In this episode, Brandee Sanders, VP of Marketing for Modal shares her perspectives and insights on the data science process and how best to use data science to go from pilot to pipeline.
Modal is a digital sales solution driving transparent auto e-commerce for the world’s largest brands and dealers. (Note: at the time of recording Brandee was a senior director and the head of marketing operations at Appetize.)
Some topics we discussed include:
- What is a data science process
- What is a data science life cycle
- Stages of data processing we need to be aware of in business
- How to use the data science process to go from pilot to pipeline
- How to determine if you are ready to use data science to drive growth
- How to ensure you have one source of truth
- How to find the budget for ABM initiatives backed by data science
- How to ensure you have leadership buy-in to go from pilot to pipeline and that a top-down directive is in place
- How to get and use an innovation budget to test your hypothesis
- The best ways to shift and reallocate budgets from existing programs
- and much more…
Brandee Sanders 0:00
I did a great presentation at that that summit, that Tech Summit in San Francisco, where we talked about the idea that quite often, when people talk about things like IBM or being data driven, it's all conceptual. So it's very high level thinking, and you're in the audience, and you're super revved up about it. And you're like, Yes, I can't wait to get back there and put that in my Trello board on my Basecamp, or my work phone or whatever. And then when you leave the conference, and you've heard or you're in, you know, some like YouTube environment or whatnot, virtually absorbing this information, you get back home, or you're in your virtual office, where we are now. And then you sit down and go, but what's the first step? And quite often this grandiose concept of data driven? What the hell does that mean? Like, how do you turn an entire org into understanding and evangelizing its own data literacy, and that's, it's supremely difficult. It's, you're not going to drop a seed today and come in tomorrow and see a Sequoia. It's, it's years of work, and I'm not even I'm putting that very lightly. It's many, many quarters of work. And it's collaboration, that's quite often, you have to kind of stretch across the aisles and bring two parts of an orc together to make them agree on what their goals are.
Vinay Koshy 1:12
Hi, and welcome to the predictable b2b success podcast. I'm Vinay Koshy. On this podcast, we interview people behind b2b brands who aren't necessarily famous, but do work in the trenches and share their strategies and secrets as they progress along the journey of expanding their influence, and making their businesses grow predictably. Now, let's dive into the podcast. Your Business generates large volumes of data. But how do you leverage that data to ensure that you can predictably grow and scale your business using a data science process could help? For those unfamiliar with the term data science is the area of study which involves extracting insights from vast amounts of data by the use of various scientific methods, algorithms and processes. To put it simply, data science enables you to translate a business problem into a research project and then translated back into a practical solution. Our guest is an expert at just that. She is a senior director and the head of marketing operations at appetize. Prior to her current role, she managed a wide spectrum of projects for a variety of clients from Silicon Valley startups, to Emmy Award nominated film studios and fortune 500 companies. She is an award winning digital nomad, director and marketing technologist at the intersection of creative and commerce specializing in data driven, quantifiable results. She's also an active keynote speaker and women in tech educator, Bernie Sanders. Welcome to the podcast.
Brandee Sanders 2:52
I love that intro. Thank you so much. It's it really helps with my imposter syndrome when other people. Thank you for having me, for having me. I appreciate it.
Vinay Koshy 3:03
I was just looking through the details of what you would done and you say you're tired and I can't I can't imagine why. doing all the things that you've accomplished.
Brandee Sanders 3:14
I can't imagine why I look and feel this way. You're gonna notice that coffee my beat up coffee mug has just made so many variances in speaking engagements. So yes, thank you though. That was a generous intro. I appreciate it. And thank you for having me.
Vinay Koshy 3:28
No worries. But despite all this, I'm curious, what would you say? Is your personal area of strength?
Brandee Sanders 3:36
Ah, that's like such a trick question. It depends on who I'm talking to, you know, actually, I think it's so when it comes to my particular brain and the way that my brain operates, for better or for worse, I should say I straddled both hemispheres. So traditionally, with marketers, you get people who are brilliantly creative, they make beautiful, engaging storytelling, visual assets, design, look, feel UX UI, like the beauty of something, and I came from a film and a classically trained performing background. So I definitely have that kind of out of the womb strength. That's, that's the hemisphere I was born and more kind of, like, you know, pretty naturally leaning towards. And so you get folks who are great at that. But quite often, particularly in business and technology, they struggle to quantifiably, statistically and empirically, measure efficacy and be able to show how the beautiful thing or the lovely campaign or that great storytelling or that video asset or that multimedia impact things like revenue, bottom line ROI, literally how it created demand or brought people in through whatever funnel process that you're bringing them through. So you tend to get that super creative person that lacks statistical analysis because it's the other side or conversely, you'll have someone who is extraordinarily good at number crunching regression modeling our SQL Python there are qualitative studies. testable, empirical analysis, but then they are not very creative. And quite often socially, they aren't the best evangelists. They're great citizens. They're wonderful citizens, but they sometimes lack certain types of skills that would make the action ability of the storytelling that the data is doing impactful for that audience. It's hard to get them to emote, and to kind of be tied into what does this mean for us. Like, if you're trying to translate data into sales, you have to understand sales has its own unique language, product has its own unique language, the engineers have their own language marketing, demand Gen, all the way down through that whole funnel, including leadership, depending upon your leader, which there are many, many colors and flavors and spices, you have to be able to tactically navigate how you're translating what you're talking about. And so I think that part and parcel when we talk about my superpower, or whatever, my my, my talent, I guess you could say is that I feel like I spend a lot of time being a bit of a un translator for those groups, I travel effortlessly. I've been in sales, I've been commissioned, I owned my own business. And I've come from kind of like that urgent hunter mentality. So I understand the urgency there. And then I understand how they view marketing, which is like the cool kids who like sketching and notebook, but I have no idea what drives revenue. And then on the marketing side, I've been the creative who doesn't feel the work is valued. And then on the product side had been a part of the team that's building a roadmap that has to explain all of this to that triangle. And then beyond that is the ecosphere of leadership, which requires a very specific, sophisticated way of discussing all of those different departments and making sure that you have things very explicitly transparent, visible and understandable. And so I think that that's usually that's where I am as I straddle both hemispheres. So that's the answer.
