LeadCrunch: Using AI to Make Sales Teams More Productive

Interview with Serial Entrepreneur Olin Hyde, Founder and CEO of LeadCrunch

In this interview with Olin Hyde, Founder and CEO of LeadCrunch, we discuss the LeadCrunch value proposition, the importance of listening to customers, and building a world-class team & corporate culture.  Olin has taken LeadCrunch through a couple pivots before ultimately focusing on the improvement of lead generation for businesses.

Olin is a serial entrepreneur who wrote his first line of code at age 12 on a home built computer. Previous startups include National Facilities Group (acquired by Accruent) and Auto-Semantics (acquired by ai-one). Olin is one of the principal architects of the DeepFind™ machine learning platform which powers all of Englue’s products. He is a frequent speaker on how artificial intelligence can help people make better decisions.

Olin founded LeadCrunch in 2013. LeadCrunch finds high-precision B2B sales leads using artificial intelligence.

Olin holds a Masters of Advanced Studies in Systems Engineering from UCSD and a Bachelors of Philosophy in Interdisciplinary Studies from Miami University.

Olin lives in San Diego with his wife, niece and two goldendoodles where he enjoys outdoor sports, the performing arts and holding business meetings on bicycle rides.

LeadCrunch

Patrick:     This is Patrick Henry, the CEO of QuestFusion, with the Real Deal…What Matters. We’re here today with Olin Hyde, who is the founder and CEO at LeadCrunch.

                  Olin is a serial entrepreneur who wrote his first line of code at the age of 12, which is a number of years ago, on his home-built computer. Previous startups include the National Facilities Group, acquired by Accruent, and Auto-Semantics, acquired by ai-one.

Olin:          That’s right.

Patrick:     Olin founded LeadCrunch in 2013 under a different company name. LeadCrunch is an artificial intelligence engine that finds high precision B-to-B sales leads for small and medium businesses.

                  Unlike other lead generation systems, LeadCrunch predicts who will buy from whom. It enables companies to dramatically increase lead conversion rates to accelerate sales.

Olin holds a Master of Science in Engineering from UCSD, here in San Diego, and a Bachelors of Philosophy in Interdisciplinary Studies from Miami University. He has the yin and yang going on there.

Olin lives here in San Diego with his wife, niece and two golden doodles. I actually have labradoodles. I’m allergic to many pets, so we have Australian labradoodles to deal with my allergies. He enjoys outdoor sports, performing arts and holding business meetings on bicycle rides. Welcome, Olin.

Olin:          Thank you.

Patrick:     Tell me a little bit about when you started LeadCrunch. What was the focus of the company at establishment? We talked a little bit before the show about how you’ve gone through a couple pivots. Why don’t you take us through the story there?

Olin:          I’ve got the personality disorder. The people who go out and start businesses usually could find really good jobs doing something that’s far steadier and less risky working for other people. However, that’s not me. I just love solving hard problems.

What we’re doing now is really interesting. The best part of my day is talking to customers who are absolutely thrilled with what they’re getting from us.

It’s really cool to get on a customer call and have a customer cheer our success. That’s really because our technology enables small and medium-sized businesses to beat the incumbent, to go after the enterprise company.

We give them state of the art technologies that enable them to call the people who they should be calling to grow their business. That is identifying people who are just like their best customers.

That’s where you have the easiest way of repeating success. However, that’s not what we started doing. This is the end stage of a long journey that started back in August of 2013.

Three of us started this company. It was called Englue at the time. We had seen how awful IBM Watson was. We decided we were going to invent a better version of Watson.

We’re going to actually go out and develop something for medical research and train the supercomputer at UCSD to pass the U.S. Medical Licensing exam. Imagine, a computer becoming a licensed MD.

We started down that path. There was a lot of doubt that we could do it. We built a very early prototype and within 12 seconds, it was able to find a link between Type 1 diabetes and the herpes virus.

That got published. It got about $11 million of funding. We didn’t get the funding. The researchers who were doing the work did, a gentleman by the name of Dr. Darius Schneider, who’s a physician/scientist. At the time he was a Fulbright Fellow at the La Jolla Institute.

