. So oh, we have all that information? And you said, yeah, it lives in and I got a bunch of logins. And somebody gave me on like a guided tour of it. But the connection there, the couse was, they said, we have all that information, but nobody could give me a coherent answer. We know all of that information, but we can't actually answer that question. And I realized in that moment that there was either a lot of processes that we could improve, the answer was there and nobody had formed a story out of it. And man, if marketing's good at anything, it's telling stories out of information. You know, or there was something in the middle. I mean, again, the catalyst came down to at the three kind of key tiers I think of in an organization, you know, your executive level, your management, or your mid level and your practitioners. Nobody really had a great sense of or could explain coherently. Where deals were coming from, where the people who love us lived and what they were thinking about what they were doing, or quite frankly, why customers kept coming back. We I was what we have repeat customers on huge ticket items. Why do they love us? Why do they come back? Why aren't we telling that story? And everyone just kind of threw their hands up? I don't know. And so yeah, we that we tackled it from a stance of like, you see this obscurity, in a decision makers mindset, and you have an opportunity, you can charge into that gap. And you can help clear the fog so that it's easier for everybody on the team to have a better day. Or you can sit back and you can say, yep, that's just the cloudy part. We don't go over there. Sometimes deals pop out, and we're happy about it. So

Brandi Starr  19:19

no, so that that is a great catalyst. And I do think that the more you ask questions, the more you start to see the gaps in the data. And the more you really get a picture of how much better you as an organization can be doing. If you had answers to some of these questions, especially in marketing where, you know, we tend to carry a large share of the budget wallet. And so if you can't actually say what's working, you don't really know are we spending the money in a good way? Which you know, seems so simple.

Steve LaChance  19:59

You highlighted Another key problem, I think, which is in a rush to justify budget, in a rush to ascribe or ascertain, return on investment, we started measuring everything that could be measured. And because it could be measured, the global we the royal we we took that as important because we could manufacture that data that became an important number. Reviewing past episodes of certain MQL, or SQL or any other qualified lead is one of those terms that was potentially banished from somebody's mind at one point. And I have to say that those folks were probably on to something where, you know, when you're manufacturing bad data, or data that doesn't accurately inform a decision, you're you're making your problem worse for yourself. So I would say number one, is actually not go get the data that you need, or review all the data that you have, but you're probably very aware of some columns in some databases somewhere that are distracting more than anything, or they every time someone summarizes them in some way, you have to go in and give a bunch of contexts that nobody makes the wrong decision off of it, such as you know, X number of MQL is converting to SQL is went down. So well, how do we how do we solve that problem? How do we describe that variance or explain that variance away? I would argue that it doesn't matter to explain that variance away because ultimately MQL to SQL doesn't is not as important to the company as delighted customer who's expanding their revenue further down the pipe, or a new segment of visitors or a larger audience that suddenly finds our message and it resonates with them. We get into the data by its definition, as a single point, it lacks dimension, it's not even a line. It's just a point in space, that's infinitesimally small. And we go focusing on single data points all the time, to the detriment of our teams to the detriment of our health to achievement of our company. So step one, stop manufacturing bad data.

Brandi Starr  22:07

Okay. perfect segue into my next question, because I wasn't saying how do we get there? Because I think, you know, I definitely you know, is if I wasn't already motivated to figure out how to solve this problem before listening to you. I'm like, Yes, like, we gotta crack this nut. And, you know, I do think that that is a great starting point, I know that something we've been focused on recently is looking at more aggregate metrics, or how does data in conjunction, you know, different data points in conjunction with one another? What picture does that paint? So tell me where like, how did you you said it was a seven year process that, you know, was incremental and kind of iterative. Tell me about that, like, what did that look like?