Vinay Koshy 6:53
So if I were to ask you, in that era of strength, what is it that businesses don't know? But should? Is it this idea of being able to communicate across specialties and expertise?
Brandee Sanders 7:09
When we talk about what they should know, I mean, half of the time, and I there's so many great data points on this, but I'm thinking of, I think it was the DMV report 2016 analytics report where it said, and I'm probably misquoting so please don't send me hate mail, I get it, just Google, just google it or check out my decks on SlideShare the answers in there, I think it was like 38% 30% of organizations that were that were sampled for this, this study had said that they were relying on third party analysis. So external analysis, and then over 87% had said they wanted to be data driven, right. But then quite often, you you could enter into an org that talks about wanting to be data driven, or thinks it's data driven. But then you lift the hood up in the CRM is a mess. I everyone's operating in silos, you know, marketing has no idea what product is doing product has no idea what marketing is doing. There's gaps there. And then there's also gaps, the traditional gaps that chasm between marketing and sales as well. And so it's really important to have someone who can travel between those realms, and if like, very effectively communicate, what the business objectives are for those unique those unique sections of the business and how it's tied back up to that the more larger goal of the org itself, certainly. So I think it's one thing to say data driven, it's another thing entirely to do it, because that's a top down cultural paradigm shift that requires teeth that quite often from the bottom up, you can't do.
Vinay Koshy 8:36
I think it'd be fair to say that a lot of organizations like the idea of being data driven, but I'm quite there yet. There's something that you've developed in a level of expertise. in is this idea of going from pilot to pipeline.
Unknown Speaker 8:53
Yes, pilot to pipeline. That's my, that's, that's my favorite. Yeah. And I mean, I was fortunate enough, a few years ago. And I know I can, I'll give you the link. It's in SlideShare, or one of my decks out there for demandbase. When I was at blackline, I did a great presentation at that that summit, that Tech Summit in San Francisco, where we talked about the idea that quite often, when people talk about things like IBM or being data driven, it's all conceptual. So it's very high level thinking, and you're in the audience, and you're super revved up about it. And you're like, Yes, I can't wait to get back there and put that in my Trello board and my Basecamp or my work friend, or whatever. And then when you leave the conference, and you've heard or you're in, you know, some like YouTube environment or whatnot, virtually virtually absorbing this information, you get back home, or you're in your virtual office where we are now. And then you sit down and go, but what's the first step? And quite often this grandiose concept of data driven what the hell does that mean? Like, how do you turn an entire org into understanding and evangelizing its own data literacy See, and that's it's supremely difficult. It's, you're not going to drop a seed today and come in tomorrow and see a Sequoia. It's it's years of work. And I'm not even, I'm putting that very lightly. It's many, many quarters of work. And it's collaboration, that's quite often, you have to kind of stretch across the aisles and bring two parts of an org together to make them agree on what their goals are. And even sophisticated businesses quite often, if you say, what's the goal, each part of the org will have its own unique idea for their particular department. But when you say, what's the goal of the company for like, 2021, for example, we should all be able to answer that effectively. Right? And and that's not always the case. That's not always the case. So yeah, pilots a pipeline is a great example, because that presentation, and the story I told was really about going to an organization and being an organization that was growing extremely quickly, had turned on a massive demand Gen machine, and was building the plane while you're flying it, which is like a fantastic euphemism, like we've all heard it before. Or we don't have time for that. Now, that's another, that's another good thing you always hear. We'll do it later, next sprint next month, next quarter. But the idea was like in order to prove to get organizational buy in on a sophisticated tool, we had to run a pilot and effectively do that ABM pilot with with demand bases, which was the tool at the time we were using to get organizational buy in on things like personalization, and ABM, things like that. And so it was a pilot that in this is, regardless of whatever piece of the MAR tech stack we're discussing, where you have that pilot where you have that select experiment that you're doing that allows you to come up with results, bit of data that based on that base, you have enough information to get a yes, for the next bit, right? So it's very much like, you know, what is it crawl, walk run, right? You want to be able to take that from what used to take months to get to yes, to take weeks, and then days and then hours and then eventually live within a dynamic ideal as opposed to static?
Vinay Koshy 11:58
Let's see if we can unpack that. So first off the data science process. As I understand it has probably six key stages to it. Yeah, from problem to collecting the raw data, you need to address the problem process that they have for analysis, explore the data, perform an analysis, and then communicate the results of that analysis. Have I Got that?