It was really his genius of coming up with the hypothesis, putting it into the machine, and out came confirmation that there might be a link there.

It did that by reading more than 10 million open sourced medical research articles. From that point, we thought, “Maybe there’s money in medical research?”

There’s not. There’s a reason why medicine took a thousand years to adopt germ theory, a reason why it took medicine a hundred years to adopt the use of the microscope. It is not an industry that is eager to change.

We found ourselves with a tool that was solving a problem that had absolutely no economic value, so we pivoted. One of my mentors is a gentleman by the name of Dr. Marvin Langston, who is the former director of DARPA’s A.I. programs.

Marv came to us and said, “You should go after this deal with Lockheed.” We had an opportunity to compete with two very big names, Palantir Technologies and IBM.

We actually won a contract to supply a research and development group inside of Lockheed with our core technology. By this time, we’d built it to the point of giving it a name and getting it trademarked and looking at the patents to protect it and so forth.

It’s called a deep find. The idea is if I show you an example of this, you can find whatever is similar to this across lots of other information.

This could be a medical hypothesis that you want to find in other literature. This could be a known target. The targets in the military look like this. What else is a target?

This is a very basic decision thing of finding similarities in very complex data. We won the Lockheed contract. A great customer. We learned a lot. We also learned that we really didn’t want to become a defense contractor.

We wanted to go into a very highly scalable business. We were in the unfortunate position of having a really cool solution that we didn’t know what it was solving.

As we were talking earlier, that’s a bad position for an entrepreneur to be in. You want to solve the problem, not come up with the cool technology.

We actually thought of six different ways we could use our technology. One of them was, if we’re finding targets for the military, can we find customers for businesses?

We took it to five companies. We thought, “If one of them buys, we might have something here.” Seven bought, because one of our early customers was a lender that did capital equipment leasing. They only care about one thing, and that’s getting paid back.

They took it to their borrowers and their borrowers loved it. Boom! We had a product. We knew that we had something that would work. We had to build it. We announced it, and in the eight months or so since it’s been announced, more than 1800 companies have signed up for a trial.

We released the sellable version of it, the one that we’re monetizing, in January. It has just been a wild rocket ship since then. It’s been great.

Patrick:     Awesome.

Olin:          We tripled sales. In the early months it’s easy to triple a small number, but we’re growing right now at about 100% per month. We expect to be able to keep that up, at least for another three or four months.

Patrick:     That’s awesome. Obviously the business model has changed over time. You’re a serial entrepreneur. Was it frustrating? Do you have board members or was it a guess?

Take us through that process on how you dealt with the challenges of navigating through a couple different pivots of a couple year period of a time.

Olin:          Pivots are really scary. That’s when teams fall apart. I’ll forever remember the date of December 4th, 2013, when I thought we were going to be shutting the business down after only five months.

                  We had that meeting around my dining room table and I said, “Guys, this isn’t working. We’re going to have to change directions.” It almost puts a tear in my eye. Everyone doubled down and worked doubly hard around that pivot.

Patrick:     That’s great.

Olin:          Pivots are not something, I think, to be particularly proud about. I think that the key lesson I learned was we were dead wrong on where we thought we were going to make money early on.

                  After we went and did the project with Lockheed, I called my attorney, who’s been a phenomenal resource, and said, “I really need to bring in an outside board of director, someone who’s turned math into money more than I have.”

We were very fortunate to be able to recruit a gentleman by the name of Krishna Gopinathan, who is the founder of Global Analytics. He’s the founder of Applied Data Finance. His name’s on the patent for the FALCON Algorithm, which a local company, HNC, sold.

HNC was bought by FICO for $800 million. Most of that value was around the FICO algorithm, which protects more than two billion credit cards from fraud. Krishna came in as a board of director. My business partner’s the best business partner I’ve had. His name’s Sanjit Singh.

Sanjit’s the sales guy. His claim to fame is he’s built a company from zero to $30 million in sales in two years in the shipping industry.

He knows how to sell to small and medium-sized businesses. A lot of our pivot was driven by Sanjit calling me at 11:00 on a Sunday night saying, “We have to do something different.” I said, “Yes, we do.”