Steve LaChance  22:55

Certainly. So there were probably five or six apps called Five, five kind of key elements to this. And these were, what? Sometimes they're called OODA Loops, which is a like a term from the 50s. observe, orient, decide act, your favorite entrepreneur who has ever written a boat, or a book, rather, has come up with their own version of that term, agile miles, you know, another fun one out there, where basically, what's going on? Like, what do you observe it data collection? Can we orient ourselves? Can we put that data in context? Can we make decisions off of that, and then act. So now we're doing OODA Loops about just the Ood, the blue part, the observer, the Orient depart, but there's probably five key steps in here that I would say one is, again, stop manufacturing bad data. Bad data comes in many different terms or forms. And we can talk about that in a minute. Second thing I would say, though, is stop maintaining useless data. And you can either do that by letting it wither on the vine, because people are terrified of turning processes off or deleting things, they always think, well, I can save it. So I'll use it later. There's this fever dream, that with enough data, like you know, the it'll just assemble itself into an insight and you'll be able to change the passion of the company, that's not going to happen. So stop investing in that. And so you can either let it wither, or you can actively deprecate, you can go out, you can delete things if you'd like. The third thing I would say is, begin creating better data, but doing it with a decision forward mindset. It's similar to the process of developing a feature and a software product, for example, you don't build that feature, unless there's a user story that goes along with it, the user acceptance test that goes along with it. So why am I making it? How will I know when it's done? How will I know when it's ready for testing and to move on? The same thing should be done for your data creation process? Don't go create a new metric, or go fix a metric that you think is broken unless you're 100% aware of how that new information and is going to be used to make a decision somewhere within your organization, it could be at a very granular level, or it could be a wide level right practitioner, the management level, the executive level, or even the board level, right? A lot of there's a huge filtration process that happens as data moves up, you lose a lot of context, things get aggravated, or I'm sorry, aggregated, not aggravated, aggregated even more. And but that comes from step four here, which is alignment, right, we need to align internally, talking marketing, sales, product, executive, administrative, all the different pillars that hold up the structure of your company, aligning around who's using what, for which decision. And I'm speaking in generalities here, but we can get into that specific in a minute. But the last thing I would say is a piece of alignment that you can never forget. So it deserves its own space, is ensure that certainly as marketers, we are making use of the data for our current customers, and for our next customers, all of that data that we're creating and using and structuring decisions and actions around is for the better fit and the delight of current customers or future customers. It's not for your C suite, it's not to defend an investment that you made somewhere, it's to deliver a message to someone who needs it, to make a promise to someone who's seeking a solution to communicate that a value has been delivered to them in a way that exceeds their expectations, which you already set because you're the marketer and you told them what to expect along this whole way. So that you can keep going through this right? What do our customers need? What do we need to do and know internally so we can deliver that to them through an alignment and a decision forward data creation way, which we make room for by letting bad data or useless data whether and of course, we never exacerbate a problem because we stop manufacturing bad data. And we can stop manufacturing bad data. Like tomorrow. There's probably a lot of bloat on your website right now. Like that's just that you probably don't need or don't use. Like.

Brandi Starr  27:07

Yeah, so there's, there's I love, I love that there are clear steps. And there's two of those steps that I'd like to dig into further. The first is number one, which is the stop manufacturing bad data, I'd like you to like you said there are different types of bad data like help to define and paint the picture. Because if that's our number one, that's the thing we can stop doing pretty immediately, I want to make sure that those listening really grasp, what is that bad data that we want to stop manufacturing? Yeah.

Steve LaChance  27:41

So data comes in a number of different types, it has a number of different purposes and uses which are different from each other. And they have different values. And I would say bad data has a low value, right? It has a sort of poorly defined use, it's probably defensive instead of progressive in its type is, honestly, I don't want to say like quantifiable versus quantifiable versus quantifiable, but rather, its type doesn't lends itself doesn't lend itself towards a specific purpose. Now a purpose can be either a defensive or a progressive data we are all guilty of and some of it's actually necessary. Maintaining and manufacturing data that has that is defensive in purpose, right. I needed to defend spend on a particular advertising campaign. So I'm going to manufacture and maintain data off of how far did that reach? What was its click through rate, if you ascribe value to that? What were the engagements dwell time on a landing page outcome from that landing page, like you're defending the decision to make an investment, again, I guess or to maintain that campaign. But, you know, conversely, you would be looking for something progressive to seek out such as, you know, sentiment around your brand, after a certain campaign value of I'm sorry, actual money that comes into your company from that, but you know, bad data would be defensive. It's usually used exclusively, internally to your organization. So when I think of data uses, I think of the two big ones of our like informed decisions are they enable a process if you're using data for anything other than informing a decision or enabling a process, some data exists just so that you can calculate a fraction or a percentage, and put it up on a slide to present somewhere we are all guilty of some quarterly business review data metric that exists solely because it was important four or five years ago, even and we just keep updating that number because somebody in the audience expects it. That would be bad data. So if you're not using it to inform a decision or enable a process, you probably don't need to use it anymore. And then data value if you've started load data value. Interestingly enough, all data will degrade over time, all value of data goes down, as time goes on. As time stretches to infinity, its value goes to zero, it doesn't even take that long. There are critical data points that marketing might find useful. For example, my name, my age, my location, my income, my name is going to stay the same. So that I mean, but not necessarily, but for the most part, that name is going to stay the same. However, my age can only go in one direction, my location can change quite frequently. And even on the psychographic side of things, how I make decisions, that's going to evolve as I a human being or your target audience evolves, as human beings, we are all ingesting our own private data diet, right? Through our own streams. And so what was true about us yesterday is less true today. So the problem that we run into in terms of stopping the manufacture of bad data, and the maintenance of existing bad data is, we don't want to clean it up. Because as soon as you change the data, you feel like we will, now we're just messing with the data set. Or we don't want to turn it off, because that's a risk, right? Nobody gets fired for having their database get bigger, right? Big data is great, we can feed it to a machine learning model in a very nebulous way. And something will happen, I don't know everybody's kind of hoping for a miracle there. But you know, there's real costs that come from making bad data, because you're going to find a reason to use it, you're going to use it to inform a decision or enable a process that might not need to exist anymore. And you're just afraid to turn those things off. That's that's never really a good idea. And then the last thing I want to say before I like take a deep breath, and you can jump in edgewise is the data type, right? There's connective data. And then there's like behavioral data, like what is someone doing on my website versus say, what bridges are they building instead of what they're doing on a page, or entering in a field? Or I don't know if people are still giving up their email addresses for like, newsletters and that kind of stuff. Because you know, you're going to get marketed to, but rather like, what is their path and the journey that they are selecting for themself through the larger world of information as they seek to solve their own problems, right, we all have problems to solve. And that's how I think about stopping manufacturing bad data. So find stuff that you know, exists for a reason has been used recently, is generally pretty new in terms of age, and helps you deliver a better experience to your customer, as opposed to justify a decision that you've already made. If that makes any sense.