Brandee Sanders 12:25
Vinay Koshy 12:26
Brandee Sanders 12:26
Yeah, you are actually I guarantee you, we're probably looking at the same visualization, because I often quite often, in case you haven't picked up, I can get fairly tangental. So I always like to have this this up. And it is very much it follows that process for the most part you stick to that. And the same way it's you have to think about almost like is the scientific method, right? It's a very empirical way of looking at things and understanding like, what is the actual, what is the problem? Or what business question are we answering? For most data scientists or data analysts or data engineers, half of the time people will come with an ask, especially if it's an immature organization, or it's gone through rapid growth or merger and acquisition. And so you've had many stakeholders kind of come through and change what the goal is, you'll have them ask a question or ask for like, hey, do this report, or can you do this? But you don't know what business question it's solving for? So like the business question, particularly like, you know, what is this look like now? Or like, Who are you? What what is it that you're actually trying to answer? It might generate a more sophisticated solution, right than just a, here's a PDF of a report that we did that we pulled from this, this dashboard for you, right? We wanted to, to understand that like, very methodical way of thinking, that requires muscle memory to be built by asking those questions each and every time these must be answered in order to have the scope and what the requirements are for the question that we're answering.
Vinay Koshy 13:54
And a lot of companies are generating lots of raw data, in fact, swimming in it. Yeah. Yeah, this, this idea of being able to process data is not as simple as just collecting raw data. There's, I believe a few stages to it, which include collecting the data, preparing it,
Brandee Sanders 14:20
Vinay Koshy 14:22
putting it into whatever system you're going to use to process it, processing it, and then pulling it out and storing it. Would that be correct?
Brandee Sanders 14:32
Yeah. I mean, the I, yeah, for sure. And I think that and there's so many resources online. So if you're, if you're new to what we're talking about, just literally put steps to processing data, or what is data management into Google and you're going to get a lot of the same things we're talking about. So from my perspective, quite often, data hygiene and governance is the less sexy thing to talk about. Data Science is the new AI. Everybody knows everyone loves to say AI Machine Learning even when it's not. And I definitely feel like that's indicative of like technology as a whole. Like we love the inflated the inflated vernacular because it makes us all feel important and it makes LinkedIn look sexy. But half of the time, the real work that you end up doing is it's it's actual work data hygiene, data governance. I'll give you an example. And I know I mentioned this at the GT Tech Summit two. And definitely at the pilots a pipeline thing, a lot of it came back to literal hygiene. So even if I'll use list lead ingestion, you had an event on the marketing side, that gets the list, the lead list would get passed to the ops team, the marketing operations team tied to the data team. And you're sitting here looking at it, and it's like, Pennsylvania is like p.a.or P and n.or P, capital lowercase a, or capital P, capital A, and you have to go in and correct for these things. Yeah. And that like level of methodical, very, you know, maniacal level of hygiene, it matters. And people get really, they get very in love with the idea of it being like this intimidating, hyper inflated language. But in fact, most of the time, it's just generalized codification, that you are leaning against with hierarchies and explicit rules, or validations, for housing and processing, and distributing and reporting and cleansing and governing that data. So a lot of it is actually less sexy than you think it is. A governance it should be should governance should be at the front of it, right governance is garbage in garbage out when it comes to data. And so I definitely think governance needs a shout out because governance and data hygiene are the, you know, unsung heroes of data science, because half of the time, if you're given data, and it's it hasn't been governed properly, or it's coming from a skewed resource, then you can't even try to answer that business question. Because you're going to spend, you know, the next couple of hours or weeks depending upon the data set, cleansing it to make sure that it's actually accurate.
Vinay Koshy 17:02
Just from what we've been talking about so far, for someone who hasn't really incorporated data science into their businesses. Yeah. How would they know if they're ready for such a level of investment?
Brandee Sanders 17:19
That's such a great question. I literally feel like
Unknown Speaker 17:24
Everyone's gonna have a probably a hot take on this. But I feel like a lot of boards think that they're ready for it. But the maturity is not there, you will have people and this is again, this is part of that kind of like the big Tech Summit thing that I did, which literally talked about like the differences because you could come to a leadership that's very hyped up on data science, because they saw their other leadership at this one place that they were at before do it. And it was great, and it's sexy, and we've got to be ahead of the ball and become digital transformation, data science, everyone gets a dashboard, that kind of mentality. But then when you really get into it, and you lift the hood up, their CRM might not be ready for it, you might not have the resources or the level of organizational maturity, with how you're even handling very base level data. So to get into complex things like regression, modeling, predictive analytics, bringing in a six figure data scientist to manage this sort of thing, before you go and you try to bite the biggest piece of cake in the whole wide world and just eat it at one time, you really do have to start with smaller level things, which is auditing, like audit it, literally just do a quick data hub, data governance, hygiene project, where we're looking in your you could whether that's your CRM, your Mar tech stack, any any and all tools that you are using on the product side, Dev side doesn't matter. Just a comparative analysis of what you're using, quite often you'll find out you have duplicative sources, right, you'll have to pick it up sources. And sometimes those two things may be saying different things for this same business question analysis. And that's an internal project that you can take on that smaller that can be self owned, if you don't have organizational top down by down, buying in. And so I feel like those those smaller projects, and there's tons of resources on that online as well. And I can be more than happy to pitch a couple of those over, but as links or whatnot for folks who would be watching, but I feel like you have to take it one step at a time. Like you cannot go to bed with just having had built like this tiny seed in the ground. And then like I said, wake up tomorrow and have like a garden that's absolutely flourishing. All the grunt work that's involved with becoming a data literate, and data science first enabled organization is back breaking work. It's chilling, aerating the soil, coming back in watering it at 2am before the sun comes up, like all of that very methodical way of thinking that's a cultural change that many organizations, especially legacy organizations, or larger organizations who have always done it this way, they're going to operate very differently than like a series a company very quickly. adapt because there's only 12 employees. So I think when we talk about what you can do, it depends on the size of the org, the maturity of the vertical that they're operating in, how many people are bought in with you? And is it a top down? Is it a top down buying, because if you're the only person trying to make that, that data sophistication or level of literacy happen, and you don't have teeth to make that change, it can be extremely frustrating. And you don't want to burn the candle, both sticks, but you could, at least on your side, initiate higher levels of sophistication on how you treat data that you receive, how its groomed, hygiene governed, and then reported on.