The bringing in of an outside board of director was absolutely critical to our success. I think we really spent a lot of time thinking about culture and how we develop a culture which really encourages people in three key dimensions: to be the best in the world at what they do, to have a sense of purpose that what they’re doing’s really important and is something that they could only do as part of this team, and, finally, giving people the autonomy to be able to do the job in a way that they see is best.

Our team is now eight full-time and we have four part-time in addition to that. We actually have three open positions at the moment. We will probably double in size between now and the end of the year.

Patrick:     Okay. Terrific. For the laymen out there, give us the laymen’s tutorial about what machine learning is and artificial intelligence. I’m sure many of us have seen the Matrix. We don’t want to be scared by A.I. Tell us a little about it and your application of that.

Olin:          When artificial intelligence works, we have a name for it: software. It’s not a black heart. It’s really quite simple.

                  The idea of machine learning is that the machine recognizes patterns and it does pattern recognition without overt human intervention. Machine learning is a very long and broad field that’s been around since the 1950s.

Patrick:     Most of the stuff around big data is really machine learning, and it’s defining algorithms that get progressively more efficient over time the more they do a particular task? Is that a good way to put it?

Olin:          Absolutely true. You can think of it in two different buckets. There’s the machine learning that’s entirely autonomic, that’s hands-free. Neural nets or deep learning would be an example of that, where it learns by examples only.

                  Then there’s supervised learning, where you actually give it feedback to say, “Yes, your guess was right,” or “No, your guess was wrong.” Pandora would be an example of that.

Pandora serves you up a song that it thinks you’ll like and then you confirm or deny that. By giving it a confirmation, it helps it learn a little faster.

Patrick:     Interesting. With LeadCrunch, are you in that latter category where there’s interaction between the salesperson, or whoever it is, the sales organization, and the product?

Olin:          We actually do both. We believe that salespeople are the most valuable people in an organization. We really look at machine intelligence as a way of augmenting human capability.

                  What we see our technology as doing is giving a secret weapon to salespeople to really outperform their competitors. The machine will never be able to make a sale. That’s a human-to-human thing and we want to preserve and augment that.

What our technology really does is tell the salesperson who is the best person to call and why. That way the salesperson doesn’t waste their time on customers who will never buy or who are bad customers.

We really want to help them find a way to shorten the sales cycle. That’s actually helpful to everyone. It’s helpful to the customer, as well, because you always welcome the sales call when it solving a problem that you have at the top of mind. We identify that and then we give that to the salespeople to go out and call.

Patrick:     How do you make money? Is this a software-as-a-service or a subscription model? What exactly do you guys do?

Olin:          We do software-as-a-service. It’s a subscription. You buy buckets of leads. Right now our entry point is you buy a $300 bucket and that gives you 200 leads.

That’s what we’re doing today. That’s our initial pricing. We know we’re grossly underpriced. We increased our price 20% and sales went up.

We’re pretty close to ending this type of pricing and really going more to industry specific and the value of the lead type of pricing. We’re starting that pricing experimentation, right now.

Patrick:     I did a little bit of research, not a ton. I looked at your website, looked at your pricing. The cost per lead seemed extraordinarily low. The question is, is it a qualified lead, non-qualified lead?

I did this experiment on one of the freelance sites called Thumbtag. You have Thumbtag, Guru, Upwork. The economic cost of a Thumbtag lead is about $15 to $16 per lead. I don’t even know if those are really qualified leads.

When you say you can do leads more efficiently and the quality of leads are better, what’s the metric that you use? Is it a sale that’s generated by that lead and you can therefore charge that way, or it’s “I’m going to give you leads and it’s up to you to figure out if they’re good or not” and you’re dialing that in?

Pricing, obviously in this business, is all over the map. How do you say, with confidence, “We’re going to deliver you higher quality leads and the metric that we use, the ‘as measured by,’ for that is conversion rate or profit per lead or revenue per lead?” How do you guys think about that?

Olin:          Let me set the context first. I think it’s really big question. The question is really what is the value of a lead and how do we determine the price for that lead?

