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Demandbase and Spiderbook: A Match Made in ABM Heaven

Spiderbook Demandbase

We all talk about the importance of customer centricity in the sales cycle, but the reality is that typical revenue funnels, from Marketing to deal close, are misaligned and ineffective with 99% of the targets being lost along the way. As a result, your next B2B buyer is lumped together with the other 99% of bad leads who will never buy, and so they are relegated to the same low quality experience with your brand as everyone else.

 

Your next B2B buyer can’t be on a generic assembly line with a 0.1% prospect-to-deal conversion rate. Your next buyer belongs in a curated experience tailored to their specific business requirements and pain points. We have all been buyers, and we want to work with people and brands who know us, can anticipate our needs, and can help us achieve our goals and business objectives. We want this experience, not just on the web, but across all channels: digital marketing, in sales conversations, at events, with customer success, and everywhere else we interact with the brand. This is the future we all want as buyers. Together with Demandbase, we are inventing that future.

 

The Solution

Enter Spiderbook. Our solution, although technologically Herculean, is conceptually straightforward. We’ve built a system that replicates the intuition and knowledge of a successful strategic account executive who knows the account intimately through years of working with them. You cannot put your rock star sales executive on every account, and not every account can have someone with five years experience and expert-level industry knowledge. Instead, we’ve automated some of the best account executive practices, such as knowing the right account to pursue, identifying the buying team at the account, having high quality sales conversations as the deal progresses, and leveraging existing relationships to get the deal signed. Spiderbook works because we are able to automate all of this at massive scale. We use data science to read the entire business Internet, billions of web pages, SEC filings, social posts, all the signals that quantify spend, business fit, trust, and cultural match. But discovering the perfect account only matters insofar as sales can act on it. Thus, Spiderbook goes deeper into the sales funnel to consider if the individual buyers can be reached with a personalized message, did they go to the same school as the salesperson, are there any common interests and intelligence that sales can use to talk to them. The goal is to simulate the intuition and experience of a good strategic account manager in every way.

But Spiderbook has only invented half of the future; Demandbase has created the other half.  

 

What Is Now Possible?

Spiderbook + Demandbase is the world’s first end-to-end Account-Based Marketing Platform that spans from account identification all the way to deal close, all while providing a consistent brand experience. It incorporates discovering and continuously refining in-market accounts, targeted ads, and a personalized online experience on the web and chat. It crosses over to Sales by automatically identifying the complete buying team and provides personalized messaging to engage them effectively. Finally the solution provides continuous account insights to maximize the quality of sales conversation during the sales cycle and even beyond.

 

How We Got Here

Even before we met Demandbase, our first large customer, Host Analytics, started taking our target-accounts and buyer profiles from Spiderbook and using Demandbase to reach them. The output from Demandbase would then flow back into Spiderbook for Sales to act on. The Spiderbook/Demandbase partnership was a no-brainer, but we both realized that the best B2B buying experience could only be achieved with a single fully-integrated solution.

 

The First End-To-End Platform

There are many ways to look at our merged solution. It is a demand generation leader’s ability to target those “whale” accounts with an end-to-end marketing effort. It is an SDR tool that leads to 5-10x more responses from prospective customers. It is something your Account Executives take with them into meetings and helps them close more deals. But most importantly, it is a combined solution for the B2B buyer.

 

The typical B2B buyer does not always fit into the simplistic patterns that lead scoring tools look for. Together with Demandbase we are inventing the experience B2B businesses want to provide to their valued accounts and buyers. Most importantly, we are delivering the world’s first end-to-end ABM Platform that spans from account identification to deal close, all while delivering a consistent brand experience.

 

To learn more, read the press release.

 

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How Spiderbook Learned to Love the Network

Networks are a mess. Often, the basic properties of how things are connected to one another are well understood, but the result of these connections is massive complexity. And yet, networks can be the most effective way to model a complex system. The observed behavior of such a system can often be reduced to the interactions between more simple entities. For example, whether or not a hashtag will go viral on Twitter largely depends on the size of the follower network of the users who tweet it. Also, a single power plant failure can cause a cascading blackout across several states, and this can only be explained through the network effects of the power grid. And protein interaction networks are a vital tool in making sense of the Human Genome.

The interactions between businesses form another complex network. The actions of one business will change the behavior of many other (seemingly unrelated) businesses in surprising ways. Every business is connected to each other through a network of their interactions. In many ways, business deals are actually new connections being added to the network. One company might be partnered with another company, who is then a supplier to yet another company, who is then in direct competition with another still. In fact, the most important events that a company goes through are actually direct changes to their local network: a new partnership, a lawsuit, and an acquisition are all new connections being made between businesses.

Spiderbook was founded on the observation that the best B2B salespeople use the network of companies to their advantage. When they are looking for their next deal, they might target the customers of their competitors, or they may use a mutual customer as a reference in their initial outreach. In both cases, the salesperson is using the existing connections between their company and the prospective customer to get the deal signed. Our hypothesis was that existing company connections are key in creating new customer connections. In fact, recent studies show that 84% of all B2B decision makers rely on referrals in their buying process. So in order to predict which business you should sell to next, we built a network of a million companies and 10 million connections between them. Building this network, however, was no small task. We had to invent a state-of-the-art natural language processing engine that can identify connections between companies being described in billions of documents (press releases, news articles, blogs, job posts, and everything else online) every month.  Our engine learns industry-specific language in order to work for all companies and industries. It also understands important properties of the connections it identifies, such as the timing of when the connection was created as well as the decision makers at each company involved in the connection.  The result is a company network with millions of connections that gives us a global view of the interactions between businesses.