Vinay Koshy 20:34
Okay, I'll ask this question first. So it seems to me that having one source of truth is important. Yes.
Brandee Sanders 20:42
Yes. All the time. Yeah.
Vinay Koshy 20:44
How do we ensure that?
Ah, that's the best question ever. And if I could come up with a silver bullet solution for this, I promise you, I would be retired and about 60 seconds, because in the next 60 seconds, what is it, there was a great, there was a great thing, it's actually gonna be in this deca masterclass I'm doing was like in the time that it takes for us to get through one slide, like 60 seconds, like 165 new businesses have opened or shuttered. It says 70 people have changed their roles. So that's changing. And all of these, like the speed at which data is growing? And what was it 90% of data has been created in the past XYZ amount of years. It's, it's huge. And so we look at like, what does that mean? How do you do it? Where do you even start? Honestly, a lot of that is owned internally, right. And I think that it starts with your team or yourself. And by showing the value of the work that you do and using data to defend that, whether that's in the marketing, side demand Gen operations, Dev product sales, it doesn't matter. I think that's really the first step. I think that's the first step because it's definitely the journey that goes very far. And it's it's difficult to try to silver bullet that but I do think that base data literacy and definitions, even the simplest of codifications, like if I asked you what a data analyst is, and I asked you what a data scientist is, and then I go, am I a data scientist or data analyst? Hopefully, they'd be able to answer that question, right. So I think having that kind of differentiation in such a hyped up buzzword environment around like data science, machine learning AI is super important. So creating even a base level glossary is massive, like being able to say like, this is this and that is that this is this, and that is that for our organization as a whole.
But would you say that for most organizations, a good place to start with would be perhaps then a CRM?
Unknown Speaker 22:42
The CRM is definitely it. I mean, in my world, Salesforce, or whatever your CRM is, is the one true source of knowledge. Because if it's not in Salesforce, it doesn't exist. If sales doesn't log in email or call it doesn't exist, verbal doesn't exist a post it note on your desk, it doesn't exist, because if I operationally I'm putting together a very targeted campaign for like a C suite, XYZ and XYZ vertical XYZ number of employees XYZ revenue, XYZ time of year because this is going to be their their big trade show season or something like that. If I'm leaning into that, and saying had no activity in the last 30 days as a variable, a Boolean rule in Salesforce or the CRM, and there has been activity, and it's already with them, but it's not documented in the CRM, then nobody gets to be mad when that person gets targeted. So the CRM has to exist as the one true source of knowledge for the organization. And all of that, though, if if you live in a fairly immature or or what I kind of organization, it's difficult to get folks to enable that. So I usually start internally, and then distribute to team and then we evangelize through action. But the CRM for sure, for most businesses absolutely would be the one true source of knowledge, particularly when you're looking at like a b2b SaaS tech enterprise.
Vinay Koshy 24:00
So would you recommend that for organizations and looking at implementing data science that they should perhaps
Brandee Sanders 24:07
work on that CRM first, first,
Vinay Koshy 24:09
but even then, at times, at least from my experience, it can be a bit of a challenge to try and do this on your own. So ....
Brandee Sanders 24:18
Oh, it's impossible. It's impossible. Because we don't have the...
I like I wouldn't have the teeth to come in and tell C suite they need to have a Salesforce lock in. They're not going to take too kindly to that. And the same would be if I had a junior operations specialist who's saying, hey, SVP, you need to have your your calls and your emails in Salesforce or, hey, you know, partnership person, like you need to, like enter that action in Salesforce. Like it really does have to be a top down a top down initiative. And the buying has to come from leadership because otherwise it won't, you won't have teeth to initiate change beyond your own department. No one department owns the data. It is us together as an organization as an ecosystem. them that all together are responsible for data. It's not just infosec. It's not just it. It's not just product. It's not just sales. It's not just Dev, it's not just marketing. It's not just an engine, it's not just web, everybody is responsible for data, hygiene and governance. And all of that, for the most part lives attached to the CRM, which is the one true source of knowledge. So in order to initiate data science levels of sophistication, you got to fix that CRM first, certainly got to do the grunt, you got to do the groundwork before, you know you've got to do a play before you're a prima ballerina.
Vinay Koshy 25:30
So would it make sense to first address the issue with the executive leadership? and case studies are something that they absolutely do.
Brandee Sanders 25:41
Unknown Speaker 25:43
Yeah, and I feel like a lot of the time with with more tech solutions, or particularly from like the marketing and the data side, you can get that now. Like, there's fairly sophisticated analysis. And there's so many decks on this, like, one quick google search, and you're just going to be absolutely drowning in decks on like, literally just searching how data silos impact organizations, or how data governance or hygiene or how data science, mature maturation levels with with orgs, even those key words, and you're going to get a plethora of knowledge on this. But it's important to know that like just slapping a six figure data scientist into a role isn't going to solve it. Because that one person cannot fix an entire org, it really is organizational buying. And if you're going to approach leadership, it's really important to come in obviously graciously with data that supports this, of which there is Gardner, Forrester, you name it Dun and Bradstreet, there's so much data that you would drown in it by very verifiable like Harvard Business Review, like get in there go find out what college they graduated from whichever Ivy League they're from, go get it go get that that study because it's there. And it could supplement that and half of the time, they're going to ask for the pilot. And that's why the pilot is important is to start with a small project, a very small project, prove the efficacy and then get that organizational financial by and certainly,
Vinay Koshy 27:01
When we say pilot in place, perhaps the requirements for a budget to be allocated to the pilot, especially if your organization is that or perhaps increasingly looking at going towards ABM.