Patrick:     Yes, that’s the essence.

Olin:          What we do is we look at the market. We say, “We’re the only solution that is literally a self-service vending solution for predictive leads. We’re the only one.”

How do we price it when we’re a completely new category, when we’re trying to create that category?

What we initially did is say, “Who’s the big incumbent?” That’s Dun & Bradstreet, Infogroup. Those types of companies.

Patrick:     For the lending industry?

Olin:          For everyone. Dun & Bradstreet’s the largest purveyor of leads in the world, certainly in the United States. They’re a $1.6 billion company. Then they do a bunch of stuff other than lead gen. They also do credit reports and other things.

                  We look at them and say, “Wow, if they are the incumbent, what does it take to completely displace them, actually go after them in a very meaningful way?”

We looked at their data and we found that it had a very high error rate. There was a lot of missing information, it wasn’t accurate, and it was very expensive for companies to get inaccurate data at the top of their sales funnel.

If you think of a sales funnel where you get the leads at the top, you qualify them, and then you make a sale at the bottom, a little bit of improvement at the top of the sales funnel can translate to huge gains at the bottom. One or two percent here might be 20% to 30% down here.

What we said was, “Okay, let’s do an initial pricing to see how much the market and what our conversion rates will be, just for going after what we call commodity leads.”

That we concluded we’re crushing it. Our conversion rate, to tell you how much we’re crushing it, 22% of companies that do our free trial buy a subscription within 30 days.

We’re still getting additional conversions later. We’ve only been in market for five months. The hypothesis we’re testing is, can we effectively compete against Dun & Bradstreet? The early indication is yes.

The bigger question is, how valuable are our leads relative to other things? We know that inbound leads are always the best. They always will be, because they’re people who are actively seeking. They come to you and they’re marketing qualified right away.

Patrick:     That’s the fintech model: I’m specifically looking for this. I’m matching you up. Therefore, at least you have a chance. However, they’re charging by the lead. They’re not charging by the conversion.

Do you guys actually get lead conversion data or are you just measuring by how many people buy the subscription service and then convert to the paid service?

Olin:          Converting a lead is much more than just me giving you the name. Back to my original statement, we have a lot of respect for salespeople and what they do.

                  We don’t want to be measured on whether the salesperson’s good or not. We get measured on whether the salesperson tells us it’s a good lead or a bad lead.

When you get a free trial from us, the first thing you do is you give us an example of your best customers. You give us 25 to 50 names of your best customers.

We find lookalikes and we give them back to you. Then you score them individually, “Yes, this one was good. No, this one was bad.” The system gets smarter. Next delivery is even more accurate. That’s what we measure, how many up votes we get.

We love to have customers come to us and say, “We were getting 7% conversion on these old leads that we were getting from an old vendor. We get 30% conversion from you.” We love those stories. That’s really what we’re looking for now, more stories like that.

We had a really fun experience the other day. I was taken out to breakfast by a customer. He said, “Do you know what? I’m going to quit my job and come work for you.”

That’s pretty cool. We’re making an offer and I think we’ll announce that next week. He had crazy success with us. He shortened his sales cycle by 40%, discovered entirely new markets, and had a 300% improvement in his lead conversion rate. That’s the type of story we like to hear.

It’s stunning how few companies measure the success of their sales efforts. One of the most expensive things a company does is hire a sales team.

A lot of companies don’t have any automation, because they can’t afford it or it’s too complex. They’re old school, dialing-for-dollar type shops that don’t measure or control the process.

One of the things that we really designed our system around is we wanted to be agnostic to any technology. All you need is an internet connection, a credit card, and a list of your best customers. That’s it.

With that as a design consideration, we built a system that can work with pretty much any marketing technology stack, from non-existent to the most sophisticated.

We actually have customers on both ends of that spectrum. We have very sophisticated customers and ones that just literally have our system and a telephone.

Patrick:     A potential client is anyone who, obviously, does sales. Are there specific verticals that you’re focused on? It’s hard to boil the ocean, as they say, if you have too big of a market that you’re going after initially.