The idea of mutual connections being predictive of new connections appears in other types of networks as well. Facebook uses the number of mutual connections in the friendship network to recommend new friends to you – the more mutual friends you and another person share, the more likely you are to know that person. In fact, we did an apples-to-apples comparison between the company network we constructed and the Facebook friendship network. The figure below plots the number of mutual connections between any two companies/Facebook users in the network versus the probability that they are connected. What it shows is that mutual connections are even more predictive of a connection between companies than it is for friendship between people.


mutual connections

 

This observation led us to the discovery that identifying your next customer is actually a link prediction problem. Link prediction algorithms use machine learning and other techniques to predict the creation of new connections in a wide range of networks. We can leverage these techniques to predict customers. The resulting algorithm is a machine learning classifier that uses over 200 different inputs, not just mutual connections. Some of these inputs are simple, like revenue or geography, but others are the results of network analysis algorithms:

  • Personalized PageRank, which is a variation on Google’s original PageRank algorithm, is used by social networks to recommend interesting content to its users.  It is a measure of how often one user will land on another user’s social profile by clicking through the network randomly. We use it to measure how likely two companies are to interact with each other if they randomly explored their network connections.
  • Matrix Factorization is used by many recommendation engines. Netflix, for example, can use it to find underlying patterns in movie watching data.  The technique makes the assumption that the movie tastes of most Netflix users are governed by a small number of dimensions, such as how much they like romantic comedies or if they prefer Seth Rogan movies. If you like Quentin Tarantino movies and love Westerns, it is a reasonable prediction that you’ll like Django Unchained.  Matrix factorization determines what these hidden dimensions are and how they apply to each user. We apply the same mathematics to discover the hidden dimensions that govern company connections.  These dimensions might include a business’s likelihood to buy from Silicon Valley startups or whether or not a business outsources their marketing.

These mathematical tools offer a global perspective on company connections.  Because we have the company network, our algorithm can ask how well a prospective customer connection fits into the entire network.

The network-based approach also allows the algorithm to look at both sides of the customer connection. The company network contains all of the companies that your prospective customer has bought from in the past. The algorithm, for example, compares your prospective customer’s revenue to your existing customers, but equally importantly it compares your revenue to the prospect’s existing suppliers. If the prospective customer doesn’t typically buy from companies as small as yours, or if they don’t like to do business out of state, then it doesn’t matter how much they look like your ideal customer. The company network gives a uniquely broad perspective on your would-be deal.

The network-based model that chooses which companies to sell to is one of many machine learning models that make up the Spiderbook platform. Identifying the prospective customer’s budget for your product, the right decision makers, and customized messaging all involve separate but equally in-depth machine learning models. The network, however, allows all of this analysis to focus on key companies that have a high likelihood of a customer connection.  As they have done in so many other applications, networks have provided a much more clear and actionable perspective on how connections between businesses are formed.

– Seth Myers

 

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The big data market – A data-driven analysis of companies using Hadoop, Spark, data science, and machine learning.

Hadoop is the flagship of the much-hyped “big data” revolution, comprising of a host of different technologies. While there are many alternatives and variants, including Cloudera, Hortonworks, Amazon EMR, Storm, and Apache Spark, Hadoop as a whole remains the most-deployed and most-discussed big data technology…. Read more

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Spiderbook Redefines CRM, Creates 10x More Accurate Customer Relationship Predictor

Top-performing sales people spend a lot of time gathering information to get to know their prospects and their prospects’ businesses. They carry out background research – on Linkedin, Twitter, community forums, company websites, news articles and the list goes on – to understand the company, the department, and the people they hope to build a relationship with. Many use CRM (customer relationship management) tools to handle the routine tasks associated with the sales process.

Unfortunately, while CRM solutions are good for tracking the progress of a sale, they are inept when it comes to actually help close the deal. Even if a sales rep can adequately manage all of their tasks, there is still too much content for one person to digest and use. But, what if they had a system that automatically processed all of the deal-closing business intelligence and served it up in an easy-to-use interface?

Spiderbook, a start-up headquartered in San Francisco, was founded by Aman Naimat and Alan Fletcher to solve those problems. If the adoption rate for their service is any indication, all signs point to a rousing success.

Aman and his team of three fellow NLP developers built SpiderGraph, which uses AlchemyAPI’s Keyword ExtractionEntity Extraction and Language Detection REST APIs to forge business intelligence based on everything from the public-facing records like press releases, websites, blogs, PR and digital marketing content to private business profiles accessed through partnerships with data services providers.

“We go beyond traditional CRM by using natural language processing and named entity recognition to understand businesses,” Aman explains. “We are curious to know how they partner, details on acquisitions, the products they sell, branding, SEC listings and even the types of resources that they look for in job posts.”

Spiderbook’s story describes how the team at Spiderbook is seeking to change the way sales people “connect the dots among companies, people, partners, products and documents.”

– by AlchemyAPI

Originally posted in: http://www.alchemyapi.com/blog/next-generation-sales-intelligence/

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