Henry Schuck is the Co-Founder and CEO of DiscoverOrg a platform for finding, connecting, and selling to more prospects using a accurate high-quality contact database. The company was founded in 2007, and Henry has led it through rapid growth going from the original three employees to over 500 today.
Henry is the list expert, so put your seatbelts on. We have a great conversation about lists. In this episode, he shares the story of how he and a friend founded DiscoverOrg and how they grew the company. They even had to refine their product to meet their own sales needs. Once they got the process down, their sales numbers went through the roof.
- DiscoverOrg is a sales intelligence tool that profiles who’s who in about 175,000 corporations.
- They use a mix of proprietary technology and a team of 300 human researchers.
- Maintaining the data is one of the most difficult aspects.
- It takes 70 hours to count to a million. They profile almost 3 million contacts across their database.
- Henry has been in this space for more than half of his life. He began with a small intelligence company called iProfile.
- Then he went to law school.
- In 2007, Henry and a friend from iProfile started DiscoverOrg and grew it organically.
- They were missing projections by 6 million dollars in 2010. They started thinking about how to plug that hole.
- Working backwards to reach a goal. They found the resources they needed, but they needed to build the list datasets for themselves.
- They realized they needed a team of English speaking humans to solve their information gap in their lists.
- They would score fits on a scale of 1 through 5 using what they called fit rank. Then they would have the human team build out the lists of the companies that fit.
- Knowing what companies and people to talk to and their contact information and what tools they use and then target the approach to them.
- Once they got the processes into place to deliver high quality information, they blew their sales numbers out.
- The biggest lever you have to pull is the data you feed your SDR team allowing them to focus on messaging and follow up.
- You want your datatool to cleanse and maintain the numbers, emails, and contact info.
- This is an investment that pays off through everything in your organization.
- They looked at performance metrics to see how many calls needed to be made to hit sales numbers and then worked backwards to provide that amount of contacts.
- If a company doesn’t respond, you still continue to call them. They are still in your market. Organization priorities are constantly changing.
- List fatigue and a 30% attrition rate. Refreshing the lists is absolutely critical.
- Henry Schuck on LinkedIn
- @HenryLSchuck on Twitter
- Steven Covey
Marylou: Hi everyone, it’s Marylou Tyler. This is the podcast that I know I’ve been waiting for forever. In fact, I think I pinged Henry probably a year ago, asking for some of his time to talk to us today.
Henry Schuck is the co-founder and CEO of DiscoverOrg. He is a list expert so put your seatbelts on because we’re going to have a great conversation today with Henry, all about lists. Welcome to the podcast, Henry.
Henry: Thanks, Marylou. Thank you for having me. I’m sorry we made you wait a year.
Marylou: That’s okay.
Henry: For what is to be anticlimactic.
Marylou: Right. As my mom used to say, great things are worth waiting for. I put you in that category, Henry.
Henry: Thank you. I appreciate that.
Marylou: I’m just going to turn the mic over to you because everybody has probably a gazillion questions. I know that you wanted to share a story of your journey and what you needed to do to get from point A to point B in leveraging the list. I’m going turn over to you.
First, tell us about your role in your company and how you got interested in this particular part of the pipeline and then the story that I think would be very enlightening for a lot of our listeners today.
Henry: Again, thanks for having me. At DiscoverOrg, what we do, we’re a sales intelligence tool where we profile out who’s who within about 175,000 corporations across the globe. We’re profiling who the people are in IT department, the marketing department, sales department, the legal department, the operations department.
We’re doing that through a mix of proprietary technology that we’ve built, that gathers names, titles, and contact information. But then the big differentiator between the way we gather and cleanse data and how anybody else does it is we have a team of about 300 researchers who are in our Vancouver, Washington offices.
After the machines go and do all the work to create what is an okay dataset, we put that in front of 300 researchers to cleanse, to make sure that people are still there, to add direct dial and mobile phone numbers, to make sure the emails work, to make sure we can gather descriptions on what the person’s job function is and then, to keep that data maintained.
