Nick Ezzo of Host Analytics- The Marketing Battleship, Personalization vs. Scale and SaaS for the CFO

Nick is a veteran marketing strategist with extensive technology experience (15+ years) leading enterprise and product marketing at large and small companies. Career focused on building and accelerating market leadership, launching innovative products with clear messaging, driving global demand, arming sales teams, and driving bottom-line growth

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In this episode Nick explains:

  • Why opportunities are more critical than MQLs
  • Getting agile enough to face competitors 3, 5 or even 10x your size
  • Why offline marketing is outperforming online
  • How to properly budget for tools and programs

Read the Transcript

Sales and Marketing Automation Tools mentioned in this episode of Stack and Flow:

Demandbase, Spiderbook, ZenIQ, Engagio, Outreach, ZoomInfo, DiscoverOrg, RainKing, InsideView, LeadGnome,, Leadspace, Salesforce, Uberflip, HubSpot, PressPage, Marketo , LeanData, Bizible, Heap, SimplyDIRECT, Captora, Bombora

John J. Wall: Hello and welcome to Stack & Flow. I’m John Wall.

Sean Zinsmeister: And I’m Sean Zinsmeister.

John: And today’s guest is Nick Ezzo. He’s the vice president of Demand Generation at Host Analytics. Nick, thanks for joining us.

Nick Ezzo: Hey. It’s good to be here.

John: All right. And Sean Zinsmeister, my partner in crime here. What’s going on this week, Sean?

Sean: There was an article that came out, actually this was from last December, but I grabbed it because I thought it was an interesting topic of discussion to sort of get us warmed up here. That’s the WTF Is AI? That was in TechCrunch. And what I really liked about this particular article is that it attacks really the definition of intelligence. I think that this is something that we don’t always ground ourselves in when we think about what is AI. And then we sort of get lost in the buzz words, a little bit.

The study that he sites is something called The Chinese Room Experiment, which is really interesting, from John Searle. You can definitely look it up. We’ll have a link of that in the show notes. You can get some more details. But it’s essentially saying that if you have a room full of people, with a Chinese dictionary, it’s possible to sort of do that character recognition given just a whole bunch of people doing it by hand, transfer it to another room, and have it be translated perfectly. Now that’s a really rough explanation of how that experiment works, but it really is understanding, "Okay, the question is of computation." Indeed we can be able to detect maybe the patterns, in this case it’s looking at the stylings of Chinese lettering and characters, to be able to translate that into English phrases and expressions. And maybe even make it look somewhat conversational. But is there any understanding actually happening here?

I want to loop Nick in on this kind of idea, because AI is obviously a big hype term in the world of sales and marketing. But when you’re thinking about intelligence and this idea around AI, how do you sort of start to demystify it in your head? Is there reasonable expectations? Do you divide up in strong and weak? How do you sort of break it down when you think about technology?

Nick: Yeah. Good question. And AI is one of these phrases that’s on everybody’s lips. You go to marketing conferences, and I go to quite a few of them, every other presentation is talking about AI. It’s talked about a lot, but in practice, I don’t think it’s being implemented a lot. So I think we’re still in the talking about it phase, and we’re not in the doing it phase.

I know Demandbase, for example, has some things in the works where they’re going to be implementing AI into their programs and their solutions, primarily through their Spiderbook acquisition. I work with another firm called ZenIQ. Their promise is to do, I would call it sophisticated marketing orchestration. Where they’re looking at your data, and they’re looking at the campaign responses. And the hope is at one point, they’ll be able to tell you, based on the campaigns you’ve run in the past, here are the types of campaigns you should front-run in the future, and to which audiences, and to which messages.

To me, the promise is that it makes good marketers great, and unfortunately it makes bad marketers good. I guess it’s more promise than practice at this point in time.

Sean: Let me ask you, so when you sort of start to dissect this. And before we start to get into your stack that you have at hand, how do you think that businesses should be thinking about this question of, "Do I have an AI problem?" How do you sort of go about dissecting, "Hey, these are the problems," and matching it with some of these solutions. Even if the actual technology and innovation, like some of the ones you described are at its earliest stage, how do you go about sort of helping to find that problem?