If you can go ahead and maybe dominate a couple niches first, and they’re big enough niches, you can still grow fast. Are you doing that? What’s your go-to-market approach?

Olin:          Our go-to-market is listen to our customers. We spend a lot of time talking to our customers. Of these 1800 that have signed up, we look at them all and we try to talk to as many as possible.

                  Our customers are primarily marketing companies that are using our technology to generate leads for their customers. That’s the first big vertical.

The second big vertical is IT and internet technology companies. Software-as-a-service companies that are selling across a lot of different verticals love us, and financial services companies love us.

Our early success was all around fintech. We thought that was a really good market for us to go after. Money’s money. A big differentiator is, how much does it cost?

If we can tell a lender who’s going to make a loan before they actually have their credit report run and they can get that relationship early, that’s gold.

That really gave us the idea that a lot of companies have that problem of “How do you get in front of the customers who are not raising their hand, who are not searching for your solution?”

They’re going to need your service, but it’s not top of mind until you bring it up to them. You can be there very early in the sales process and use that contact information for account-based marketing, content marketing.

There’s nothing wrong with using all of the other marketing technologies that are out there. In fact, we encourage you to do it.

As I said earlier, inbound leads are always going to be the best, because they’re actually looking for you. Our technology fills that gap in for people who are not looking for you, or where you can’t compete with other people who are better at inbound marketing. That’s where we really come in.

Patrick:     Terrific. Do you have data that shows the quality of your leads is superior to other methods?

Olin:          Absolutely.

Patrick:     Okay. Is that just the conversions to paid services? What are the other metrics you’re looking at? Raw customer feedback? Anecdotal feedback? What are you looking at there?

Olin:          The best single measurement is negative churn. Churn is what your revenue drop off is from any given account in a month. Churn, if it’s positive, is a bad thing. That means you’re losing customers. Negative churn means it’s a good thing because the value’s going up.

Our average subscription price went from $177 to $588 in just three months. People are spending more money on our leads because they’re working.

We have good case studies from some of our early customers. I just mentioned the guy who joined our company. I went on to AngelList week and out of the blue someone gave us an endorsement. I had never talked to this guy. I thought, “Wow, that’s great.”

The other good indicator is, we provide a referral code. We have a lot of people who are referring us to their friends and people they do business with. That is called a viral co-efficient. Ours is a very healthy number for an early stage company.

Patrick:     Terrific. That’s awesome. How big is the market for this kind of stuff?

Olin:          It’s really big. If you’re creating a category, I think it’s important to be able to look at it from bottom up. What if everyone bought your product? What does that world look like? Or in top down, theoretically, what should this market look like?

                  Let’s do it from the bottom up first. There are about nine million businesses in America. It depends upon what database you look at. Some people say as many as 30 million. We think there are about nine million significant businesses in America that have sales below $1 billion.

Take off the enterprise. We have competitors in the enterprise. That’s what they do. Go for it. We focus principally on the mid-market and small business, which is a fairly open market for us.

Let’s say 30% of those companies do B-to-B sales. That’s about what the economy is, right? That leaves us, just to make the math simple, about three million companies.

We believe that our product will really be costing around $1000 a month for most customers. As we mature, our prices have gone up from $177 to $588. We believe it will easily double again. To be conservative, let’s say $1000. That’s a $36 billion a year market. It’s big.

Patrick:     That’s huge.

Olin:          Ruth Stevens is a very well known sales consultant, who has written some books. Brilliant woman. She was actually on one of our webinars. She estimates lead gen is a $700 billion a year problem.

                  I think the way to look at that is also to say, “How big is the economy?” Let’s say the commercial sector of the economy is somewhere around $15 trillion. Say a third of that is B-to-B. That’s something like $5 trillion.

How much do you spend in sales and marketing? What’s the impact of lead generation on that spend? If it’s 10%, now you’re at a $500 billion market. That’s probably where she’s coming at it from her angle.

I think what’s clear is, if predictive lead generation delivers on the promises that we’re making, it fundamentally changes the marketing technology space.

