In this episode we welcome Niel Hildebrand III, Director of Marketing Operations and Analytics at AppDynamics. You can find him on twitter @NFH3. Please tweet us with comments and requests @TINT with #SocialStudies. Check out our 1st Episode: Social Media and Customer Experience, our 2nd Episode: Why People Suck at Content Marketing, and our 3rd Episode: Making Your Users Your Story
Facilitating The Marketing to Sales Handoff
Nathan Zaru: Hello and welcome to the next episode of the Social Studies podcast. Today our guest is Niel Hildebrand III, Director of Marketing Operations and Analytics at AppDynamics. I hope you enjoy the episode as much as I did. Hey Niel.
Niel Hildebrand: Nathan.
Nathan Zaru: Welcome to the show man.
Niel Hildebrand: Thanks.
Nathan Zaru: Thanks so much for coming by. You know we had another guest on this podcast, Kevin Goldberg. Just so the listeners know, I met Niel recently, and Kevin couldn’t tell me enough great things about you Niel, so I hope you’re ready to flex all your muscles.
Niel Hildebrand: Very flattering.
Nathan Zaru: Absolutely. Niel you’re at AppDynamics and you’ve been really involved with marketing analytics and operations for awhile now. Do you want to give us a quick background as to what you’re up to?
Niel Hildebrand: Sure. Yeah, I’ve been at AppDynamics for a year. I founded their marketing operations analytics function. Prior to that I was at Box. Did the same thing founding their marketing operations analytics function. I have a background in supply chain operations analytics at Walmart.com.
Nathan Zaru: You were an engineer of sorts, right?
Niel Hildebrand: Yeah, my undergrad in optical engineering, and then an MBA. I have a weird mix of supply chain operations, engineering, and business.
Nathan Zaru: You went from engineering to marketing. What made you make that switch?
Niel Hildebrand: Being at Walmart.com and starting their operations analytics function, I gained experience in reducing costs and making things more efficient in the physical world, but I really had an interest in getting more to the core of growing a business and optimizing revenue. It was a match made in heaven this new role in the industry of marketing operations. Being able to take this operational discipline and mindset and apply it being disciplined about growing revenue.
Nathan Zaru: I think the listeners already know I’m really into analytics and metrics. Data is great, but without insight it’s useless, so making insight from data and giving marketers data insight period is really important to me, and that’s what you do. Can you give us a real quick high level overview of what your day=to-day is like?
Niel Hildebrand: A lot of my time is spent trying to get the business users, so maybe the head of digital marketing or the head of marketing programs to articulate their needs, and what they would need in order to make better decisions about success in their parts of the business. Then translating that into instrumenting those parts of the business. Understanding more about the customer experience and life cycle, and then translating that into a path to revenue, creating targets, and then helping them understand how to optimize for that.
Nathan Zaru: Great. Great. Today you’re going to talk to us about the marketing funnel. I think specifically you mentioned you want to talk about facilitating the marketing to sales hand off.
Niel Hildebrand: Yeah. Many of you may know SiriusDecisions is pretty famous for this, Demand Waterfall.
Nathan Zaru: Demand Waterfall.
Niel Hildebrand: They have this concept of MQL, and SAL, and SQL to create some standardization around how a prospect may go from being just a prospect to somebody who’s actually closer to being a potential to buy.
Nathan Zaru: Can you give us real quick definitions of what MQL, SAL, and SQL are?
Niel Hildebrand: MQL stands for marketing qualified lead, so that’s marketing through the automated processes, making a determination that somebody may be ready to buy. SAL stands for sales accepted lead, so that’s a salesperson agreeing that it’s worth following up to call somebody or email them and make that determination. SQL is the sales person then agreeing that yes, this person probably has the potential to buy, creating an opportunity possibly in your sales automation system, and then moving the deal forward there.
Nathan Zaru: What’s this funnel all about?