Nick: Yeah. There are certain things that are out there today. There are certain problems that can be solved by, not what I would call the classic definition of AI, which is where the computer is technically as smart or smarter than the human. But in things like predictive, and ideal customer profile type of technology, where it’s a very simple mouse trap. You feed in all your customers, the system kind of computes what your ideal customer profile looks like, and then spits that back out. And then, again, like robotic mouse trap, it scores all of your leads against your ideal customer profile, and I wouldn’t call that AI. I don’t even know if I’d call that predictive, but it’s using kind of an intelligent approach to a large data set to make your marketing team smarter and to draw tightly defined targets. Does that make sense?

Sean: It does. And actually, you brought up something around that sort of assisting mechanism. I think the one thing that I’m really fascinated about is this idea of human-assisted AI, right? Where, even the self-driving cars that we see around Mountain View, have pilots in them. Hands on the joystick, if you will. Even the auto self-driving cars have a driver in the well. And I’ve heard a lot of these analogies around, "Well, we’re not building pure automation, it’s more like Iron Man." To have that sort of comic book reference there. Is it the environment that’s driven us to sort of create this new level of tools in order to help sales and marketers do their jobs better? Or is this sort of just a natural evolution from other means, perhaps?

Nick: I think it’s … we’ve gotten to this point, because we’ve been hearing for years that personalization is key. So crafting a personalized email, doing a personalized phone call, for every single prospect is the most important thing. Spray and pray is dead. Personalization is king.

So we’ve been told that personalization is important. But then now we also have a scale issue. And having a conversation with somebody yesterday around ABM at scale. We could talk about that more later. I think the two things are contradictory. One is a focused approach. And one is a not focused approach. How do you craft personalized emails on larger scale? Where that’s where you have this machine-assisted technology. And companies like Spiderbook, and Engagio, and Outreach and others, become that kind of Iron Man suit that allow at least the SDR team to kind of fake personalization, or to do personalization a lot quicker, rather than go out on LinkedIn and find out, what does this guy do? Where does he show up? What are her interests? And what is her background? The machine feeds the SDR the stuff automatically nowadays, so that they can do a more personalized approach without having to do all the legwork.

John: Dude, that’s so funny. I have to jump in on that because it just kills me. ABM at scale. That’s like the scumbag alert goes off when I hear that. ABM at scale. What is ABM at scale?

Nick: Exactly.

John: Let’s step back a bit, Nick. Tell us a bit about what you do and what kind of staff are you working with and give us kind of the elevator pitch for where you’re at.

Nick: Okay. Cool. So in a nutshell, the company I work for, Host Analytics, we sell a SAS application to the office of the CFO. Our buyer is a finance buyer, and it’s people that do planning, close and reporting, all the finance functions. So we’re a SAS application. Our job in Demand Gen is to make the phone ring. Get people to fill out our forms. Interact with us. So, I don’t have 10 people on my team. My Demand Gen team is very small. Right now it’s four people. The marketing team here is 12 people. And I’m competing, by the way, with companies that have twice or three times, or in the case of Oracle or IBM or SAP, 10 times the people that I have.

The way I think about technology is it allows me to do more with less. A hundred years ago, if you wanted to operate a sailing ship, like a clipper or a schooner, you’d need literally, a hundred men. You’d need guys to rig the sails, you’d need men to run the cannons, you’d need somebody up in the crow’s nest navigating, and of course you’d need the captain to pilot and command the ship.

I read recently that today’s modern battle cruisers require four people. You need one person to run propulsion, one person to run munitions, somebody on navigation, and of course you have to a captain to pilot and command the ship. So my Demand Gen team, I consider to be a battleship. We automate what we can automate. We use technology. We don’t have a hundred people to do work, so we have to use technology to make our small four person team act like 10, or 20 person team. Does that make sense?