Marketing technology spend right now, which includes CRM, marketing automation, all of these technologies like Salesforce, HubSpot, the total spending in that sector is around $25 billion to $30 billion, depending on whose numbers you’re looking at. It has been growing at about 18% per year.

If our technology works, it eliminates the need for a lot of that stuff. Do you really need to score your leads if you’re getting great ones at the top?

The whole point of scoring leads is that you’re scoring stuff in your sales funnel because you don’t know where to focus your energy. If you start with really high quality at the top, the need for that goes away.

Patrick:     As you get more customers, does the quality of the leads continue to increase?

Olin:          Absolutely.

Patrick:     Let’s say someone else has a machine-learning algorithm. What are the barriers to entry for them getting into this space and competing with you? What’s the sustainable competitive advantage?

                  Clearly, your advantage is that you’re the first mover. That’s going to help you because you’re going to have more learning earlier than other people getting in. However, are there other things? Your algorithm’s better? What are your thoughts there?

Olin:          As an engineer it pains me to say this, but the truth is, math is available to everyone. A better algorithm does not create a better business.

                  A better business is having a really happy set of delighted customers that is repeatable and growing. That gets back to, what’s the team like? Do we have a really strong team?

It gets back to, is our data better than anyone else’s data? We spend a phenomenal amount of time engineering data sources. We have created, what we believe, is a data attractor. This is the secret sauce: how do we get our data? With better data, we make better predictions.

Patrick:     This gets back to the old days. I hate to say this but I was in sales in the mid-80s, working for a big industrial company. You’re looking up SIC codes. “These customers kind of look alike.”

                  You’re basically doing the research element of it. What are the other things besides SIC codes that make these customers similar, and that’s the way you put the stuff into the system.

Olin:          Absolutely. You brought up an absolutely brilliant point. What is machine learning, you asked that earlier. A machine is just doing exactly what a human would do at machine speed and scale.

                  When you’re a salesperson, you look down and say, “What are the features that make a good customer? My good customers come from this industry. They’re this size of a company. They have this many job posting openings.”

There’s this concept in cognitive science called bounded rationality, where the human brain can only handle about seven variables in memory at any given time.

You can’t handle 20. You can certainly handle more than one or two. The number seven, plus or minus two, is what the early research started to show.

What our technology is doing is not looking at seven features. It’s looking at hundreds of thousands. It is determining which features are most important.

It’s doing it by comparing against all other features it finds from other companies that you’re selling to. It’s constantly learning and testing.

Patrick:     Even though you’re getting only a thumbs up or thumbs down, good lead/bad lead, that’s enough in that volume of leads that you’re getting a lot of learning associated with that in terms of all those different factors that are going on?

Olin:          Right. Why our technology is so sticky is, we’re better on day one, we’re amazing on day 100. It gets smarter as you go.

Patrick:     This is like A/B testing across hundreds of different variables and then continuing to refine it.

Olin:          Correct.

Patrick:     That’s very cool.

Olin:          When you said “competitive advantage,” the real competitive advantage is the data that comes out of that. We’re learning at a speed and scale that gives us that first mover advantage. Anyone else who comes along has to learn the same things we’ve learned.

                  They might learn them faster. That’s the paranoia in me. There are smart people all over the world working on this. We just happened to be the first. Our advantage right now is, move fast.

Patrick:     You’re out in the press raising money. Is this really to put the pedal to the medal and grow things from a “Let’s reach more customers” side of it, the marketing the sales arm of it, or we need more development technology? Take me through that. Where are you as a company?

Olin:          That’s a great thing. Usually a company only needs to do one thing really well to succeed. Trying to do more than one thing really well is oftentimes a recipe for disaster.

                  The reality is that for most companies to do well you have to do a couple things right. You can’t just do all one and bet the farm on that. Our use of funds is about 40% in sales and marketing. That’s the overwhelming majority. About 30% of it is around the data engineering.

Data engineering to us a little different than other types of engineering. That’s the machine learning, collecting better data sources.

We do have some engineering we want to do. We’re expanding our distribution channels. We’re augmenting the product. That represents about 20% of our spend. The rest of it, a small portion, goes to administration.