Niel Hildebrand: The key in this funnel is both in an organization having clear definitions around what an MQL is. I define that in words, but every organization has a different understanding of when they think a prospect may be worth following up from a sales person point-of-view. Then what you need to do is in the system have some way to measure that. If you’re a marketer and do you drive people to a bunch of white papers and pieces of content? Not all those are equal.
Some piece of content that you drive a prospect to may be so high level that it’s not worth the sales person to follow up with. You want to know that they read that piece of content, but not require a sales person to make that determination of whether there’s an opportunity. However, they may download another piece of content like an analyst report or some eBook that you may have created, and that may indicate a high enough level of interest that you then want the sales person to follow up and understand whether they can create that dialog with a prospect to then get that first meeting and make an opportunity for you to …
Nathan Zaru: Is this the classic situation of marketing team sending sales team leads they don’t like and vice versa?
Niel Hildebrand: This helps to solve that.
Nathan Zaru: Solve that, okay.
Niel Hildebrand: This whole conversation where the classic situation is a sales person says, “You’re not sending me quality enough leads.” The marketing person says, “You’re not following up with my leads appropriately.”
Nathan Zaru: Right.
Niel Hildebrand: The only way to really solve that is to have an agreed upon objective way to build a measure the efficacy of those leads flowing through the funnel. For example, both AppDynamics and at Box we use a technology called Full Circle CRM, and they in Salesforce natively help us to measure that. When something comes out as an MQL, ultimately our ask is for the sales person to then create an opportunity, or explicitly tell us it’s not worth an opportunity yet, and give us a reason for that. Once you get that in aggregate, then you can slice and dice the data a bunch of different ways by campaign, by rep, by region, what have you, to then understand the trends within that.
Then you can understand when a sales person says, “Hey Niel, event leads are crap.” I can actually look in the system, and say, “Actually I kind of agree with you.” It turns out when they don’t come by our booth, total crap. This is a real example, they only convert at 0.8%, but if they come by our booth and we’ve deemed them, we’ve marked them as being a hot lead, it turns out they convert at 6%. Perhaps when they don’t come by our booth, we don’t MQL them yet. We put them into a nurturer and we try to warm them up a bit more. When they come by our booth, you guys should still continue to follow up. That was the conversation that in the past would have devolved into just subjectiveness, but now we have an objective way to answer those questions.
Nathan Zaru: A lot of this depends on your definitions of MQL, SAL, and SQL. How do you come about these definitions? Who decides that? Does the CEO just dictate it one day? I can’t imagine the marketing team can do this in a vacuum without the sales team and vice versa. Right?
Niel Hildebrand: Yeah, that’s a great point and great question. That was basically the first thing I did when I joined AppDynamics. Well, second thing I did next to getting Full Circle as a company to sign them on. Spend basically three months road showing with all the sales team and having that conversation. You need to have something that you can, you’re right, have a mutually agreed upon definition that you can go to. I need, at the end of the day, to put some criteria in the system that will determine whether something’s MQL or not based on objective data driven criteria. You need to be able to get down to that level, otherwise if I can’t go to it, then we haven’t agreed upon anything.
Nathan Zaru: Should a marketer be optimizing for MQLs, or his or her job not done until it’s an SQL?
Niel Hildebrand: That’s actually a fantastic question because MQLs can be gamed. Where companies might fail is trying to just optimize for leads and for MQLs because what can happen is the head of digital marketing can easily go and just buy a bunch of names, or dump a bunch of money into AdWords and get a bunch of people to come in. It may be all total crap, and that’s not going to help anybody because you’re going to overwhelm your sales team and you’re not going to get towards revenue what you want.
Really what you want to be doing is optimizing for SQLs. What you’re trying to do is send in the highest quality leads that you can to get the most opportunities that you can help create. Once it becomes an opportunity, it’s really then incumbent upon the sales person. That becomes more of a sales job to then go and get sales engineers involved, and try to build the champion internally, and do all the things that you pay the sales guys to do. The more marketing can help create those first meetings and get those opportunities by bringing in better MQLs, the better that relationship will be.
Nathan Zaru: In this funnel, there’s MQL, SAL, SQL, I don’t want to get caught up on definitions, but …
Niel Hildebrand: Yeah.