That tends to be one of the most difficult pieces of what we do, gathering a net new contact today. When it’s fresh, it’s great. Six months from now, the person has gotten a raise of left their job. And so we’ve created a large engine that also maintains that data. Yeah, it’s a pretty big operation at this point.
Marylou: Sounds very daunting.
Henry: Yeah. I wish I would’ve written it down but I googled the other day how long it takes to count to a million. What do you think is the answer to that?
Marylou: Oh my gosh. Two hours? An hour? I don’t even have a clue.
Henry: If you did it without any breaks, then it would take nearly 70 hours just to count to 1 million.
Marylou: Oh my God.
Henry: You could come into work every day for two weeks and just count all day. It would take you two weeks to get to a million. Today, we profile almost three million contacts across our database. You think about the effort that it takes to make sure those three million records are up to date and have phone numbers, titles haven’t changed and they haven’t left, the reporting structure is still accurate. You just need a big machinery behind it.
I got in the space, actually, I’m young, actually next youngest, but next year I will have been in the space longer for more than half of my life. I got started in 2001 when I was in college and needed a job. There was a small little sales intelligence company called iProfile that was based in Henderson, Nevada. I took a job and we were building really high quality dataset of information technology decision makers. I worked there from 2001 to 2006 where the company grew from about $300,000 in revenue when I got there to close to $5 million when I left.
The company had a successful private equity exit. I left and I went to law school at Ohio State in Columbus. In early 2007, a buddy of mine called me who I have recruited back at iProfile and said, “Hey look, I see a lot of value in building a company that provides this type of data to corporations and I’d like to start one. I don’t want to compete with iProfile but I want to start something like it.”
That was 10 years ago. We started DiscoverOrg and grew it really organically until 2014 when we took some investments from PA Associates and Goldman Sachs. We have been bootstrapped until then and sort of figuring things out as we went along and we brought in these institutional investors in 2014. All of a sudden, when you do that, one of the things that comes with it, and there’s no illusion to this, but one of the things that comes with it is a lot more focused on numbers and forecasting and what are you going to do in 2015 or 2016? How are you going to get there? Let’s think through all of those things so we don’t miss a number and we have these high projections for the business, how you meet those.
We went from a company that was growing fast and profitable and didn’t spend a lot of times thinking about those things which is sort of like organically, we’re growing and so there was less of focus on that. A company that needed to plan for growth.
That brings me to late 2015. We had an offsite meeting with all of our executives and one of the things we realized in that offsite meeting was with the current staffing level and what we were doing, we were going to miss our projected forecast by $6 million.
Marylou: Oh no. Okay.
Henry: Not a great transition to be in. And so we started thinking about how do you get there? How do you plug that $6 million hole? I saw, Marylou, some of the notes from one of your previous podcast about how you work backwards to figure out how you plug that hole. The way you do it is you go, “Okay, you need to do $6 million in ACP. And so how many wins is that?” You just take your average contract price and divide it against the $6 million. And then once you get that, you go, “Okay, what’s my opportunity to deal closed ratio?” You figure out that. You figure out how many opportunity do you need.
Henry: And then you say, “How many demos do I need in order to get that number of opportunities?” What’s the ratio of demos to opportunities you go up against. Once you get to the demos, you figure out, “My current people who set demos, how many can they create a day, a week, a month? And then how many of them do I need?”
We did that. It wasn’t a resource issue. We’re like, “Okay, we need x number of people. We need 15 more SDRs in order to do that.” You knew the resources you needed but the next thing that our Chief Revenue Officer said, remember, we solve a problem for our customers around knowing the people at companies they should be selling to and knowing the company they should be selling to.
For example, at this point, it’s 2015, we’ve been doing it for eight years. And so here we are, we know the number of people we need, we know how many demos we need, we know how many opportunities we need, we know how many wins we need to back into the $6 million number. Our Chief revenue Officer says we still can’t do it.