Brandee Sanders 27:15
Oh, yeah, yeah.
Vinay Koshy 27:17
What would be your recommendations in terms of approaching the executive leadership appropriately, and maximize?
Unknown Speaker 27:25
Borrow, literally borrow from your own bucket first, okay. And if you have faith that the pilot that you're about to launch is going to have efficacy, which a lot of the time you will see that effectiveness from something that's geared towards a meme, like even just a webpage that's personalized, like gas and oil industry, if they're landing on that page, and the assets, the visualization, the content, the case studies, the white papers aren't just like, here's our product for everyone, all industries and all verticals, but rather particularly towards gas and oil, you're going to see Double, double digit triple digit in most likely unless there's something crazy that content improvements in time on site, average duration, click through downloads, etc. So like being able to start with something very small, like a personalization project, data governance hygiene project, like you can start with something very small, and borrow from your own budget. Like there's in almost every budget, there's a little bit of slush fund, so you can take that and reallocate it.
Vinay Koshy 28:20
Yeah, certainly. And assuming your pilot is successful, and you're looking to build out towards pipeline. Yeah. I guess another layer of complication is now the fact that you've got to start pulling in other teams,
Brandee Sanders 28:35
Vinay Koshy 28:36
and resource Yeah. Again, your your recommendations.
Brandee Sanders 28:40
So the minute you start talking ABM, you have to understand that you're going to be married to sales. It's not just fiance anymore, you're picking out China patterns, like you, you're gonna have to cross the chasm. And I think this is where that translation of being able to understand their mentality, their level of urgency, the pressures that apply to that world that are uniquely different than marketing, are massive, because it's a completely different world, like they're directly held accountable for things and marketing should be in is as well. But uniquely, the level of visibility for rev and pipeline Gen are just two very different things, when leadership obviously looks at that with a lot of scrutiny. So I think that you have to before you, if you're going to if you're going to pitch a pilot, that should already be in the mix, you would have the SVP of sales cc on that email, because you're going to have to select the accounts. And if you're going to select the accounts, then they're going to have to be targeted to for that pilot, which means you would say what accounts have the highest value for you? Which ones do you believe are more likely to do XYZ, like you're effectively targeting, and that means you have a marriage between those those two departments that has to be explicit and understood that it's not just like a one time thing. You have to have that visibility across the thing more than just like once a quarter for the cube er, we like see each other for five minutes, and you're like Bye. And then you're just sending them like assets, like, here's the white paper, here's a case study, here's this thing on the webpage, like it really does have to be a push pull dynamic sink, as opposed to a static thing that they get. And that's it, it has to be push pull, which, again, is a lot of characters, a lot of different personalities that you work with across those realms. So being able to effectively translate Why the hell you should give a damn that we're running this pilot really matters. And when you say things, like we're trying to improve the quality, that usually perks in Europe, because half of the time sales is going to say these are crap. You know, that's almost that's like the anthem, like these leads are crap, these leads are crap, you know, this SQL is garbage, no way, they're gonna close. And so you have to be, you have to be ready to kind of counter that with data that suggests otherwise. And if they take two steps forward, and you take two steps forward, that's for for the team. And then that gets you a little bit closer to an ABM model, which again, takes time it takes this is not an overnight, it's not an overnight success. And anyone in leadership, who would think that is incredibly lovely, so naive, and fairly data illiterate, because it really does take a lot of effort to truly get to a model that's sophisticated enough to be called ABM, there's a lot of work that you have to put in. But in the same way that we were going to kick it with that garden thing again, if you do the work right the first time, you don't have to reinvent the wheel, you just come out next season and lay the seed down again. And I think that makes a tremendous investing and doing it right the first time, versus reinventing the wheel every two quarters is really what matters. Because you have to look at longevity, there has to be what worked is 7 million arr will not work at 70 million will not work at 700. So it's all about being able to scale effectively, through repeatable successes and standardization. And the way to do that is obviously by leveraging data and treating the CRM as the one true source of knowledge and having cross departmental collaboration.
Vinay Koshy 31:53
Could you help us wrap our minds around this whole idea? Because it's fairly broad? With with perhaps an example you will you're familiar with going from pilot through to?