Brandi Starr  32:55

It does. And so the second step that I want you to dive into is actually kind of the opposite, which is your step three, which is creating better data. And so I'd like to, like really understand what is good look like, like what, you know, at its core, and I know, you know, this is going to vary business, to business, industry, all those sorts of things. But what are some of the data points that we really want to make sure that we are able to get in place that we can get these insights and enable a process or enable decisions?

Steve LaChance  33:36

Yeah. So oddly enough, one of the best things, best data points that you can really go out there and you can find involves significantly more than your own company. In fact, my favorite things in the world are actually benchmarks, I can appropriately sourced benchmark, and we give you a very specific example of what I'm talking about in the product lead growth world. There's, you know, a generalized benchmark around whether the number of visitors to your site plg is almost exclusively going to be self that not entirely self service, but starts off as a self service in terms of research. So visitors to your site plg also always includes a free trial, or freebie components of Free Trial or freemium, or my favorite trial loop. Then from that trial, you will have those who experience the value of your product from within it. And then there are those who come back again for that second, ongoing or third, fourth ongoing experience, that conspicuous value experience, they're aware of it and then there's like a referral rate or revenue expansion rate at the end. Now in each of these step downs, it's a typical funnel. I think every marketer listening to it is aware that you're going to have fewer people turn into referrers or customers then visitors to your website, but what left I left out of that one We're like individual pages that people go to, or if someone spends five seconds on this page and clicks on this link right away, or, you know what, what's our bounce rate on the homepage, like that kind of metric is great for describing, you know, a point in time and a very particular framework for a website. But what it doesn't tell you is the funnel that I just sort of described or the flywheel that I prefer to use, where have 1000 people who come to our site, how many ended up being customers, and what's a good number for us. And the best data out there for that is you're going to know your own company data, you're gonna know your own, I guarantee you that's in some of your data. And that's a good one to have those five key elements who's coming to the site? Do they start a free trial? If they do? Do they make it all the way through signup? If they do? Do they experience the value of the product? And you know that based on their experience in? And do they come back to try it again, like all of these are indicators of how well marketing is telling the story and making promises and working with the product team to deliver on those promises? Those are all great data points, who you are and where you sit in the world. I don't really care, quite frankly, I mean, because we, you're usually solving a pretty, you know, universal problem, connectivity issue, like that kind of deal. Another thing that people, a good data point to connect with there is going to be how folks who have been in your product, for example, choose to share the output of your product? Are people screenshotting things off of your product? And then, you know, moving and sharing that out in the world? Are they inviting their friends into it? Are they logging in on their own solving a problem hitting export, and never bringing another colleague or friend into your product at all? Like these? are behavioral connected data? metrics to track like, what's a human being doing in the screen and on the screen, as opposed to you know, where they are in the world? Or what's their job title? Or whether or not their company just raised a seed round or something like that? It's stopped me if I failed to address your question adequately, because I can try it again. But well, it's going for. Yeah,