Why can we still not do it? We’re going to give you the money for the headcount, we have an HR staff that’s going to go hire these people. We’re going to tell them to go set meetings and we’re going to do it. They said, “We can’t do it because we don’t have a list clean enough that list out our accounts and our prospects at those accounts to be able to do that.
Marylou: That’s saying the cobbler who has no shoes.
Henry: It’s just this moment I’ll never forget because here we are at the time solving this problem for 2,000 customers and the trick here was we had never built the data sets for ourselves, we’d always built it around information technology decision makers and large corporations only. And so you have this great dataset but it just doesn’t do anything for us.
Our CFO said, “We’ll, that’s a problem this group is really well suited to solve.”
Henry: And so we did. Before that moment, what we had been doing was sort of in a willy nilly sort of way, we would find companies that we wanted to prospect into. We would extend a list of companies to an outsourcer in India and we’d tell them to build us a list of contacts that we would then use in our sales process.
That wasn’t good enough for us. The data that was coming back would get old very quickly. It would bounce and when it bounces, it would hurt our email deliverability. We’re having trouble getting into inboxes because we were using lists. We’re buying from a bunch of different list brokers and that tended to cause us more trouble than it was worth. At one point, we had been blacklisted against three different servers, doing nothing outrageous. We’re not a big spamming house or anything. We’re just sending prospecting emails to prospects and then getting blacklisted because we’re sending too many emails that bounce, too many emails to people who weren’t there anymore.
We said, “Okay, the only way to solve this is the same way we solve it for all of our customers. But it’s expensive.” You have to do it with researchers. You have to do it with human beings. They have to be English speaking. They have to mainly be in the US. And we’re going to need our own team of that. What we did was we said, “Okay, if that’s the way we solve this gap, then let’s solve this gap.”
We stood up a team at DiscoverOrg in Vancouver. We call them DiscoverOrg. We built a process where our SDRs and then our third party outsourcer would do something called a fit rank of account. We could find big lists of accounts. We’d pull accounts out of our Salesforce system. We’d pull accounts that we had previously prospected to. We’d pull list of accounts from the Inc. 5000 listing. We’d pull fast growing company lists. We trained all of our SDRs and we trained our third party outsourcer to be able to what we call fit rank these companies.
On a scale of one to five, how good of a fit are they for us? I wanted to home run. It’s like B2B sales, more than 100 employees in the United States has a VP of Sales, has sales reps. That’s a great fit. That’s how you fit rank them. We trained everybody how to fit rank one through five. All of a sudden, we had this really great list of companies that were ranked one through five based on how good of a fit they were for our product.
And then we gave those lists to our team here in Vancouver, the DiscoverOrg for DiscoverOrg team. We said build us out the VP of Sales, the VPs of Marketing and the CEOs of these companies. And put that data into DiscoverOrg where we’re going to consume it with a new team of SDRs that’s going to call on it and bridge that $6 million gap.
We solved two problems. First, we solved the problem of we don’t know who the companies are, we don’t know companies that fit. We know our ideal buyer profile, we don’t know the companies that fit that ideal buyer profile. Let’s get to that with a fit ranking.
We don’t know who the people are at those companies we should be talking to so we need that. Once we know who those people are, we don’t know what their emails are, we don’t know what their phone numbers are, we don’t have a way to get in contact with them and so we need contact information.
And then, we layered on top of that. By the way, if they used Salesforce and Marketo or HubSpot or Pardot, those are even better fits for us. If they use Outreach or Tellwise or PersistIQ or one of these SDR tools, that’s another great fit for us. From now, we can look at a universe of fit rank one companies that are using Outreach, Salesloft, Tellwise, that are using Salesforce, that are using Marketo and then be really targeted with our approach to them. That’s what we did.
