Spiderbook: Redefining CRM by Creating a 10x More Accurate Customer Relationship Predictor Using...

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An IBM Company Case Study Spiderbook Spiderbook Redefines CRM, Creates 10x More Accurate Customer Relationship Predictor Using AlchemyAPI “At Spiderbook, we go beyond traditional customer relationship management (CRM) by using natural language processing (NLP) and named entity recognition to understand businesses,” explains Aman Naimat, co-founder of Spiderbook. “We are curious to know how they partner, details on acquisitions, the products they sell, branding, SEC listings and even the types of skills that they look for in job posts. That’s the kind of information that makes a difference to our customers.” Naimat’s path to Spiderbook began when he graduated from Stanford with an MS in Computer Science, augmented with a focus on NLP. He spent time as an architect for both IBM SuperSell Enterprise and Oracle CRM. He was also the Director of Special Projects for the CEO’s office at Oracle and Senior Director of Product Management in the Oracle database group. That experience, along with the entrepreneurial spirit that’s endemic in Silicon Valley (Spiderbook is headquartered in San Francisco) brought Naimat together with fellow co-founder and CEO, Alan Fletcher. They recently closed funding that will help the company keep pace with user demand. Adoption has been exponential since releasing SpiderGraph in January 2014, currently reaching 10,000 users. The Challenge: Processing all of the content available to provide deal-closing business intelligence While traditional CRM systems help salespeople manage a sale, those task managers really do nothing to close deals. Spiderbook, on the other hand, automatically reads all of the information a salesperson needs to identify ideal prospects, providing business intelligence that significantly increases the odds of a mutually advantageous, “two-way match” between vendors and customers. “The problem I ran into was that most NLP and named entity recognition algorithms were developed using pristine data sets, hand-curated for test suites,” Naimat says. “Those algorithms cannot accurately analyze the content you find on the Web, which is not perfectly written articles, blog posts or tweets.” That content is also what salespeople need to painstakingly comb through to understand their prospects. As Spiderbook says on their website, it’s overwhelming and extremely difficult for people to “connect the dots among companies, people, partners, products and documents.” Company Profile: CRM/Sales intelligence NLP Problems: Accuracy in content analysis Analyzing badly written or non-standard format content Capacity to handle Big Data AlchemyAPIs Used: Keyword Extraction Language Detection Entity Extraction Volume of Content Analyzed: 756 terabytes regularly Type of Data Analyzed: Public online data: website content, PR, marketing materials, social media, SEC filings, LinkedIn, SlideShare Business profiles through partnerships with data services providers Result: SpiderGraph, a unique business intelligence service 10X more accuracy for predicting business dynamics than lead ranking Exponential user adoption; $1 million seed funding

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Spiderbook, a CRM and sales intelligence company, sought to build a customer relationship discovery app that stood out above the rest. They were able to with the help of AlchemyAPI.

Transcript of Spiderbook: Redefining CRM by Creating a 10x More Accurate Customer Relationship Predictor Using...

Page 1: Spiderbook: Redefining CRM by Creating a 10x More Accurate Customer Relationship Predictor Using AlchemyAPI

An IBM Company

Case Study Spiderbook

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

“At Spiderbook, we go beyond traditional customer relationship management (CRM) by using natural language processing (NLP) and named entity recognition to understand businesses,” explains Aman Naimat, co-founder of Spiderbook. “We are curious to know how they partner, details on acquisitions, the products they sell, branding, SEC listings and even the types of skills that they look for in job posts. That’s the kind of information that makes a difference to our customers.”

Naimat’s path to Spiderbook began when he graduated from Stanford with an MS in Computer Science, augmented with a focus on NLP. He spent time as an architect for both IBM SuperSell Enterprise and Oracle CRM. He was also the Director of Special Projects for the CEO’s office at Oracle and Senior Director of Product Management in the Oracle database group. That experience, along with the entrepreneurial spirit that’s endemic in Silicon Valley (Spiderbook is headquartered in San Francisco) brought Naimat together with fellow co-founder and CEO, Alan Fletcher. They recently closed funding that will help the company keep pace with user demand. Adoption has been exponential since releasing SpiderGraph in January 2014, currently reaching 10,000 users.

The Challenge: Processing all of the content available to provide deal-closing business intelligenceWhile traditional CRM systems help salespeople manage a sale, those task managers really do nothing to close deals. Spiderbook, on the other hand, automatically reads all of the information a salesperson needs to identify ideal prospects, providing business intelligence that significantly increases the odds of a mutually advantageous, “two-way match” between vendors and customers.

“The problem I ran into was that most NLP and named entity recognition algorithms were developed using pristine data sets, hand-curated for test suites,” Naimat says. “Those algorithms cannot accurately analyze the content you find on the Web, which is not perfectly written articles, blog posts or tweets.” That content is also what salespeople need to painstakingly comb through to understand their prospects. As Spiderbook says on their website, it’s overwhelming and extremely difficult for people to “connect the dots among companies, people, partners, products and documents.”

