Laura Kendall, VP of Marketing at MadKudu, On How Cloud Go-To-Market Teams Can Unify Around Revenue Generation

In this episode, our guest is Laura Kendall, VP of Marketing at MadKudu. Laura is a data-driven marketing leader with over a decade of experience in B2B marketing, demand generation, and building cross-functional teams.

Show Notes

On how marketing and sales can work in harmony

"If it's worth having a conversation about, it's worth having with your counterparts in sales." (12:28)

"Marketers should be in pipeline reviews. Sales should be in content meetings. And both should be attending prospect calls – or at least listening to recordings." (14:05)

"The story should never be, well, marketing hit their MQL goal, but sales didn't hit the revenue goal. The company needs to hit the revenue goal. And it's every leader's job – whether you're in sales, marketing, or finance – to help steer the ship in that direction." (14:19)

On making data actionable for go-to-market teams

"Simplicity is key. We've got so many tools to go into. So bringing those insights to sales in a solution that they're using every day – their Salesforce or their email – put it where they're already living, and make it holistic." (17:13)

Full Transcript

Michael Pollack [00:00:09] Hello, everyone, and welcome to Selling in the Cloud, a podcast about the business of cloud sales and marketing, brought to you by Intricately, the authoritative source of digital product adoption, usage, and spend data for cloud sales and marketing teams. I'm Michael Pollack and I'm here with Sarah E. Brown. And we are your co-hosts.

Sarah E. Brown [00:00:38] Michael, it's great to be here with you today.

Michael Pollack [00:00:42] Sarah, It's wonderful to be here with you today.

Sarah E. Brown [00:00:43] In this episode we're speaking with Laura Kendall, the Vice President of Marketing at MadKudu. Really looking forward to chatting with Laura about how cloud revenue teams can make better use of data. Shall we dive in?

Michael Pollack [00:00:55] I'd love that. Laura, thank you for joining us today. I'd love for you to just take a few minutes and kind of introduce yourself and give us a background on you and obviously where you are today. If you could talk a little bit about MadKudu, but it'd be great for you just to kind of introduce yourself to our audience here today.

Laura Kendall [00:01:11] Yeah, happy to be here. And thanks so much, Mike and Sarah, for asking me to join. But a bit about me. I'm a B2B marketer through and through. I grew up in the demand generation and marketing operations functions within marketing. So I definitely see myself as more of a revenue focused marketer than a brand marketer. It's not that there's only two types, but in general terms, I've been in some businesses for the past about seven years and have actually accidentally found myself as one of the first marketers hired to build out a function. And in fact, that has happened three times now. And really quick, a bit about MadKudu, marketing ops intelligence platform that helps B2B marketers like myself and like yourself, Sarah, to really predict, measure and optimize everything that they're doing for all of their marketing efforts and really things like, you know, lead and account scoring, maybe across multiple go-to-market motions and multiple geographies, analyzing what the core ICP is, happy path analysis in general, overall audience segmentation. So some of those things that are inherently challenging for B2B marketers today. We do this by aggregating data across your entire tech and data stack. So that includes your go-to-market tools like your HubSpot, your Salesforce, your Intercoms, Drifts, that sort of thing. That includes your product tools like Segment, Mixpanel, Snowflake, and all of your third party data. So things like ZoomInfo, Bombora, HG Data, Predictly. And what's unique about us is we are built specifically with the B2B marketer in mind. So we bring the power of data science and machine learning and that good mix of predictive modeling to marketers without them needing to write SQL. I don't know about you, Sarah, but I am no expert in programing, nor do I really want to be. So what this means is we no longer have to ask our engineering teams for resources or use up our valuable headcount to hire marketing engineers or spend hundreds of hours trying to make updates to models that have been built in-house. So I'll stop there. But just a really brief, high level overview of what we're doing over here at MadKudu.