The first four or five months, we were getting our act together in 2016 and the back half of 2016, we absolutely blew out our number. It was because we put these systems and processes into place that were able to deliver really high quality information on our prospects on our accounts that allowed our SDRs to be leveraged more fully.
The SDR model works. Having people do outbound calling to set appointments and send emails, that’s a model that works. It works in thousands and thousands of companies across the globe. But there are varying levels of effectiveness of those SDRs. The biggest lever you have to pull is the data you’re speeding your SDR team because you can either live in a world where we live in where data goes into a dialogue, data goes into email sequencing tool, the SDR comes in, stitch down and the phone is dialling direct dial phone numbers for them all day. They’re able to focus on their messaging. They’re able to focus on being personalized. They’re able to focus on doing really good follow up to the people they’re reaching out to as opposed to wasting large parts of their day doing research on their prospects and research on their accounts. That all starts with really high quality data that’s set into the system and then it’s maintained.
That’s how we came to this. I would say if you’re looking for a data tool to drive your sales and marketing efforts, one of the things you want to make sure of is that the data being cleansed and maintained, that it has direct dial phone numbers, that the emails are not just guessed based on a formula but are being validated and verified for you in some way to make sure that you’re not bearing the brunt of deliverability issues.
And then you should invest in this. It is an investment that pays off everywhere throughout the organization whether it be in marketing campaigns, or SDR calls, or territory mapping. Really, everything you’re doing is driven by the account in prospect information you have on both customers and prospects.
Marylou: You know, Henry, one of the things that, this is way back in 2011, when we did Predictable Revenue, I had done a personalized benchmark or AB split test, if you will, of lists from vendor A to vendor B. The net result was I maximized my return on effort by going with the vendor who had a 5% undeliverable rate versus a 30% undeliverable rate.
Part of the shopping around and figuring this out is like you said, the cleansing portion is one of the biggest problems to keep on top of, that and the original sourcing. I love the way you said for planning your fit rank is that you looked at various sources that weren’t necessarily list per se of contact information but they were lists of companies like you said, I think you said Inc. 5000. You looked internally into your house list. Previous customers is another gold mine if you’re an existing company already and have what we used to call dead accounts. The accounts that you had conversation with, to update them.
Henry: Previous leads, people who didn’t convert could be a pretty good gold mine too.
Marylou: Me as a sales rep, when I went into any new company, I would always go to the turn away lead bucket to start my work because it was a pretty good deal to have a higher probability and closing those accounts.
The other thing I heard you say is that you really focused on the ideal account profile and you had a one to five ranking. That’s a great idea for those of you who are thinking about how can I segment out my accounts. What did you decide? Were there like three or four parameters that made it to number one or did you have more loose guidelines as to how to score these one to five?
Henry: People will create their own rules around this but the way we thought about this was there was a certain size component so there was the size of the company component. There was a company type component. Like for our IT data sets, this piece was more qualitative. Could you look at the website and get a sense that they were selling to IT buyers or marketing buyers? One of the datasets that we sell to. That’s more of like is there a product stick there? Do we have a product that fits for them? Do they have a vice president of sales or a vice president of marketing? That’s an indication that the organization is mature and making investments in these types of tools. Those are some of the parameters that went into the one to five rank.
I think for a lot of companies, it’s actually probably easier. What you can do in DiscoverOrg is say show me all the companies that are financial services or insurance companies with over 1,000 employees based in New York, New Jersey, or Connecticut that are using Oracle, or Salesforce, or any technology and then let me see the VPs of information security there. That’s just a couple clicks away to creating that buyer profile.
Marylou: Exactly. Like you said, when you were working through the planning of the actual ACV and working backwards, I did the same thing with the actual list because we basically want to know if we have the luxury of having clients but some are more established companies, go to your companies that you closed and find out the ideal companies that high revenue potential, high lifetime value, highly likely to close. Profile them if there’s a good percentage in your win rate and then work backwards as to the ideal account profiles from there and also the ideal prospect personas, who the people that were involved at top of funnel, middle of funnel, bottom of funnel and make sure that as part of the discovery process that you’ve got this people entering into the CRM or your database, whatever you’re using in order to be able to have a more holistic approach to your sales conversations at the various stopping points in the pipeline.
