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Spiderbook’s SpiderGraph: Linking Datasets To Help You Sell Better

By   /  July 7, 2014  /  No Comments

spiderpix1by Jennifer Zaino

Startup Spiderbook, which is building a linked dataset of companies and their partners, customers, suppliers, and people involved in those deals, has recently closed its seed round for $1 million. The next-generation sales intelligence company was co-founded by CEO Alan Fletcher, who was a vp of product engineering, IT and operations at Oracle, and Aman Naimat, who has been working in the realm of CRM software since he was 19 years old and also has a background in natural language processing. Along with other core members of the team, the company puts natural language processing and machine learning technology to work to help sales people better connect the dots that explain business relationships, extracting information from unstructured text to sell more effectively.

State-of-the-art CRM, says Naimat, by itself doesn’t help salespeople sell. Since the days of Salesforce, which he worked on at IBM and Oracle, it has remained the same thing, he says, “just evolving with better technology. But basically it is an internal-facing administration tool to give management visibility, not to help a salesperson sell or create business relationships.”

Built from billions of data elements extracted from everything from SEC filings to press releases to blogs to Facebook posts, Spiderbook’s SpiderGraph is taking on that challenge, starting with the goal of helping salespeople understand who is the right contact to talk to, how he or she can meet that person (through shared contacts, for instance), and who competitors are, including those providing technology or other products already in use at the company. “We have created a graph of customers, competition, and suppliers for every company that is all interconnected,” he says.

“We want to be a sales intelligence platform that tells you who you will sell to, how to reach them, and when and what conversations to have when you get there. We want to give you a map on how to really sell to a company using this network.”

Doing this research on their own — the “black art” of prospecting, as he calls it — currently can consume 50 percent of salespeople’s time, as they manually delve through things like 400-page SEC filings to discover who a potential customer’s main provider currently is, or what partnerships they might want to take on to win new business. “That could take months,” he says.

Spiderbook actually began life as a political science project that Naimat undertook when he went back to school for his PhD at Stanford. He and another PhD student who also studied social networks wanted to employ NLP to research these networks’ impact on political engagement, and use that to build a platform for lobbying. The thesis was to crawl the networks, and extract information from them using NLP to predict who was most likely to participate in lobbying in the city of Palo Alto. “I figured from that network that if some companies are connected to the city, they are more likely to be more connected to city politics,” he says. “Social dynamics are very interesting for business relationships, and the idea was, can we build and utilize a network to see what impact it can have on sales, business development, partnerships, acquisitions, and so on.”

That started the year-long mission of building a network of every company in the world, their partners, customers, suppliers, competitors, and the people and products involved in those deals, with the information stored in a Neo 4j graph database. Spiderbook’s technology reads everything on the Internet, using NLP to extract structured relationships from publicly available and private sources. It also has built confidence rankings from that data to predict the likelihood of whether there is a business relationship to exploit between companies. The company uses the snap analytics technology, which leverages a probabilistic machine learning model, developed by its data scientist Seth Myers to predict potential new deals. “We first learned the language of how business relationships are expressed using machine learning models then used machine learning models to predict the future edges of the network,” says Naimat. “It’s a little different from typical machine learning because it is machine learning over a network of graphs.”

While right now it is focused on making connections of companies linked to people, people linked to deals, and deals linked to business drivers to find new contacts, identify influencers and be prepared against competitors in order to help salespeople sell better, it sees lead discovery, investments, references and buy side opportunities ahead. Think of what Spiderbook does, he says, as a LinkedIn at the business sales level. It will be able to help to lead, for example, a startup that sells to Mercedes Benz and four other auto companies in Germany to its next customer – which is likely to be a key supplier in the arena, and then find out the details on who to sell to there, that person’s summary, and other important information . “Two-thirds of all B2B deals come from existing customers and partners,” he says. “You can just tell us who you are and we tell you who is most likely to buy your service.”

Right now users have to search for what they are selling or what department to sell to, but integration with CRM solutions is something Naimat is aiming for, both to be more prescriptive and to populate CRM systems’ account plans with customized asset plans of how to sell into a business. The company has been in beta with the product since January, and counts 5000-plus engaged users. From selling to individual reps, it wants to expand across sales teams and convert from there to enterprise deals.

About the author

Jennifer Zaino is a New York-based freelance writer specializing in business and technology journalism. She has been an executive editor at leading technology publications, including InformationWeek, where she spearheaded an award-winning news section, and Network Computing, where she helped develop online content strategies including review exclusives and analyst reports. Her freelance credentials include being a regular contributor of original content to The Semantic Web Blog; acting as a contributing writer to RFID Journal; and serving as executive editor at the Smart Architect Smart Enterprise Exchange group. Her work also has appeared in publications and on web sites including EdTech (K-12 and Higher Ed), Ingram Micro Channel Advisor, The CMO Site, and Federal Computer Week.

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