Sean: It makes perfect sense, actually. And I’m in a big believer of this being sort of an evolutionary model, as well. Where I was actually talking to a marketing executive just the other day, and we were talking about the use of predicting analytics and AI in his organization, and they don’t have such a huge top of funnel, so how should he be looking at things. And I asked him. I said, "Well how many sales development reps do you currently have?" And he gave me the number, and I said, "Well do you think that that may be one too many? Are you sure that you’re getting the maximum amount of efficiencies there?" Because I think that we’re so tempted to throw humans at the problem, but it’s not always the most efficient way to run our business. And I think that great analogy that you draw of the battleship is perfect in sort of helping to visualize how you can help small teams punch above their weight. Because I think there are a lot of companies out there that are looking up at the larger incumbent, perhaps, in some of the spaces, and they need to be able to lend technology for what they’re doing.

And that kind of looks at the strategy. When you look at your own stack, Nick, how do you divide up things? How do you go about organizing the pieces that you know to need to plug into those spaces? Do you look at, "I need this for analytics and identification." Or do you map it to the funnel like other practices? How do you sort of go about organizing that?

Nick: I have something that looks like a layered cake. And I give this presentation a number of times. At the very bottom is my data providers. So data sources, because it always starts with data, right? If you’re going to do any kind of marketing, you have to know who your audience is. I used to say that we have every data source known to man, but I think we’ve gotten rid of a few of them. But in nutshell, at the bottom layer, we have ZoomInfo, DiscoverOrg, RainKing, InsideView, LeadGnome. We used to have, we used to have Leadspace. We’ve got a few other things that are pulling things in, but I consider the data to be the foundational level. How do we get the right contacts?

Right above that I have a marketing and automation platform, which would be Marketo. And above that would be our CRM, which would be Salesforce. And then on top of that layer, I would call the next layer the engagement layer. And that’s all the different tools that we have in which to engage with our audience. And some of those are web tools, like of course, our website is on WordPress. But we also have a resource center which is powered by Uberflip. And our blog, which is powered by HubSpot. We have demand-based personalization. We’ve got a PR application called PressPage. We have all these different ways of interacting. And of course we have Marketo creating landing pages, and we use them for email marketing. So there’s all these different types of things that we use for technology to engage our audience.

And then the final layer, which is at the very top, is what i would call the attribution layer. In that section of a stack, we have applications like LeanData which help us to lead to account matching, and opportunity to campaign attribution. Of course we use Google Analytics, like everybody else in the world, and then finally we use Bizible, which it helps us draw a straight line between our paid search campaigns and opportunities. Like it literally opened up an opportunity and find out what was the search query that brought this person in through our paid search. What was the ad that they saw. And run reports around my paid search and tie those to actual pipeline dollars.

We’re playing around with another application right now called Heap Analytics, which is maybe a replacement for Google Analytics. It’s kind of a … they basically hoover in all of your website data, and allow you to do some very sophisticated paths around your website to figure out where your visitors are getting bottlenecked, and where they need to be unstuck. But that in a nutshell is kind of our martech staff. We have a few other random vendors out there that are little point solutions that do little things here and there. But those are kind of the big moving pieces.

John: That’s great. Now how about the actual flow. Building the funnel, or the demand waterfall, or whatever you want to call it. What ways are you getting people in the front door and how are you qualifying them and pushing them along. What’s the landscape look like there?

Nick: In our world, we care about MQLs. We only care about MQLs to the degree if they’re a leading indicator for opportunities. Here in our marketing team, at Demand Gen team, we align around the exact same goal as the sales team, which is stage two opportunities, or sales accepted leads. And that’s the trend. All the conferences I’ve been to this year, that’s the trend. Everybody’s saying, "MQL is not the right thing to be focused on. Opportunity is the right thing to be focused on." So with that in mind, we look at campaigns that drive opportunities. And the ones that have the fastest opportunity acceleration, and the ones that result in the highest win rate.

We do a lot of online and off-line marketing. So we do pretty much the same stuff that everybody else does. We have our website, and we have our paid search. So we have organic and paid. We have social, both organic and paid social, through LinkedIn sponsored updates. We do a lot of retargeting. We do content syndication. We do account based advertising, also through Demandbase. We do email marketing. We have a number of nurture streams that are persona based. We do survey marketing. And of course we do field events. So large seminars and conferences, trade shows. But also small breakfast, lunch, dinner type engagements, where we do a thought leadership piece, and serve bacon and eggs and that kind of thing. So we have a lot of different ways that we bring people in the door.