Henry: It’s a really great advice, Marylou. It’s a really, really great advice. One of the things I used to do was after we’d close a deal, I’d Google the company and then I’d take the first line at the descriptor in Google. It would be like DiscoverOrg will say like sales intelligence. And then I copy whatever that descriptor was and I throw it back into Google and then I just look up all of the other companies that use that kind of phrasing in their description.
Marylou: That’s great advice. You know Steven Covey, Begin with the End in Mind. Go to the end, the desired result and work your way backwards and you’ll have, it’s not perfect but you’ll have a better start than just putting your finger up in the wind and see which way it’s blowing kind of thing. It’s not going to be as scientific and as pleasurable for you when you’re banging your head against the wall because you didn’t pick the right ideal account profile.
Another question that I have is, I don’t know if you have a sense of this, The DiscoverOrg for DiscoverOrg team, did they have some benchmarks that that they had to meet in terms of net new growth on the list or were you just letting them do their thing? How did that look? I’m interested in list update and net new. Were those metrics for you with this team? And if so, how did you decide per rep what that look like?
Henry: A lot of that was baked into the performance metrics we have for the other team, on our research team that were already doing this but I think that we looked at it was once you got to how many demos you needed. We broke it down one step further. So then you knew you needed to get this many demos and so say an SDR needed to set two net new demos a day, and so then we went back and we said, “Okay, the SDRs who are setting two new demos a day, how many calls are they making each day in order to do that?”
We worked back to that point and we said, “Okay, there’s a certain number of contacts that we’re going to need each week, each month, each quarter that we’re going to need, that DiscoverOrg for DiscoverOrg team to deliver in order for these additional SDRs to make the number of calls that they need to make in order to get the number of demos that they need to set.”
One of the things to keep in mind, because it’s a pitfall to fall into, that if you need to bring on, call it 200 net new customers this year. In order to get net new customers, you need to have call it 1,000 opportunities in order to bring those 200 on. You don’t need 20,000 accounts to prospect to get to that 1,000 because what ends up happening is just because you call a company between the month of January and March and they didn’t respond, it doesn’t mean you don’t continue to call them from March to June.
Your total addressable market is going to be something that doesn’t change. And so if you didn’t get a hold of them between January to March, it doesn’t mean they’re no longer in your addressable market. It probably just means like timing wasn’t right. You probably want to bench that company for three months and then go back to it.
One of the things you realize is that organizations are constantly changing and so their priorities are changing and so their needs for your tools and services are changing. Something that’s not interesting in January, if DiscoverOrg is not interesting in January, it may be really interesting in April when a new VP of Sales comes in and decides that he’s going to close that six million dollar shortfall by hiring SDRs and going to market in a different way.
Just keep in mind organizations are dynamic changing things. Just because you didn’t close Wells Fargo today, doesn’t mean it’s not an opportunity tomorrow.
Marylou: There are trigger events, like you said, with movement of employees. There are also times of the year like I know, for me, Marylou Tyler, I can spend February in content creation alone. I can build all my classes. Because everybody is still in lala land over the fact that it’s first quarter and life is good, but as the year progresses, it gets crazy busy. I know that so I know to plan my list advanced and also triggers I watch for with the internet now, we get all this wonderful triggers when people move around, especially people who have been clients of mine that moved around or other events that come up that I know would be interesting for me to start again with my sequencing. Those are the other things that with the list, you can definitely take advantage of.
The other thing I wanted to mention was as you said before about contacting an account, I can remember just like it was yesterday that I sent three emails into City Groove for an account that I was working on at the time, two people responded back with we don’t give out information. I was asking for referral like the the Predictable Revenue model.