Patrick:     Okay. Terrific. Do you have a clear path to profitability?

Olin:          Yes. We were actually profitable our first full year in business.

Patrick:     That’s awesome. Congratulations.

Olin:          Thank you. It was a moment of pride for me. I thought, “Let’s grow a profitable business. Oh, let’s pivot away from that and go into something that’s not profitable.” We had that moment. We are close to profitable now.

Patrick:     That’s awesome. From an intellectual property standpoint, is it mainly the trade secret of how you collect your data? Are there patents around this? How do you protect your intellectual property?

Olin:          We have a clear path to at least six utility patents that we’ve identified. I’m not a big fan of patents. I think that the state of machine intelligence is evolving at such an exponential scale that the algorithms that work today are not likely to survive more than 6 to 12 months.

                  One of the key things we’ve done is what we call an ensemble approach. There’s no one algorithm. There are algorithms that compete with each. There are different technologies trying to solve the same thing. Then they vote on who’s more accurate. The machine literally takes its own vote internally.

I think that patents that San Diego tends to get really excited about because we are a patent wall city, with Qualcomm boasting about patents; SPAWAR, which is a very closed system, does not talk much about what they do because it’s in national security.

Whereas the Bay Area is very open. It’s not about the algorithm. It’s about the business model. It’s about solving a customer problem.

I think that we tend to be more Bay Area-like. For instance, we’ve open sourced some of our stuff on GitHub. We’re really proud of that. We have some activity on GitHub.

We are never going to expose the actual tunings and algorithms. We do a lot to protect our intellectual property, as a trade secret, but it is evolving so fast I’m not sure that spending $50,000 on a patent, when there may be a better model out two years from now, really is going to make a lot of sense.

You can’t patent data, or you can’t patent math. What you can patent is processes and methods and so forth. I think that we are evolving so quickly right now that that’s not as big a concern.

Patrick:     Okay. Interesting. What’s the exit strategy for a company like LeadCrunch? Is this something you want to take IPO and grow to a huge company or are there various different options? How do you think about that, or you’re not even thinking about that?

Olin:          I think a really good way to run a business is to run it as if you have to run it forever. The first thing is having a profitable business, where it’s really an amazing place for people to work, and attract the best talent possible.

                  It really comes down to the team. Good teams work in profitable environments that are really fun, with high productivity. Of course, I go to bed and dream every night of doing an IPO, because that’s the only outcome I can control. However, the reality is less than 1% of startups are ever going to get to an IPO.

Most exits happen early. Most exits are what we call trade sales, where one company buys another. In our space, they’re buying them up left and right. We just had a competitor taken out last week in an acquisition.

We had an acquisition offer very early. We said, “No, it’s too early. We’re playing for a bigger ballgame.” How big of a ballgame? That’s something that’s really a matter of economics.

Patrick:     Interesting. Obviously, you’re one the more seasoned entrepreneurs out there. You have a lot of battle scars. For our audience of entrepreneurs out there, any key insights?

Not only from the current LeadCrunch experience, but what are the one, two, three things you think are the most valuable things for the entrepreneurs out there based on your experience?

Olin:          That’s a great question. The reason I pause is there are a lot of things. I think one of the most important things, it’s a buzzword right now, is grit.

                  I just finished reading Angela Duckworth’s book, Grit. That’s the idea. Persevere: it truly overcomes everything.

The reality is, there are far easier ways to make a living than being an entrepreneur. There are very few ways that are as rewarding. Entrepreneurship is one of the most rewarding, fun things you can do.

I think that if there’s one thing anyone wants to learn to be a successful startup is how to cultivate the right kind of grit, to know when to persevere and when to pivot. Those are very important things and there’s a lot of wisdom involved in there.

I think that my success is really a result of the team. It’s not about the CEO. It’s not about the founder. It’s about getting the ball rolling and how that snowball gathers mass on its own.

That’s really about those key early partners in the company. I call them partners. We spend a lot of time on culture. A local entrepreneur who gave me some great advice, this guy by the name of Aaron Fulkerson, CEO of MindTouch, spent a lot of time on culture.