Michael Pollack [00:03:27] I'd just chime in with a fun factor that I don't imagine our audience knows. But Sarah knows the MadKudu team from her time supporting folks at TechStars. And I know the MadKudu founding team because we shared space together when we both went through the Salesforce Accelerator program. And so it's a small world around how kind of all these companies in the data space know one another. So interesting trivia for our audience, but I guess maybe I'd ask just one follow up question, which is, is the vision to some extent the MadKudu helps alleviate or perhaps support what data scientists and businesses are doing? So we at Intricately, we provide data to many customers and many of our customers are data scientists. But for some of our smaller customers who don't have all the resources and means to be able to hire and keep data scientists, is the vision for MadKudu that it augments, supports, I won't say replaces that function, but am I characterizing that fairly?

Laura Kendall [00:04:28] Absolutely, yeah. And really allowing those, whether it's the data scientists that are on the marketing team or the marketing team themselves to get back to being creative and spending time experimenting on messaging and channels on campaigns, rather than spending all of that time making very manual tweaks to a model and ensuring that those models are adopted and trusted by both sales and marketing.

Sarah E. Brown [00:04:52] I'm curious, given all of the data sources that you work with, with marketers, what kind of data do you think they struggle with the most? Are you seeing that you're maybe alleviating that pain as you're helping them?

Laura Kendall [00:05:03] Yeah, that's a great question. I think probably the biggest is that second layer that I mentioned, those additional data tools that modern marketers are really starting to think about now, the Snowflakes and the Segments and being able to bring that in and ingest that in a way that is usable to a marketer is not something that I have seen anyone else really crack the code, so it's super exciting.

Michael Pollack [00:05:26] It's interesting in that vein, I'd love to ask so for our audience, the majority of our listeners are in the sales and marketing space in this cloud universe, particularly enterprise sales side. But I'm curious for you to talk a little bit about data in your own business. What do you think about as a marketer? How MadKudu uses data to find the next best mag to the customer, because every business, increasingly, particularly digital ones today are doing some version of ABM, some version of targeting, some version of being sophisticated. Most of them don't have the liberty of just buying billboards and hoping customers show up. So it'd be great for you to talk a little bit about how in your business you utilize data to find your next best customer or your suspect prospect.

Laura Kendall [00:06:11] Yeah, for sure. And I think it's important that we all kind of eat our own dog food or drink our own champagne to some degree. Right. So let me give you a few examples. For one, I mean, I think any time we can incorporate data into our decision making, it's going to improve the bottom line. Right. And so from a revenue perspective, one example is as a company, we started looking at both qualitative quantitative data to fine tune our ICP and overall market segmentation, and started to think about when customers do become paying users of our product, what attributes are more likely to make them stick around longer? So if we can start focusing on companies that are most likely to have a longer customer engagement with us, we can impact our lifetime value. We can increase our average deal size and we can decrease the estimated churn, for example. So if you think about that in the grand scheme of things, that's how we want to impact the full funnel. But we can also do things at a campaign level. So one thing that my team is working on right now is to re-target the best fit leads that are visiting our website. So we all know that only, let's say like 10 percent of website visitors are actually going to engage and convert in some form or fashion. But that 90 percent doesn't mean that they're useless to us. Traditional retargeting means that you're going to go after anybody who hits your website or engages with you and try to with an outbound marketing motion, get them to come back and convert. But it's a pretty expensive and kind of a black box-y, I'll say, effort. And so if we are able to use our predictive model with MadKudu and identify based on their IP address, which of those visitors that did not convert would be a very likely fit or likely to convert, lead or account for our own product, then I feel comfortable increasing the amount of money that I'm going to put behind that campaign. Right. And so I can be more efficient and more comfortable spending the value of marketing dollars that I have and campaigns by incorporating the data that MadKudu provides.

Sarah E. Brown [00:08:25] You sell to cloud providers as some of your target customers. Is that right? Can you talk a little bit more about your MadKudu, who you tend to be valuable to and who you prospect to.

Laura Kendall [00:08:35] Yeah. So for us, as I mentioned, we're really focused on the B2B space right now. And so generally speaking, if you're only getting one hundred leads a month, you're probably not going to see a lot of value in the machine learning and the models. But you probably might see some value in some of the data science pieces and ICP analysis and things like that. But generally speaking, if you're generating about 2,000-3,000 leads a month, you're going to start to see some efficiencies. And so to your point, we work mainly with marketers, growth marketers, VP of marketing and sales leaders and such. And yeah, really in that B2B space. One thing that we are really excited about lately is the awesome work that that we're doing with some of our customers. So things like determining whether sales should actually get involved in removing that unnecessary friction and the buyer journey and really making sure those enterprise sales teams are more efficient when there's a free trial or freemium product offering and then helping marketers leverage product engagement or an app behavior in their marketing campaigns to increase our ally.