Brandi Starr  37:13

I think what I'm hearing just to kind of distill it down, is the key in getting your data in order is really identifying those core metrics that really matter. And we talked about metrics that matter all the time. But those things that really are those true indicators of how the business is performing, where the revenues coming from, and not focusing on all those little, you know, what I call the vanity metrics of the things that make us feel good? Yes. So I think what I take away from this is, it is really thinking about the problem differently. Because I've always been an advocate for, you know, metrics that matter and focusing on those things that really impact the business and not, you know, like, discounting the use, I won't say completely stopping because there are certain things where some of those other metrics are useful. But stopping the focus on those things, stop reporting them upwards. And framing it that way, is a much easier problem to solve, than trying to get all of the data. And that's what you're calling, you know, that manufacturing of the bad data or the vanity data in trying to get all of that in order and making sense and, you know, being useful, etc. So it's like, it's almost tackling a smaller problem than I think what I was initially thinking about it,

Steve LaChance  38:56

it's yes, for everything you just said. And it's it's really about tackling a series of small, achievable problems, right, so that you can demonstrate a progression over time to I mean, that's, you want to be able to deliver value out of any of these projects, right? Hey, boss, we just stopped doing or we're thinking about stopping the tracking of this metric, because it consumes this amount of resource for us, it's kind of a headache, we're not actually sure if the data is accurate. To take, for example, one of my least favorite vanity metrics out there is like the number of followers you that your company has on LinkedIn, or the number of impressions, a post or an ad might have somewhere or even the number of clicks on an ad, for example, is decreasing that number that the validity of that number is decreasing over time. There's just a number of mechanisms out there that are well beyond your control as a marketer to determine the value of a click or an impression or a follower on LinkedIn. And by the way, if you're looking to boost your followers on LinkedIn, start posting jobs, people tend to follow companies after they apply for it, it's a very terrible way to do it. And it's dishonest, and has lacks credibility. But if you're gonna have cheat on vanity metrics, that's one way to do it. I would say that, you know, when I think about specific data points to go clean up, right, you're gonna hear some numbers that you look at marketing leader, you look, maybe you've got a dashboard, you look at every day, maybe you've got some columns that you naturally have to update, maybe there's a, you know, a report on pick your favorite CRM or marketing automation tool that you look at. And there's numbers in there that you're just kind of assuming matter. And I would say pause, when you see that number, pause and ask yourself, What if this number were to go up? 10x? What impact would that have down the road for my company? Like, What would change? If open rates went up from you know, 10% to 100%? What would happen from that? would you what would you change? Like, what can you even tell yourself? What you would change? What would you go recreate in the next email that you sent out? For example? Or if it went to zero? Like, what would you change? So there's the extreme tests that you can do, but it's really about like, which are the ones that you think matter? And if you don't know which ones matter, then I guess start somewhere, pick one. But start with the ones you you're you have a hypothesis matter and stress test them, you know, war game, those with your leadership, like that is something that's worth getting into is figuring out sort of what information you manufacture, create, package and pass along, how is it being deployed? Not just like, top down from you down? But up? What is what are your peers using it for? What is your boss using it for? If you could expose that value to the, you know, the most junior member on your organization? Would they know what to do with that information? And if the answer is no, then you can either teach them because it's valuable, or don't use that number. Like maybe you maybe you quickly find out that you don't need that information at all. There are plenty of stories out there in the world. I'll give you one real life example here with this company where I came in, dude about a little under a million dollars in revenue, but losing money. Now doing significantly more than that, and making tons of money. We were working very hard to figure out, you know, where people were coming into the website, what they were doing their, which articles they were reading, what questions we could answer for them with just content that was on the website. And then we were just sort of dumping it into the CRM. But that would also drag along a lot of information, right, where like their physical location, which was important, but we thought it was important. We dragged in, you know their name, and then we use that to triangulate on like job title, and that kind of stuff, all in the hopes of identifying a certain persona, we wanted to put a human being in a box based on demographics. And then we'd make some assumptions on like, their psychographics, and how they make decisions. And like all of these things, like certainly around persona stuff, we manufactured all that we took a test or hypothesis, and we were testing it. But it we're spending a lot of energy to keep these plates in the air. And we eventually realized that of the I think probably 200 data points that we were collecting. What really mattered was not how they found the website and what they did once they were there. But how they found the neighborhood, they were in particular product and services, you know, had to do exclusively with their physical location, in improving in that kind of thing. So what mattered was less say, home value, right, but rather time in that home, right, if you had been in that home for this particular company, core target. If you've been in there for less than three to five years, that was actually far more valuable than someone who had been in that home for 20 years and worked up a whole lot of equity, which was a complete flip of what we had thought we originally thought, Oh, you've been in there a while, probably want to do some upkeep probably want to make some significant changes, you've got a lot of equity you could borrow against back when rates were nothing that was assumed to be a good thing. But it turns out if you've been in a place for 20 years, you're actually really not in the mindset to go make major remodels or disrupt your physical location to, you know, to fix those things. And we're spending 1000s and 1000s of dollars per lead because the ROI was there we thought the ROI was was what mattered. It's okay if we spent 10 grand at any one lead because if one closed, it would completely cover everything else. Turns out ROI was a bad metric to be making decisions off. We would go spend way more money than we needed to when we could have looked at the important things that mattered like time in that neighborhood time in that particular home within a specific window. A number of other things that I'm just drawing a blank on. And I don't want to force an editor to have to go find later. So I'll just stop there.