Once they get in the door, then we apply our definition of MQL. We qualify them. And for that we use Infer. In our world, an Infer A, B, or C lead gets counted as qualified, and a D lead is not. Just so you know, about 50 percent of what I generate are D leads, and we have to kind of throw them in the trash can. I’m simplifying here a bit. But the D leads are things like students, professors, self-employed, unemployed, housewife, truck driver, you know, those people. And occasionally, the SDRs will go through the trash can. They’ll dumpster dive and they’ll find some gold nuggets in the D leads. But for the most part, we want to prioritize the As, Bs, and Cs for them to follow up quickest and with the most high-touch outreach. Then we apply a few other criteria to our MQL. It has to be in the U.S. or Canada. We have to have a real employee count. Like we found it in some database they’re a real company. But that’s kind of the flow.

SDRs are the first line of defense here. Our STR team is here in Redwood City. They touch every single thing that comes in. And their goal is to set up stage two opportunities for the sales reps.

Sean: And I know that we’re going to have to circle back now to the ABM mania question. The ABM at scale contradiction. Are you guys looking at account based marketing as sort of a part of your strategy? You have all these engagement channels that you guys are looking at to drive people into the top and then you’re sort of using lead to account matching and predictive analytics to help qualify. Where does the account based strategies sort of fit in to your go to market plan?

Nick: So ABM, the only thing I hear as often as ABM is ABM journey. Like, "Where are you on your ABM journey?" So I’ll tell you about our ABM journey. We’re on the journey.

We started about a year and a half ago. Summer of 2015. So really, almost two years ago now. We work with our enterprise team, because what we’re solving for was, we’ve got these different tiers of sales reps, enterprise is the largest accounts, and therefore the most lucrative accounts. How do we give them more love? We started with a focused list. We used a couple of technologies to identify which accounts we should be going after on that list. So today, we have an account selection criteria. We have an account selection model. And once we came up with that list for our enterprise team, we threw a number of really high-touch programs at those accounts, including survey marketing, swag, account based advertising. They got a lot more outreach. We made sure that we had the right three to five contacts in every single account so that we could do the right amount of outreach both electronically and human contact.

We’ve been doing it for about a year and a half, almost two years, and we’ve seen a lot of results. Our win rate in our ABM list is much higher than our win rate overall. And the deals are larger, and they move a little bit quicker.

John: That’s funny. I had never even thought about just looking at your ABM in terms of win rate. That’s just a great thumbnail sketch that saves you hours of digging through stuff. Thanks for throwing that out there. I should have kicked that around earlier.

You just mentioned swag, and you also mentioned off-line. What kind of stuff are you doing off-line that’s actually working for you?

Nick: Actually the off-line programs outperform the online programs every time. And you know, what’s old is new again. Breakfast seminars. Those are working. If you sit across from somebody and have your bacon and egg burrito and learn about finance transformation, and you shake the sales rep hand and say, "I’d love to see a demo of your product." That’s pure gold. You can’t get that type of interaction through email. So the off-line stuff like breakfast, lunch, and dinners, and field events in a city near you, those work. They always have worked. It’s hard to execute a lot of them, so it’s hard to scale that one up. But the conversion rate on those is quite high.

Survey marketing is another one. It’s like, what’s old is new again, right? We just give a list of folks to a firm on the East Coast called SimplyDIRECT. They give us a hundred campaign responses. The people fill out six questions that indicate their paying point. These are director level and above. In exchange for filling out the survey, they get the survey results plus a nice Columbia fleece. We get awesome data from our survey program. People don’t think about that. Surveys have been around for years.