Sarah E. Brown [00:09:44] I love that. And I also wonder, do you ever find yourselves being sort of data whisperers and explaining things about data to all of these different teams so they can align with each other? And maybe if you could speak to that alignment that the modern B2B SaaS company is facing?

Laura Kendall [00:09:59] Yeah, I mean, I certainly don't feel qualified to be that data whisperer, but everyone on the team is that's really supporting our customers are that's a great way to put it, is is really holding their hands. And this educational journey that most marketers are actually on when it comes to data science and machine learning, myself included, and helping them figure out how to make sense of it because there's so many ways to misinterpret things. You might assume that because you've got all these products lined up, then you're not seeing paying customers, that sales isn't reaching out to the right people or that there is friction in your buying process. But it could actually be the fact that you have a quality problem, not a quantity problem. I think I might have mixed those up, but I think you get what I mean.

Michael Pollack [00:10:46] We get what you mean. And I think most customers or most businesses, I think today struggle with the quality and the quantity problem in general. Right. That they may have lots of leads, but they're not very good or they may have a small number of good leads. And how do you get those two to be in harmony? I think that is the biggest challenge. I think today one of the things that's happened over the past decade is marketing has tools to do mass scale marketing, like marketing automation products that they didn't have a decade ago. And the challenge with that is how do you focus those tools? Right. How do you ensure that you're actually you're focusing them on the right set of prospects that are worth your time? And so I think that's an enormous challenge. And it's interesting in our business, we spend a lot of time doing more data whispering than I thought we would do around customers using our data to basically step away from really complicated models because we're mapping digital infrastructure. So if you sell a digital thing, we could give you a really simple answer. That may be one ingredient in a complicated cocktail, but it carries a lot of weight. And so it's a longer story for another day and pause that there. But I guess I'd love to ask you taking a step back, because I know it's relevant for our audience. I'd love for you to talk about how you drive sales, marketing alignment at MadKudu specifically. I'm sure for your customers that's its own challenge. But for you as a marketer, how do you take what you're doing and translate it into qualified leads, opportunities for your sales organization? And how do you work in harmony to identify the best prospects and turn them into opportunities?

Laura Kendall [00:12:15] Yeah, really, it sounds super cliche, but working closely together on literally everything. So ensuring that nothing gets decided, nothing monumental, right, like if it's what you're having for lunch, go ahead, make a decision on your own. But if it's anything that's worth having a conversation about, have it with your counterparts in sales. And even if it's just a conversation, it doesn't have to be a full blown meeting. I do think that a lot of times there are far too many meetings, especially now that we're in this whole virtual zone. But whether it's defining what a qualified lead is or when you should be sending an alert for sales to pay attention to something, or what type of sales enablement content to develop or what new tool to consider, that list is a very long list of things that you should always be engaging with your sales team on as a marketer. And at MadKudu, we actually refer to ourselves as one team – we're the go to market team. Do we use the word sales team and marketing team? Yes, but generally speaking, we are sitting under one roof and advice I would give, too, is whomever told us that sales and marketing shouldn't get along – shame on them. Somebody put the idea into our head. We didn't all just come to the realization on our own, but we need to remove that from our memory. It just is clouding our judgment and it's putting a bad taste in our mouth, in my opinion. It's making marketers join sales meetings defensive when they have no reason to be frustrated, annoyed, insert some negative emotion that marketers are probably feeling joining these meetings and probably like why it called the marketing meeting. Why isn't sales joining our meetings to ask to join their meeting? The things that you hear marketers complain about? It boggles my mind where all this came from. But the point about meeting is that you should be in the same ones, like we call it our go to market meeting. No hurt feelings. Marketers should be in pipeline reviews. Sales should probably be in content meetings and vice-versa. Both should be attending prospect calls or at least listening to recordings. And then lastly, and probably the best advice I've gotten is own the number together. And the story should never, never, never be marketing hit their MQL goal, but sales didn't hit the revenue goal. Nobody hits an MQL goal, that's not necessarily an important goal. The company needs to hit the revenue goal. And it's every leader's job, whether you're sales, marketing, finance, are to help steer the ship in that direction.