Brandee Sanders 32:04
Yeah, yeah, for sure. So I was with an organization that mean it's in the deck. So I was with blackline, I was back with blackline, great, amazing company, just such an amazingly talented or wonderful team that I had there. But I was with blackline. And we were we were trying to pitch the idea of targeting and ABM. And it was, of course, a classical high growth chasm between marketing and sales. And so we were just starting to kind of get into the idea of investing resources into things like web personalization, and, you know, tokenisation, and just really digging into personalization versus the generic, canned b2b demand Gen machine which had existed previously. And so we tested a pilot, we actually use Debian base for personalization. And this is in that presentation pilot to pipeline demand base at the Tech Summit. And what we found is that, again, the personalization aspect, when someone lands on a page with content that is curated for their role for their vertical, that compel that is compelling to them with a particular language that is unique to their role or unique to their industry, or unique to themselves. Just like if you love cats, and you land on a page for for animal food, and it just shows horses, dogs and cats. But if you go to that page, and you love cats, and it shows you cats eating cat food with some facts about how katzie food, like you are more likely to engage with that content. It's a very simple idea that often has some complex things behind it when it comes to ABM, but it's it worked. And we had double and triple digit, I don't have the results, that KPI is in front of me right now. But that's in the deck. And we saw amazing success from that when we re engineered again, for a different initiative that I was working on, which was for LinkedIn. And again, we saw double digit and triple digit improvements on things like click through time on site, did they read? Did they attend the read to attend like retention, all those rates, repeat visitors, all that kind of stuff. So like being able to even take something very small, like personalized content on this page, because you have to think if you have more than one vertical you're serving or your industry, that means whether it was one white paper for generic now there is five or 10, which means resources, you don't have that one person just making that right? And then do you want to have niche creators who were unique to that role or who are unique to that vertical, who can spin and turn up content in the way that is most effective to engage that audience. And so coming in coming in with not just the pitch for the pilot, but having in the back pocket data that supports the additional resources you would have to ask for to land and expand, I think makes a big difference.
Vinay Koshy 34:38
In hindsight, is there something that you would recommend people watch out for but they could easily trip up on through this whole process of filling the Leanna pipeline?
Brandee Sanders 34:51
For sure. So people are feeling beings, they have emotions. And if they're not, if they are not analytically loving, metrically minded person. And it's always been this way. And it worked up until now, why are we changing it? You have to be aware that you're going to be approaching quite often, not always. It's not universal. But in my biased opinion, quite often I've seen it a legacy mindset. So your job is to be that data evangelist, right? You have to explain it to them. It's not just for hype, it's not some newfangled idea where we're just like making everyone do data or data science or have dashboards or reports, just because we love to like, look at, you know, Tableau visualizations, we really do want people to be understanding what they do. And is it is it effective. Now, some people tend to get sensitive about that, I would say a precautionary word is is to be prepared for some pushback for folks who have never had to lift the hood up and show people what they're working on. Because they're going to take a lot of folks, not all, but some certain departments for sure. Take it very personally, when you ask them to be accountable to someone they don't report to directly. So but if you're pulling in their data, and you're being effectively like in an ongoing, like dynamic situation with them, then that relationship needs to be established, because it's it's push and pull, right? So it's dynamic, it's a two way street. We're going to show you what we're doing, what is effective, how it's actionable for you. And then you can show us what your feedback is what you're doing all the different kinds of rates, conversions, percentages, KPIs that are affiliated with that. So transparency, for some folks is a painful process. I err on the side of hyper transparent in case you can't figure that out. It's that it's open book, like there's no cloak and dagger, no mirrors, no pompous Bs, hyperinflated language that makes it a difficult barrier to entry into data, a strip all of that away, because most of it is just absolute expletive deep, it's absolute it. And so I feel like a lot of that people get very stuck on on that analysis, paralysis, that language, that inflated nonsense, that ego driven stuff. And when you strip it away, it's really just about being able to do your job faster, better, smarter, and then getting that time back so that you're not doing reactive ad hoc work or reinventing the wheel every quarter. And you're able to step back from some of the other stuff that you've you've made operational that you've made automated and look at things like strategy, which is for many people, what you'd want to be doing, you don't want to be stuck in, you know, Excel hell, for eight hours a day, who would I Well, there's only a few folks that I can think you want to do that, and they are on the accounting side. But give him a second, because that's going to get automated too. So I just I definitely feel like being able to kind of reach across those chasms and explain like, here's what we're doing, we're doing it together. And being mindful of the human element, which is traditionally ego gets in the way, you don't need to know that they get people get very protective about work. And you have to make them understand that like, this is why top down matters is like the org wants to see what's working and what isn't. And wouldn't you rather be working on something that generated higher sales, because if the company makes more money, hopefully you would make more money, you would get a better review, merit increases bonus ot like, there's a lot of things that are tied to that. So you have to kind of like pick it apart and be a bit of a site, the psychiatric element of like analyzing the best tactical way to approach that character.
Vinay Koshy 38:10
Excellent. I'm also wondering about the lifecycle of data science, because that can be quite a quite a process in itself. And another question that comes to mind is the longevity of all that data that of your story.
Brandee Sanders 38:29
Vinay Koshy 38:30
Could you talk us through the implications of of that?
