“Bootstrapping” is a funny term. In the context of startups, it means to proceed without outside funding. But of course it derives from the phrase “to pull oneself up by one’s bootstraps.” More generally, it's about lifting oneself up.
In one sense, Relationship Science is the furthest thing from a bootstrapped company. To date, the young company has raised almost $90 million from some of the country’s biggest investors; to hear founder Neal Goldman tell it, he was practically bullied into accepting millions more than he’d sought. Financially, “RelSci” didn’t need to reach for its boots. People were throwing their own boots at it—$13,000 Luccheses.
In that more general sense, though, RelSci did bootstrap. As Goldman explains, the company’s proof of concept was itself. The very aim of its product—a kind of LinkedIn on steroids—was to help businesses add clients. And like any young business, RelSci, which was founded in 2010, needed to add clients. “We used the system ourselves,” says Goldman, who will sign his 250th client this month, and currently adds about two a day. “We’re a living example that this works.”
Goldman would probably object to the LinkedIn shorthand above. (He says he admires LinkedIn, but doesn’t consider it a competitor.) Like LinkedIn, though, RelSci is at its heart a database intended to help networking and business development. But there are a few crucial ways in which RelSci differs from its (non)-competitor.
LinkedIn boasts over 200 million members. RelSci, meanwhile, has about 2.5 million entries in its database, most of whom are people, but some of which are companies and universities. So why do RelSci’s clients pay a minimum of $3,000 annually—and in some cases, far more—to access RelSci’s database? (A “client,” often, is an organization that might pay that fee a hundred times over for many employees to access it.)
RelSci presents data on what Goldman calls the “most active, influential people across business, finance, investing, and politics,” he says. They control the purse strings, often of massive purses.
And not all of them are on LinkedIn to begin with. Goldman estimates that over half his database doesn’t have any LinkedIn presence at all. “I’m not on LinkedIn,” he says. “There’s a whole range of people buying high-end goods and services who are not on LinkedIn.... There’s a whole universe of people that are running businesses who are not on LinkedIn.” He conjures an example to make things vivid. “The CFO of the Chicago Board Options Exchange is not on LinkedIn. He’s buying millions of dollars' worth of legal services, millions of dollars' worth of accounting and consulting services.”
In fact, that CFO would appear to have a LinkedIn profile, albeit one so minimal that you suspect someone else made it for him. Which brings us to the second way RelSci is fundamentally unlike LinkedIn: It goes far beyond listing straight professional connections and employment history, instead modeling the widest range of relevant information about a person or institution you might want to do business with.
On LinkedIn, you might learn about John Smith’s last few jobs. You'll probably find that on RelSci, too. But on RelSci, you may also learn: people related to John Smith by marriage; people known to be friends with Smith (caught in a photograph together by a party photographer, say); people who’ve invested together with Smith in a deal; what political candidates Smith has given to; what charities he’s given to; what memberships he has; what issues interest him; and whether he was a member of a fraternity. (The same goes for companies, organizations, hedge finds, and universities. “We basically modeled out the world,” says Goldman.)
It might seem a little stalkerish, but all this data is gleaned from publicly available information. Often, data is stripped from sources algorithmically, then tidied up manually. (A script run on SEC documents, for instance, can pull out names and numbers, but you’re going to want have some human eyes on that data before it’s fed into the system.) “We integrate that data into one very clean and very precise database,” says Goldman. “It takes lot of time and people and money, frankly, to build this monster of a product,” he says, likening its precision to that of a Bloomberg terminal.
Once you have access to this wealth of information about decision-makers, it helps you strategize your approach. You can figure out who can broker an introduction, and you can make an educated guess of what to say to ingratiate yourself once the introduction is made. Even though you stalked a bit, it’s up to you to bring the social grace at the offline summit. “We bring the science, you bring the art,” says Goldman, in a nod to his company’s name.
Goldman’s aims are grand; he sees his database as a utility that as one business adopts, all competitors will need to remain competitive. For the time being, though, Goldman continues to win clients one at a time—again, by reaching down toward what now might be termed his 90 million dollar pair of boots.
At a recent conference, Goldman had his sights set on meeting with a big real-estate mogul. Goldman plumbed his own database and was able to conjure an email introduction. Sitting down to a demonstration, the mogul couldn’t disagree when Goldman said, “The proof of the pudding is that I’m sitting here talking to you.”
He won the client.
[Image: Flickr user Shoothead]