Michael Pollack [00:14:43] That idea of the challenge, the innate challenge that people talk about sales, marketing, alignment and a boogie man between those two charts. Right – and their failure to connect. I mean, that's a there's a recurring theme that I believe many multibillion dollar businesses have been built around. And I'd argue they're trying to resolve that. They're trying to drive a wedge between that. They're trying to make that more than it is. I'm curious on that comment. Do you find for many customers of MadKudu that you guys are encouraging them to move to this one team model? And do you find that most of them take you up on that? Part of my observation in dealing with data is different organizations. Different teams see it differently. Sales has a different perception of data versus how marketing might. And getting around the same page about what it means and how to use it is a key part of actually making that asset, that strategy, that approach successful. Do you find that many of your customers do something similar or is the challenge to get the sales and marketing team on the same page?

Laura Kendall [00:15:39] I think it's a spectrum and I don't know that there is necessarily a, you know, these types of companies fall here, and these types companies fall here. I think it kind of depends on company culture and things of that nature. But I wouldn't say that people are against it. I think that everyone's trying to move in that direction. And there's some inherent wedges, as you said, that have been potentially put in place and even technology that's been built in a way that hasn't allowed for easy collaboration. And so I truly feel that we're on the cusp of something great and that companies like ourselves are building products and services in a way that is going to bring everything together. Couple that with the rise and movements like product led growth that are seeing marketing leaders and even product leaders own a revenue number, it just brings that empathy to a new level for sales counterparts and bringing that ownership of the bottom line to more people within the organization.

Sarah E. Brown [00:16:42] Yeah, it's interesting. We spoke with Gali Kovacs, who's a cloud revenue leader at NetApp, and she was saying all the different data sources that her team uses. She did mention product data. And I do wonder, you know, something we hear from our customers is how grateful they are that marketing has all of this data that finally they can show to sales. We keep it all in our silo. And I think that's a detriment to the organization. So I'm curious, how do you help people sort of share their data and put it in the best format for other teams to understand and make actionable?

Laura Kendall [00:17:13] Yeah, I think simplicity is key. And as we all know, we've got so many tools to go into. So bringing those insights to sales in a solution that they're using every day, i.e. their Salesforce or their email, it doesn't really matter, but put it where they're already living and make it holistic. So, for example, if we're just alerting sales and saying, hey, this person is on our website right now, you're not giving them the full picture there, there might be historic interactions that would be relevant to service at that same time, or there might be six other leads or contacts that have engaged at that account in the last five days as well. So those one-off alerts aren't as powerful as bringing all the signals together and one place. And so that's really what we aim to do at MadKudu. And it is one thing that we are continuously trying to improve is helping our clients as marketers serve their clients, so to speak, as sales.

Michael Pollack [00:18:10] And when you think about the role of data science and your customers at MadKudu as a service matter expert, as support, or resource around that – is your primary customer, the end seller, the salesperson who's trying to get the deal across the line? Is it the marketer who's trying to understand their top of funnel or middle of funnel? Is it a combination? And do you support both? I'd love for you to talk about how you think about it, because I'm imagining there's somewhat of a slight dislocation between the economic buyer who's buying MadKudu, who believes, and then ultimate end seller, who's got to use this data to score this number. How do you think about that?

Laura Kendall [00:18:48] It's a tough question and you'll probably get a different answer depending on who you ask and when. But the way I see it is, it's really both. I mean, we are catering to the marketing mindset of connecting all of the data and being able to improve campaigns. We are still, to some degree thinking and leads and generating awareness. That's a big part of marketing. And so being able to slice and dice the audiences and be able to provide a more relevant and compelling message, you know, it's just super important in the same breath. So all of that data and those scores are getting passed to sales and they're making decisions based on them as well. So it's not that only marketing is going to care about the number because they can use that to inform their marketing strategy, their campaigns decide what to continue executing, what to maybe discontinue, but sales have to use it as well. So we do need to think about both and we need to think about both when it comes to who we're engaging with in the sales process, but also how and when they might interact with our product. So it is complicated. And as we are continuing to build out our product, it's something we have to think about and make concessions depending on which persona we're going to put more emphasis on.