Brandi Starr  45:17

So I think this is really good. And it changes the mindset around data. And so I always like to walk away with actions. And so I always say nothing changes, if nothing changes. And in traditional therapy, the therapist gives the client some homework here at revenue rehab, I like to flip that on its head and ask you to give us some homework. And so you know, in thinking about, you've given us some clear steps. But for those that are listening, and you know, what you're saying is really resonating. Where would you propose they start? What's your one thing?

Steve LaChance  45:55

Sure. So as marketers, we have three big problems to solve. Use case creation, so we can go to the product team, and they can build products that people want, that would be one of the PS, the foreperiod, new logo revenue, obviously, marketing is working towards bringing new customers into a company to help drive revenue growth. So you're gonna talk to sales about that. And then revenue expansion, the most profitable revenue that you can bring into your company is from your existing customers. All the data in the world supports that decision. And that's customer success. So what you want to go do tomorrow is spend if you've got, you know, two, if you got two things you can do and one time slot available to it, and say it's like, let's do another focus group with our customers. Or let's go have a one on one meeting with my customer success team, or my sales team or my product team, go have that internal conversation with product sales or success. In that conversation. What you want to do is talk about what information they gather from you today, what you already provide to them, use them use their words to tell you that way, you're not reporting to them, Hey, I send you these 300 things. Which do you use? What should I stop giving you because they're also maintaining bad useless data? You want to hear from them? What information are they taking from your group to help them build better products, bring in new logos, and expand the current revenue base, focusing on what value they ascribe to those different data points and insights that you put together for them, and what decisions you're informing for them, this will help you really distill down, what's worth your time. And what's going to go into the hypothesis Hopper for we don't need to maintain this anymore, we can deprecated. So again, go in tomorrow, by somebody's lunch, send them a coffee, if they're remote, whatever it takes to just talk to them about, again, what they're getting from you, what they're using it for, have them use their words for it. And then you as the marketing leader, can think about that from a marketing context and your own process context, so that you can shift your team's efforts and very limited resources, mainly time and energy towards use case correction, or use case creation, new logo, generation and revenue expansion.

Brandi Starr  48:24

Awesome. Well, Steve, I have enjoyed our discussion. But that's our time for today. Before we go, how can our audience stay connected with you?

Steve LaChance  48:35

Sure, sure. Well, you can reach out to me@discerner.com, which is my organization, you can find me on LinkedIn, I'm confident that that's going to be pretty easy to find stable chance. Or if you're really interested in I mean, more of a product lead growth, approach to things or even just becoming a more of a company leader within your organization. Go to marketing plg.com. That's the website for the book. And the book is not just about product lead growth. However, if you're in any b2b space, there's already a 60% chance your company has a plg motion, there's 100% chance they're going to at some point, so it's probably worth it. While you're on the website, you can actually read the first couple chapters, I think for free. So if you don't love it, you don't have to buy it. But if you do, you can find it there.

Brandi Starr  49:24

Awesome. Well, we will make sure to link to discerner, your LinkedIn and the books website. So wherever you are listening or watching this podcast, you'll be able to follow and connect with Steve. Well, thank you so so much for joining me.

Steve LaChance  49:41

Thanks for letting me geek out about data. 

Brandi Starr  49:43

Awesome. And thanks, everyone for joining us. I hope that you have enjoyed my conversation with Steve. I can't believe we're at the end. We'll see you next time.

Outro VO  49:55

You've been listening to revenue rehab with your host Brandi Starr your session is now over, but the learning has just begun. join our mailing list and catch up on all our shows at revenue we have dot live. We're also on Twitter and Instagram at revenue rehab. This concludes this week's session. We'll see you next week.