Unknown Speaker 38:34
Yeah, I mean, there's so many, that in and of itself, I think, is its own session, because you get into things like how long you can keep something by legal compliance standards, and then things like GDPR there's so many COPPA. Wow, there's, there's so many different things that you can look at when it comes to how long are we able legally to retain this data? What of this like PII information and stuff like that? What can we retain? What is appropriate to retain, versus what can we retain, you can get into very complicated stuff with that. But when it comes to the like, insight driven organizational maturity scale, there's like those five cycles that you could look at one where it's like you're aware of analytics. So you know, you can measure stuff, you maybe you're pulling it out of like the, the regular, like, I always think of GA, like just audience overview and just export the PDF and like that's it. So it's just like very high level, like month to month or bi weekly analytics, you're not really looking at the long picture. The second one would be localised, where each department kind of had their own way of looking at analytics, but doesn't understand how it works with others. Then the third part, you're coming into it where you're basically looking at like, you become aspirational with analytics. So you have ideas that might need cross departmental like the ABM model, or pure data science, where we're talking about things like regression modeling predictive analytics lead modeling sets, And then the two, the two end spheres, which are really the more complex level of the cycle, which is analytical companies, companies that make every decision based off of analytics, like you're looking at large logos like Amazon or, you know, wayfair, or like, these just glides that have entire teams dedicated to qualitative, quantitative statistical analysis. And it's dynamic. And it's ongoing. And it's not just periodic, or a pilot, it's a part of the lifecycle, and then competitors. So you know, the, the Walmarts of the world, the Amazons of the world, where they've gone from just like they're in that space, and they do it to now they understand how others are doing it. And they're in a competitive space, literally using real time out like, literal real time, not just like once in a while, real time on site visitors and GA, but across products, platforms, hardware, software, everything, and being able to stream that into to improve whatever it is that they're doing, whether that's obviously the customer obsessed side, or servicing the product at a faster speed, or whatever it is. And so most folks kind of get stuck. When we think about, like the lifecycle of it, they're usually stuck at three. Yeah, I mean, usually they're two, they toggle between two and three. And that's really most of the time due to top down literacy issues with like, understanding the importance of data versus what a vanity metric is, versus what makes a certain department looks good, like a percentage that they really cling to, because they think it looks good. But actually, if you peel that back, it's showing that there's been a deficit there for a while that needs to get addressed. And again, that's where you get into like, the emotional aspect of like, Oh, no, you're going to show that we're not doing a good job, because now we're changing how we are viewing success of the company. And so I mean, that's the lifecycle. There's like, really, when you look at like insight driven organizations, and like a maturity scale there, you're really coming through those five steps. And I think actually, I'm poor. I'm cheating a bit. I'm pulling that from the DMV state of analytics report, we're really does talk about that lifecycle, because most folks are kind of stuck toggled between two and three. And by the time you get to four, you really do usually have a data evangelist already residing within the company.
Vinay Koshy 42:03
Certainly, you're currently at Appetize. What How would you describe Appetize in terms of this whole data science application to the organization?
Brandee Sanders 42:14
Oh, my gosh, that's such a great question. It's a great question. I mean, I feel like, I'm not going to single Appetize out just because I'm still here. But I feel like universally for many orders that I've come into, they all have their own character for how they handle data, or whether it's my time at Sony Music, my time with, you know, black line, my time with appetize. Even my time working pro bono doing data for like nonprofits or like smaller organizations that just don't have the bank for that. You come in, in each CRM, each industry, each sector, each leadership, has their own character, has their own uniqueness, has their own challenges, has their own level of maturity, has their own level of data literacy has their own level of enablement. So I do feel like it's like, it depends, it really does depend. And it's, it shouldn't be a surprise that so many rapidly growing enterprise level companies have that because that's what happens when you build a plan when you're flying it. And if you've had like a great product or solution, and it's really growing and doing great, then the expectation is like keep doing whatever you're doing, right. So if you try to come in and and reset on that, I think there's always going to be pushed back regardless of the company vertical or enterprise. So I definitely I feel like to be fair to current to current and then also also previous previous persons and companies and clients I've worked with before, it literally is and I rarely say this a case by case basis, because each business has its own goal, like some businesses are just here to be like brand awareness and turn that into something that they can, you know, acquire or merger off. And then other ones are here, like, Hey, we want to be the long term solution for you know, this plumbing solution or for this tech solution, or the CRM, or this marketing Mar tech stack solution. So they're really here for the longer game. So like, depending upon what the needs are the wants of that leadership, board, p investors, all of that all those things kind of shape the uniqueness of how they handle data. And it's definitely no matter who you are, whether you're a junior person just getting into data science, or you're an SVP or a CTO, it's willfully naive to say 1111 place has it perfectly solved. Because business is an ever evolving, never changing in the 60 seconds, we've talked about how many interactions have happened in a digital space it's ever changing. And so the one thing that I would say is is like to be uniquely aware of that. And that matches the maturity you know, the maturity model is ever evolving. And we could wake up tomorrow, especially in 2020, anything's possible a new model could come out and just blast data science right out the door. And we have we went from AI to data science to you know, whatever Bob Jones thing could be the next solution for us like you know, blockchain and AI ml I mean, it's all buzz worthy. But each each word in each place really is going to kind of cherry pick out the things that are more valuable to them based on their business needs. Certainly. And I have a great team at appetize shout out, I have, I feel like I have to say that like, that is definitely something I'll say is I have a wonderful BDR team exceptionally talented folks, and just a glorious SVP and Tony Lani. So shout out because they actually really do value data, and they have a great heart for it.
Vinay Koshy 45:27
Brilliant. If you had a magic wand, what would be the issue that you would like to solve?
Brandee Sanders 45:35
Oh gosh, universally or just for business?
Vinay Koshy 45:38
Just given the current circumstances, perhaps...