And then swag. Our CEO is a big believer in swag, and he says that there’s no more valuable real estate in the world than somebody’s desk. If you have a mug, and a mouse pad, and a battery charger, and everything on the desk, one day when they need what you have, they will somehow get the idea to call you. Hopefully we’re putting the phone numbers on our mugs, but swag is one of those things. I hear anecdotal evidence from the SDR team all the time about the power of swag. And it’s things like, "Hey, that guy was eluding me for five months. I was chasing this guy for five months, I sent him a mug, and he called me back." It’s like, "Yeah, it works." Why does it work? It plays on this behavioral trick, or how we’re all wired, of reciprocation. I do something for you, you feel like you need to do something for me. We have this unresolved feeling, and we want to erase all of it. And that’s why swag works. It doesn’t work all the time. Some people just take the mug and say, "Piss off. I’m not going to call you back." But in a percentage of time, people will take the gift and then they’ll call you back because they feel a little bit guilty about it. Hey, whatever it takes, right? We gave them a nice mug.

Sean: Exactly. It’s the Robert Cialdini sort of reciprocation concept.

John: (Laughing) I was just going to say Cialdini. Good for you.

Sean: Yeah. And one point to me. (Laughing) It shows if you ever look at my bookshelf it’s strewn with those types of marketing thought leaders. You have got to get your plugs in there.

Well, Nick. The other thing that’s interesting is like, when you look at all this stuff from a budget portfolio, do you have a best practice, or maybe when you pass advice on to other go to market leaders, how do you think about, "Okay, this percentage of my budget we want to allocate towards technology, maybe some proven, some innovative. This is going to off-line market for this." Because obviously, the cool thing about off-line is that yes, it’s new again. It’s novel, but it can also be quite expensive for some teams as well. And there’s a risk factor in that. How do you build that diversified budget portfolio to sort of drive for success.

Nick: In my world, I would say, and I’m going to estimate here, probably 25 percent of our marketing budget is around tools. So SAS applications, and that includes things like Marketo, Captora. These are not insignificant expenses. So probably 25 percent of my budget is tech. Which is okay, because remember I’m building a battleship. If I threw some of that money back in the pool, I would not be able to hire very many people for that. We always have a part of our budget that is dedicated to tools. We also have a part of our budget that’s dedicated to programs like webinars, and content syndication and other things. Field events. We have a part of our budget that’s dedicated to online marketing. So paid search, SEO projects, paid social. And so those are kind of like the three big buckets that we have.

When we evaluate tech, I want to make sure that we are maximizing the tech. That we don’t just have a bunch of toys lying around, because we like to play with toys. I always challenge our marketing ops people to get the most value out of these tools.

The other thing I do, which at the risk of giving away one of my trade secrets here, is I do try out things that are new. If they fill a need, if they solve a real problem, I’m willing to give him shot. And I have no problem being an early adopter. Some people are risk averse so they can’t do it, but I get a high threshold for risk and I know the right questions to ask. Sometimes I’ll lead with, during the budgetary phase, where we’re negotiating, "Hey I’m not going to be a fender basher here. I’m going to take the opposite approach. I will be your strongest advocate if this thing works. I’ll do a webinar with you, put me on your website, your video, take my quote, take me to your customer event. If I believe in what you say, I will be your strongest advocate." And I know … you know, "Put my quote in your press release."

I know as a marketer, one of the hardest things to do, if not the hardest thing to do, is to get people to say good things about your product. So I throw that out there. And I lead with that. And I point them out to other websites. You can go on the web and find me on five different websites. But I always ask, "What can you do for me for a discount?" And by the way, they’ll meet you halfway, man. They will … if you offer to be an advocate, they will drop their price most of the time.

Sean: That’s an excellent best practice. And actually, you touched on something around sort of the technology evaluation, and how you’re sort of measuring value with so many different types of tools. Obviously, the outputs are all going to be different for some of them. We’ve talked to marketing operations leaders, for example, like Ray Miller, where he’ll do almost like little internal surveys to try to ferret some of the under performers. What do you, from a pruning strategy, when it’s time to sort of look at things, and sort of be like, "Okay, either this lacks in ownership and we can’t maximize value," or, "It’s just not driving the results." Do you have, sort of, a measured approach, or do you have a strategy for how you’re looking at that? Do you do a quarterly? Is it sort of a monthly? What’s your thought there?