The third person said that I am so happy that you found me, Marylou. You never know. You gotta work that bullseye of indirect and direct influencers as well. You can really thread an account over the year, just by changing out the ideal prospect personas that you go after as well.
Henry: Absolutely. So many examples within 10 minutes of sending an email out exactly that. One person says this is totally uninteresting. And another person says this is exactly what I’m looking for.
Marylou: Right. The convention approach. I get it. “We never do that. We’re not interested and we’re never going to change.” And then you get the guy around the corner and says, “Oh my gosh, this is the best thing since sliced bread.” You never know and you got to plan out for that, that’s why I was curious about the list replenish and whether you think about not only the decision makers but the people who influence the decision makers.
The other thing that I’m really curious about is I came up through call center ranks. Early 90’s, I was running a 250-seat call center. We did a lot of calling, obviously. We set appointments. List fatigue, that term was really important to us. Is that an important term now? And if so, are there some metrics around that that you can share with the audience?
Henry: What was the term, Marylou? I missed it.
Marylou: It’s called list fatigue. It’s essentially how do you blend in net new names with existing names to create a dataset that’s fresh every time?
Henry: That’s a great question. One of the things that we realized across our datasets is that there is a 30% attrition rate year over year. You get a new list on January 1st. you can count on 30% of those people having switched jobs by the end of the year on average. And so, your ability to remove those 30% and refill in the next 30% of people, whether they’re at those organizations or at others, I think it’s absolutely critical to your success there.
I think the thing is list fatigue if they’re getting the same messaging over and over and over. And so, your ability to create content that’s valuable and interesting and having a unique approach to reaching out to buyers becomes even more critical because think about the fortune 1,000. Every technology company is trying to sell to the IT department of a fortune 1,000 company. They’re doing it through calls, emails and in person meetings. You have to get your timing right and you have to stand out.
Marylou: The attrition rate 30% and then you add on to that if you’re having meaningful conversations, they’re moving into the pipeline. They’re not moving out necessarily. They also have to be added into that number. I think we came up in the new book, in Predictable Prospecting, a 40% number to play around with. For every 100 net names that you have in your membership list, in your dataset, at the end of the sequence, you can count on 40 of those records out of 100 out the door. Once you let that thing incubate and when you bring it back into the fold, there should be 40 new names now that you’re going to stick in there. They could be names from other datasets that have gotten too small. There is a little bit of secret sauce to creating your datasets so that you have a fresh list each time you run a sequence.
And as Henry pointed out, having a messaging, because you’re getting smarter now, because you’re having messaging, you should be able to wrap up those messages with more specificity around what the buyer responded to for a pain point and caused them to want to continue to having a dialogue with you. All that information should feed into the next sequence so that your ordering the problems and pain points accordingly so that you’re reducing lag.
Having all these wonderful tools with the list, the list becomes really fun to work with. It’s my favorite part of the pipeline because you do live and die by that list in terms of your revenue.
Henry: That’s right.
Henry: I couldn’t agree more.
Marylou: Henry, I want to be respectful of your time. We’re running out of time. We’re running towards the end here. What can people do to get a hold of you or the work that you do or to understand more about this magical thing called the list, how would they start researching or getting smarter about this?
Henry: We have a ton of resources at discoverorg.com so you can visit discoverorg.com if you’d like to see some data on some of your target accounts. To get a free list of leads, there’s a free data button there to fill out. So discoverorg.com is a great place to see resources. We also have a checklist for when you’re looking at a data vendor, what things you want to make sure they have or do. But lots of great resources there and if you want to see some leads on your own account, fill out the free data form and we’d be happy to respond to you and connect with me on LinkedIn.
Marylou: Very good. I will put the link to the checklist because I’m sure people just perked up when they heard that because that’s one of the things, that where do I begin, how do I know that I’m not forgetting something, so having a checklist like that would be great.
Henry, thank you so much for time. I very much appreciate you coming on the show.
Henry: Thanks a lot, Marylou.