Nathan Zaru: … how versatile is this funnel for different companies of different sizes and different sales programs?
Niel Hildebrand: Those general definitions I think apply to every type of company. If you look at the SiriusDecisions funnel, the people who originated this, they actually got way crazy complicated. If you look at the new funnel they have, it’s all over the place and over-metriced in my mind. I think if you keep it simple and you have this MQL, SAL, SQL as a general framework, almost any company because it’s not that complicated. You’re basically saying bring in leads that marketing think are qualified, and then the sales person will help you understand whether those are actually qualified. It’s not rocket science.
Nathan Zaru: I read a really interesting tweet yesterday. I forget, it was from some tech business leader, something like, “Sales people have sales quotas, marketing people should have lead quotas.”
Niel Hildebrand: Yeah, that’s total crap.
Nathan Zaru: What do you think?
Niel Hildebrand: I totally disagree with that.
Nathan Zaru: Interesting. Tell me about that.
Niel Hildebrand: What we’ve come to at AppDynamics, and we’re all trying to get revenue, both sales and marketing we’re working towards revenue.
Nathan Zaru: Sure.
Niel Hildebrand: There’s general disagreement about that. Right?
Nathan Zaru: Mm-hmm (affirmative).
Niel Hildebrand: We’re trying to optimize revenue for the company, but we play different roles in trying to optimize that. One of those roles that marketing helps is to make it so that sales people have to do less out-bounding, but what we’re really trying to do in marketing is , “Hey we’re going to help you create a certain amount of your opportunities, and we’re going to do that by creating a certain amount of MQLs, but ultimately we’re helping you create a certain amount of opportunities.” That’s the sales person language. Again, it goes back to it’s easy to game at leads, it’s harder to game trying to get those first meetings because you really need to collaborate with sales people in order to get to that nice happy meeting for SQLs.
Nathan Zaru: You mentioned out-bounding, which is relevant here. If the sales person goes out-bound and then they qualify someone, by definition I think it’s the top-ish part of the funnel. Would that be a MQL generated by a sales person?
Niel Hildebrand: In effect, and actually what we’ve done at AppDynamics is when we had these conversations about the SiriusDecision funnel, we actually changed the name slightly. MQL is publicly what it’s called from SiriusDecisions. We actually just stripped away the M and call it a QL.
Nathan Zaru: QL.
Niel Hildebrand: We didn’t want that to be marketing centric. We want it to be anyone in effect can generate a QL. Even a partner. A partner can send in a lead, a channel partner, and have that be a quality lead, and then you’re right. In the example you just gave, a rep, that time between a QL and an SQL. It could be zero seconds because they could immediately move it from saying, “Hey I met this guy in the street, and they’re definitely qualified. I’m going to create an opp.” Then there’s zero time, whereas somebody in marketing could take days.
Nathan Zaru: Sure. Sure. Growth is always on the tip of everyone’s tongue. Growing and as you said, optimize revenues. Growing revenue, growing the business. You, Niel, work on intersection of marketing and data and optimization for that matter.
Niel Hildebrand: Yep.
Nathan Zaru: I’d be remiss to not ask you, how do marketing people find more leads? How do we grow? How can you use data to go find yourself more customers?
Niel Hildebrand: The key to that is if you have instrumented understanding that mark of quality lead, and you understand what you’re trying to get in terms of opportunities because then beyond opportunity creation companies could have two day sales cycles, or 100 day sales cycles. That opportunity normalizes that. No matter what it should take you some normal amount of time no matter the company to understand what take to find an opportunity.
Nathan Zaru: Right.
Niel Hildebrand: The key data-wise to be able to optimize creation of those opportunities is to capture both the how and the what on that marketing campaign. Where a lot of companies I think fall short is they’ll capture one of the two. They’ll say, “It turns out most of our leads come in from this asset, or most of our leads come in from our website, or most of our leads come in from contact means, or this event.”
Nathan Zaru: That the how or the what?