Company Profile:•CRM/Sales intelligence

NLP Problems:• Accuracy in content analysis•Analyzing badly written or non-standard format content•Capacity to handle Big Data

AlchemyAPIs Used:•Keyword Extraction• Language Detection•Entity Extraction

Volume of Content Analyzed:• 756 terabytes regularly

Type of Data Analyzed:•Public online data: website content, PR, marketing materials, social media, SEC filings, LinkedIn, SlideShare•Business profiles through partnerships with data services providers

Result:•SpiderGraph, a unique business intelligence service• 10X more accuracy for predicting business dynamics than lead ranking•Exponential user adoption; $1 million seed funding

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An IBM Company

Case Study Spiderbook

The Solution: A predictive customer relationship discovery app that mines the entire Web: SpiderGraphNaimat’s experience with NLP and knowledge of machine learning advances sparked an idea that he could create sales applications to build unique and useful prospect profiles at a scale that people can’t – and launch a business to bring the applications to market. The primary challenge was to find an NLP service that enabled highly accurate results and could handle the big data associated with crawling the Web. “I tested a dozen different NLP parsers only to find that most of them were abysmal, but AlchemyAPI’s services outperformed its next closest ‘competitor’ by 20 to 30 percent.”

Naimat and his team of three NLP developers then built SpiderGraph. It uses AlchemyAPI’s Keyword Extraction, Entity Extraction and Language Detection REST APIs to forge business intelligence based on everything from public resources like press releases, websites, blogs, PR and digital marketing content to private business profiles accessed through partnerships with data services providers. On top of this proprietary knowledgebase, Spiderbook has developed an engine that can predict who is most likely to do business with you.

But what sets SpiderGraph apart is the information it amasses on a company’s business culture, including its partners, suppliers, investors, buyers and even whom they have litigated with along with the people and products involved in those deals. With that knowledge, marketing campaigns and sales activities are 10x more effective for Spiderbook users.

From an application developer’s perspective, Naimat says, “If you can program in Excel, you can use AlchemyAPI.” He also believes that the majority of next-gen applications will be data-driven. And because so much of the world’s data is unstructured, you need an infrastructure that enables you to focus on applying your domain knowledge, as opposed to building databases and web crawlers. As Naimat puts it, “If you are really trying to build applications with commercial value, you need to leverage the best quality service. AlchemyAPI gets better every day.”

The Value: Speed is great, accuracy is paramount – AlchemyAPI delivers both“What really matters is accuracy and precision. If accuracy is poor, it doesn’t matter how fast the service is,” explains Naimat. “AlchemyAPI is the best. They have a deeper understanding of language, entities and topics than anybody else I have seen. Of course, their speed is also great.”

Spiderbook is the only enterprise that offers a unique set of business intelligence data that predicts prospect behavior. Spiderbook uses their vast knowledgebase to forecast who will partner with whom and which companies will transact

“AlchemyAPI is the best. They have a deeper understanding of language, entities and topics than anybody else. Alchemy gets better every day.”

Aman NaimatCo-founderSpiderbook

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An IBM Company

Case Study Spiderbook

business with “10X more accuracy than any lead ranking systems.” The value for Spiderbook’s users? Less time researching for valuable conversation starters and more time discussing real business problems with potential customers.

ConclusionSales and marketing initiatives are becoming increasingly difficult. Not only because competition is greater, but also because the volume of content now available is overwhelming. Decision makers on both sides of the table are constantly bombarded with messaging.

However data-driven applications offer a solution. By taking advantage of high quality NLP algorithms offered through easy-to-use APIs, application developers can rapidly bring to market solutions that accurately parse big data, including all online content.

Spiderbook’s SpiderGraph is transforming sales CRM, shifting it from a tool for managing sales tasks to customer relationship discovery. Powered by AlchemyAPI, SpiderGraph is enabling users to derive business intelligence from unstructured data, to the benefit of buyers and vendors alike.

About AlchemyAPIAlchemyAPI’s mission is to power a new generation of smart applications that understand human language and vision by democratizing breakthroughs in deep learning-based artificial intelligence. Our easy-to-use, high-performance cloud services for real time text analysis and computer vision give companies the intelligence needed to transform vast amounts of unstructured data into actions that drive their business. AlchemyLanguage™ is the world’s most popular natural language processing service; AlchemyVision™ is the world’s first computer vision service for understanding complex scenes. AlchemyAPI is used by more than 40,000 developers across 36 countries and a wide variety of industries to process over 3 billion texts and images every month. For more information, visit our website at www.alchemyapi.com.

AlchemyVision is a registered trademark of AlchemyAPI, Inc. in the United States and/or other countries. *Other names and brands may be claimed as the property of others. To learn more about our company and services, please call us at 1-877-253-0308 or email [email protected].

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