Nick: Actually, are you asking about campaigns or are you asking about technology. Because I can answer it differently depending on if we’re talking about marketing-

Sean: Actually about technology itself. Just the stack and the different pieces.

Nick: Yeah. We typically only sign one year agreements with every vendor, because I think it’s important to reevaluate your martech every year. If it’s a no-brainer, like a piece that you’re not going to change up very often, maybe you want to sign a multi-year agreement, but I evaluate it every single year. Is it working? Is it not working? We got the squeeze put on us a little bit by our finance team at the end of last year. Like, "Hey, you need to bring your usage of SAS applications down." And because of that, a few of them didn’t make the cut. Primarily ones that were redundant to other things. Like if you have a data provider and you have another data provider, there’s probably some overlap. Figure out which one is providing better data at the right price and then jettison the other one.

That’s what we had to do. There are a few where we try out an application, we know it’s experimental. We’ll have a set of goals that we want to achieve. And either we achieve the goals or we don’t. Sometimes we do decide, "Hey look, this was a great idea. Either we didn’t invest enough time in it, or the product’s not fully baked yet, let’s come back and revisit it when it makes sense to." So we’ll occasionally prune things from the tech stack with that methodology in mind.

John: Okay, great. How about for future tech? What has come out recently that you want to explore? What kind of things have you got on your list to check out for the coming year.

Nick: We started the conversation by talking about AI. And I know it’s a broad term. It’s overused. But it’s one of those that I’m hopeful for that at some point in the future I can get the machine to tell me what it thinks I should do. Because here, I could ask five different people and we can all look at the data and try to interpret what to do, but it takes a lot effort, right? It takes a lot of analysis. I would like, really just thinking of those 60s movies, to press the button and the computer lights flash and all of a sudden a ticker tape comes out and I read that and say, "Okay, I need to run five more campaigns." That’s my goal.

The other thing is, I want to start make better use of intent data. We’re using intent data from Bombora, through one of our other providers. So we get an attempt run once a quarter. But that actually has a lot of promise for us, because we have our fit model from Infer and Spiderbook so we know which accounts we should go after. I have two different vendors who have created a fit model, and the overlap of those two vendors is kind of the beginning of my ABM list. What I want to do in 2017 is overlay some intent data to find out not only if there’s a fit, but who’s in market right now and who’s surging on terms that I care about. That will be the Holy Grail. I think that will improve our conversion rates.

Sean: All right. That sounds great, Nick. How about if you want to learn more about you or Host Analytics, what’s the best way to get in touch?

Nick: Of course, I’ll give the plug to my company first. You can go there if you’re a finance person and you want to learn about that. Find me at LinkedIn, or you can go to If you go to, you’ll see that blog posting of my analogy to turn your marketing sailing ship into a battleship. And it’s got a screenshot of my marktech stack. Feel free to connect to me on Twitter and LinkedIn. @nickaezzo is my Twitter, and then LinkedIn is just nezzo, or just find me. There’s not very many of us.

John: That sounds good. And Sean, how about for you? What’s going on? Anything we should be checking on the web from you recently?

Sean: I have a couple of articles coming out. Keeping the writing desk very fresh was sort of the newest ideas I have around AI. I’ve started a new series, actually, newer publication called I have a kind of an AI 101 that’s really breaking down some of the really basic concepts … getting into the weeds a little bit around predictive analytics, and actually starting to talk about data acquisition, and then sort of modeling techniques. Like what is the preparation look like for data? Sort of doing it bit by bit by bit, so it’s this like really snackable for marketers and sales people alike, so they don’t feel like they get overwhelmed. Be on the look out for that from me, but everything else you can follow me on Twitter @szinsmeister, Google Sean Zinsmeister, or just find me on LinkedIn. Those are the best spots.

John: Okay. That’ll do it for this week. You can find more from over at But thanks for listening, and we’ll see you in the stacks.

John Wall

John Wall

John J. Wall speaks, writes and practices at the intersection of marketing, sales, and technology. He is the producer of Marketing Over Coffee, a weekly audio program that discusses marketing and technology with his co-host Christopher S. Penn, and has been featured on iTunes.

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