Niel Hildebrand: That’s the what.
Nathan Zaru: That’s the what.
Niel Hildebrand: That’s what did we get the prospect to do.
Nathan Zaru: Okay.
Niel Hildebrand: Really what helps you from marketing is how did you get them to do that. Did you pay for traffic? Did it come in organically? Did you make it happen via email? Did you email them then they download the asset?
Nathan Zaru: Okay.
Niel Hildebrand: That’s the thing that a marketer can actually take action on, but you need to capture both of those. Not only is it that maybe it turns out this eBook that you created is driving a lot of quality leads, but it turns out that a sponsorship on some third party site is what’s driving people to download this eBook that turn into better quality than when you send them an email. In which case, put more money in the sponsorship. If you capture both of those piece of data, and whatever other meta data you can about that how or the what, then you can run that through, progress it, and help to optimize for SQLs.
Nathan Zaru: Okay. In terms of marketing analytics, a lot of people think, and I think most marketers start with Google Analytics to get you some of the how I suppose in this case.
Niel Hildebrand: Yeah.
Nathan Zaru: Okay. What do you think about Google Analytics as a starting point and assuming someone’s already there, what’s the next step?
Niel Hildebrand: GA, it will tell you how someone interact with your site. It’s okay for website path analysis. It’s okay for telling you for setting a goal and a funnel towards that goal.
Nathan Zaru: Goal optimizations, definitely.
Niel Hildebrand: Right.
Nathan Zaru: That’s what we use here.
Niel Hildebrand: Then understanding what’s the best way to get someone to download that asset, but it falls short of then continuing that discussion all the way through to opportunity or beyond.
Nathan Zaru: Okay.
Niel Hildebrand: In a B2C environment, it actually I think connects fairly well because it’s a very linear thing. If somebody comes in and does something, buys a product from you, and you can take whatever data base has all that success data around your dollars …
Nathan Zaru: Sure.
Niel Hildebrand: … feed it back in. That I think is a pretty straight forward scenario. In the B2B world though, GA alone will only tell you, “Hey you got a bunch of people to download this asset,” but not the quality of that download.
Nathan Zaru: Right.
Niel Hildebrand: You may be patting yourself on the back because you got more people to sign up for your web trials, and maybe you dealt with it week over week, but maybe that extra came for people who were signing up for some promotion and are all just total crap. You have no idea just in GA. You really need to connect that all the way into Salesforce.
Nathan Zaru: Okay, so Salesforce. Scoring it in Salesforce. Salesforce being your CRM here, but what about customer success technology that everyone’s talking about nowadays? Is that a big factor in this equation?
Niel Hildebrand: You mean intercom or Gainsight?
Nathan Zaru: I mean intercom, Gainsight, Preact. On the side of 101, Salesforce has a banner now saying, “The best customer success tool.”
Niel Hildebrand: Yeah.
Nathan Zaru: Everyone’s a customer success now. What do you think about that?
Niel Hildebrand: I think where customer success comes in is when you’re trying to optimize the churn and up-sell.
Nathan Zaru: I see.
Niel Hildebrand: It could be that you’re getting a bunch of people to come in and sign up for a trial, and it could be that those people end up buying, but if they’re all churning out after a month, you just changed the point of yeah they get you some money, but they’re still not a quality customer. Right? You want to get that person that continues to come back and pays you money. Where the customer success platform data could come in is if you have a high velocity type product. You have a lot of people coming in and trying it for one month and then churning out. That’s really then where you would benefit from connecting all the way to the end because then maybe it turns out that people you bought from AdWords tend to come in, only buy for a month, and leave, and you’re not going to be able to know that unless you connect to them all. If it’s not that high velocity type model, and you aren’t seeing people churn out after a month, which you can get that data without connection to the front of the funnel, you’ll really only get the benefit if you’re then trying to optimize for up-sell.
Nathan Zaru: Bringing all the data together to facilitate marketing to sales hand off. Niel, thanks so much for coming by.
Niel Hildebrand: Yep, thanks. Appreciate it.