Michael Pollack [00:20:13] I just have one follow up to that comment there. And I'm curious your thoughts as a marketer who deals with this, with your internal sales team – and something we deal with as a vendor in the data space, that ultimately the data you produce is only as good as the person that's using it? Right. That no matter how great your data is or how great the tool is, if the person is using it, doesn't know how to use it, it doesn't do much. And so there's this inherent innate challenge, I think, between marketers who may have the liberty and the ability to be really thoughtful about data and be able to look at it at a huge scale. And then the individual salesperson who's trying to get a deal across the line or get a prospect excited. I'd love to just ask you, in your opinion, how does the average salesperson take advantage of the amazing leaps that we've had around data science? A lot of times the manifestation for the average salesperson is a score which is kind of devoid of detail. And maybe somebody spent a lot of time thinking about and programing and putting together and obviously organizations like MadKudu to help build. And they're incredibly effective and incredibly logical. What do you say to the average salesperson who says the score doesn't make sense, or I don't understand, or it doesn't look right, or I don't trust it? We hear some version of that. I'm imagining you do as well. And I'd love to hear how you think about that.

Laura Kendall [00:21:30] Yeah, and we love skeptics. Like I think it's great that there are questions that are coming. But you're also right that there's a lot that goes into that for – that concept of black box that I brought up earlier. I'm going to I'm going to go back to that. So in my experience, most A.I.-based marketing intelligent solutions end up having their predictive models referred to as something like a black box. And I can certainly speak to that firsthand and being told we can't really show you what's going on back there. It's our proprietary A.I. algorithm or something along those lines. It's not very customer-centric, in my opinion. And it's also difficult as a marketing leader to justify the expense of a tool like that to your CFO. But outside of that, if sales or marketing doesn't know the why or the what behind the model or the score, there's not going to be a whole lot of trust or adoption of it, period. And so a major way that we differentiate in this market is by bringing that power of data science into the hands of sales and marketing. And so if there are tweaks that need to be made to the model or they're just curious why this lead scored this way. It seems odd to me, but I'd love to get more information. Like we can go in and show exactly why something was scored a particular way, whether that is behavioral based or firmographic, technographic, demographic-based. And if there are situations which commonly come up and be to be like maybe expanding to a new market or testing out a new channel or supporting a new go to market motion, overrides can easily be made. And in fact, they don't need to engage with mad to do it all. We, like I mentioned, don't require marketers to be able to write SQL or know a programing language in order to take advantage of making those the adjustments real to.

Sarah E. Brown [00:23:17] That's awesome. For folks who are getting started and becoming more data driven, becoming more of a go to market team unified around revenue. What advice would you give them to folks who are listening to really get started on the journey?

Laura Kendall [00:23:29] I think that the alignment in conversation between the revenue-generating teams is key. And I also think that while data is super important, it's never perfect. And especially in a startup, you know, it's better to make the hard decisions and move quickly. And also, there's nothing worse than that getting stuck in analysis paralysis, but starting with data is huge.

Sarah E. Brown [00:23:54] And for folks who want to learn more about you and your work, where should we direct them to?

Laura Kendall [00:23:58] So, I mean, learning more about MadKudu, we'd love to see you on You can hang out and interact there, and see the real time scoring work. But you can also find me on LinkedIn. Would love to chat.

Michael Pollack [00:24:12] Laura, this was awesome. Thank you.

Laura Kendall [00:24:14] Yes, awesome. Thank you so much for having me.

Sarah E. Brown [00:24:16] That's it for us. This episode may be over, but we can continue the conversation on Twitter with the hashtag #SellingInTheCloud. On Twitter, I'm @SEBMarketing.

Michael Pollack [00:24:25] And I'm @MRPollack.

Sarah E. Brown [00:24:27] Thank you to everyone for joining us for this episode of Selling in the Cloud, brought to you by Intricately the authoritative source of digital product adoption, usage, and spend data for cloud sales and marketing teams. If you like the show, head on over to iTunes or wherever you listen to podcasts, and please give us a review. We appreciate it. Until next time.

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