We took some of his and took some of our own. I think the most successful startups are the ones that have a strong sense of identity and culture and are doing something that is truly purposeful.

They believe in what they’re doing. Otherwise, why would you put that much energy into it? As a person, I don’t think that money is particularly good motivator. I think ownership’s a great motivator. I think that owning a piece of your future is really key.

Patrick:     Absolutely. I said, “What’s the definition of a small company versus a big company?” In a small company, you have one person doing six jobs, and in a big company, you have six people doing one job.

                  The other part of it is your ability to influence moving the needle at a corporate level in a smaller organization. Sometimes you’re in these big organizations and you don’t have that.

It’s not only having the equity piece of it, it’s also “Hey, my work is meaningful. I’m actually moving the needle. I’m actually moving the ball forward. I can see how what I’m doing here is impacting the company.” It’s a very cool aspect of being in startups.

Olin:          I’d like to disagree with you on that point. I think there’s a much more profound difference between an established company and a startup.

                  The startup is seeking a repeatable scalable business model. It is an experiment. It is science. You don’t know whether it’s going to work. You have a hypothesis and a bunch of assumptions, and you’re throwing money and time at it to see if it works out.

In a big company, you know what works. You get a paycheck. You get a 401K plan. Why? Because you know you’re going to have sales next quarter. Small businesses are their own little thing. They’re not a big company. They’re in-between.

The challenge for most small businesses is they’ll never have the chance to scale. The worst thing that’s going to happen to a startup is it turns into a small business. The best thing is it turns into an enterprise.

That’s why startups take the risks they take and why they try to solve those big, audacious problems. They have a better view, or they think they have a better idea, of how to solve a big problem than a big company does.

Patrick:     That’s what you’re looking for in your employees. You’re looking for people who want to have that bigger vision. They want to take not nothing into something, but nothing into something huge.

Olin:          Right. They have to be willing to fail. What was the thing that really turned me off about working in national security is, first off, the number of acronyms. It’s just unbelievable.

You ask people what does the acronym mean, they don’t know. “That’s what we call it.” Usually it has to do with how they get paid through some government program. There’s no existential threat. They’re going to have their job tomorrow, regardless of what they do.

In a startup, there’s non-stop existential threat. You are on the savannah and lions are coming for you. You will figure out how to build the spear quickly or you will, as a company, die.

I really like that type of adrenaline rush. I like that thing. Someone once said that starting a company is like jumping out of an airplane, inventing the parachute, and chewing on glass as you’re falling.

That’s kind of true in a way. I think that we look for people who have very strong skills in their respective domain, very much willing to try and fail and get up and try and fail and get up until they get success.

That process may take a while. What they get as part of our team is one of the cornerstones of our culture: listening. When you join our team, we want you to come with ideas. You’re going to listen to our ideas and we’re going to listen to your ideas.

We’re not going to agree. However, at the end of the day, we’re going to make a decision, and as a group, we’re going to go down that path. We’re going to hold hands jumping off that cliff and we’re going to invent that parachute as we’re going down.

So far, that’s worked pretty well for us. I’m very proud of the quality of people who we’ve recruited. We’ve not had trouble recruiting high caliber engineers, which a lot of companies in the Bay Area do.

The secret of San Diego, what’s great about it, is you get amazing talent at the engineering level that is very high retention. You don’t have that turn over the way you do in the Bay Area.

Patrick:     Yes, for sure. This has been awesome. Thanks a lot for doing this. I’m super excited about it. I’ll be following LeadCrunch very closely.

Olin:          Sign up for a free trial at www.leadcrunch.com I have to give you a plug.

Patrick:     Terrific. Thanks, Olin.

Olin:          Thank you so much. I appreciate it.

Patrick:     This is Patrick Henry, the CEO of QuestFusion, with the Real Deal…What Matters.       

This article originally appeared on www.QuestFusion.com.

Patrick Henry
Written by

Patrick Henry, CEO of QuestFusion, former CEO of Entropic Communications, entrepreneur, executive, father, and freelance blogger living the luxury and active lifestyle in San Diego.

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