Brandee Sanders 45:41
circumstances I mean, universally for I think universally for for all, for all businesses, I would just say literacy. And I know literacy is such a tricky word. And people often get people often they puff up a little and they get mad when you say data illiterate, but literacy can be measured. So it's important to draw that line in the sand and say, if you can be tested on a subject, then there is a level of literacy just like you'd be tested like Si, T, G, or whatever, like, language is a language and it is very much a language so it can be tested. So literally, I'm sticking to literacy versus savvy because I could say I'm, I'm something savvy, and then you get behind the wheel. And you're like, No, you've watched a car race. That doesn't mean you're a NASCAR. So I would definitely say just the importance of understanding. In a in a world obsessed with growth at all costs. Quite often in technology, we sacrifice longevity for instant gratification. And we are often looking for the hockey stick growth, but not understanding the long game. And the true winners in technology have a long game, right? Whether that's to be acquired to merger, to go public to build this and then build five other startups or something behind it, you really do have to look at long, the long game the longevity, how long do you retain them? Do they come back? Do you have a level of customer obsession with with that group, and I think I think it matters because if you're focused only on today, and tomorrow, then there's a great likelihood that you know XYZ amount of time from now you might not be there. And so I think being able to change the obsession with growth at all costs and become a little bit more like, for example, I'm gonna say his name wrong, Jason from Basecamp. And, and that team over there, it's all been very organic. And it's been, you know, mindful, and it's not rushed. And that product shines because of it. So I think being beholden to yourself operating and having that that level of understanding that like you put the seed in the ground, there will not be a Sequoia tomorrow, this is a long term investment. How long are you? How long can you really stay in the game, I think is important, because quite often, it's like sugar rush, like everybody in tech is on the high carb diet. And we're just like shoving bread in our face. And it's low nutritional value for the business, but you have energy. But there's the inevitable crash that comes after that needs to be supplemented by long term branding, data literacy, you CRM, governance and hygiene, which is really kind of the meat and the protein of that business diet. And I'd love to see less carbs, high sugar peaks, valleys, peaks, valleys, we and I'd like to see let's do it the right way and be here for the long game, which requires a certain sense of, of discipline and then push back. So..
Vinay Koshy 48:21
Is there a resource that you would recommend people use to advance their literacy?
Brandee Sanders 48:28
Oh my gosh, there's so many resources at this point is insane. I myself, am a huge fan of edX, I love it. I've taken some great they've gotten those Ivy League credits up on that LinkedIn because of edX. So you can step into classes like Columbia, MIT, Harvard, you can take a CS course at Harvard, shout out to CS 250. Just like really brilliant educational resources that come with a certain a certain shine that people in tech really love. And those many of them can be free. You can audit those classes, you can take the entire course. And really, whether that's Coursera, edX, LinkedIn learning Khan Academy, I mean, there's so many resources Google Academy right now, Salesforce has trailhead. drift has chat bot certification, there's everything, particularly in 2020, where everyone's trying to skill up and be as sharp as possible. The only limitation is how long can you stay awake at night after work to get that certification? Yeah. Excellent.
Vinay Koshy 49:23
Is there an aspect of data science process that we haven't quite covered? But you feel that we should highlight?
Brandee Sanders 49:32
Oh, gosh, I know, I think we've covered a lot. I think I think we've I think we fairly covered it a lot. And I mean, it's never it's never ending there. It's all about as cheesy as it sounds. It's definitely about that journey. Because, again, we could wake up tomorrow and there could be a solution or some kind of revelation on algo birth. You know, there's so many different you know, Skynet. I mean, there's so many we have that moment, right. I highly recommend watching the TV. Silicon Valley because I feel like it's a pretty accurate representation of this nonsense. But no, I I joke partially but I think that we could wake up tomorrow and the whole world could be tipped, much like it was in 2020 on its head. So agility more than anything, you want to hold on to that and just never stop learning. As cheesy as that is, it's absolutely lifelong learning is just absolutely inherently tied to that world of STEM in general, the scientific process being scientifically literate, being data literate, it rips away all of the dogma and things that can can lead to gut checks, feel felt found all that kind of stuff, it has a place, maybe if you're trying to make a gut decision on eating sushi from a gas station, that's where that should be or, you know, meeting someone for the first time and thinking do I want to go to dinner with them? That's a place for intuition and gut check. Right? And also how they treat the waiter is usually the first way to answer that. But I think it for the rest of the world, particularly in business, like leaning into data, being analytically minded, paying attention to data governance, being extremely aware of the characters and the different kind of archetypes you work with cross departmentally. All super useful, and it never it should never really stop. There is no end point where you go Okay, yeah, I know it all. And now I can relax for five years. Because it's ever ever changing state. It's dynamic, it is not static.
Vinay Koshy 51:15
Right? Brandee, if listeners want to find out more or get in touch with him, where would you recommend they head to?
Brandee Sanders 51:23
You can reach out actually, you can reach out to a couple of different ways you could reach out on LinkedIn, it's B ra m d s&t ers, there's a couple of us now, actually. So you'll recognize the colored background, super colorful black round there. Reach out there anytime I love networking, redirecting people to great groups to kind of scale up if you're just starting off, or if you're in a leadership role, some really great groups at the kind of like CEO SVP level where we get together and chat about business. And then also my website, which is Bernie Sanders comm tons of information there, not only just about tech, but about the creative realm and all the other side projects that I work in, super passionate about nonprofits and enabling, you know, first gen folks who aren't at at Harvard and Stanford and MIT who have to kind of grip and claw their way through everything to kind of get that extra mile like I've been there, I've done that. I've taken a nice big fat bite out of government cheese myself in my childhood. So I love I love opening the door and holding it open for people who are behind me so that they can enter this world and not be intimidated by the barrier to entry that quite often is built by those stakeholders. So I kind of like to strip that away, simplify and show that you can come in and to learn these things as well. And everyone has a kind of a right to that education.
Vinay Koshy 52:36
Excellent. Brandee, thanks so much.
Brandee Sanders 52:39
No, thank you. Thank you for having me. I appreciate it.
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Links and resources mentioned
- Check out Brandee’s site
- Brandee’s slide deck – Data Science Process: How to go From Pilot to Pipeline to Drive Growth
Connect with Brandee
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