Zoominfo Apollo LLC

Everstring Innovation Technology

Amit Rai

Hua Gao

Yi Jin

Vincent Yang

Rakesh Gowda

Joseph James Kardwell

An automated predictive analytics system disclosed herein provides a novel technique for industry classification. In one implementation, the analytic system provides a method of matching company names, the method including generating a provider-side company dataset, for each record in a client's company records, identify a list of match candidates from the provider side company dataset that has overlapping company signature fragments, for each client-side company, compute match scores against the list of provider-side match candidates on each signature fragment and form an overall match score, for each client-side company, use the match scores to rank candidates and keep a predetermined number of top predetermined matches, and display the predetermined number of top predetermined matches using a user interface.

Keyword and business tag extraction

An automated predictive analytics system disclosed herein provides a novel technique for industry classification. Leveraging specific API to construct a database of companies labeled with the industries to which they belong, the automated predictive analytics system trains a deep neural network to predict the industries of novel companies. The automated predictive analytics system examines the capacity of the model to predict six-digit NAICS codes, as well as the ability of the model architecture to adapt to other industry segmentation schemas. Additionally, the automated predictive analytics system investigates the ability of the model to generalize despite the presence of noise in the labels in the training set. Finally, the automated predictive analytics system explores the possibility of increasing predictive precision by thresholding based on the confidence scores that the model outputs along with its predictions. The automated predictive analytics system finds that the approach yields six-digit NAICS code predictions that surpass the precision of gold-standard databases.

Automated industry classification with deep learning

An automated predictive analytics system disclosed herein provides for generating sales leads with lead engagement recommendations. In one implementation, the system determines similarities between fitness, engagement, and intent characteristics of a plurality of target clients and fitness, engagement, and intent characteristics of an entity's existing clients. Subsequently, the system generates recommendations for engagement with the plurality of target clients, wherein components of the recommendations for engagement are based on determined similarities between the fitness, engagement, and intent characteristics of the plurality of target clients and the fitness, engagement, and intent characteristics of the entity's existing clients. The system presents the plurality of leads with the recommendations of engagement to using a graphical user interface (GUI) at the application layer of the system.

Predictive analytics for leads generation and engagement recommendations

An automated predictive analytics system disclosed herein provides a novel technique for industry classification. In one implementation, the analytic system provides a method of matching company names, the method including generating a provider-side company dataset, for each record in a client's company records, identify a list of match candidates from the provider-side company dataset that has overlapping company signature fragments, for each client-side company, compute match scores against the list of provider-side match candidates on each signature fragment and form an overall match score, for each client-side company, use the match scores to rank candidates and keep a predetermined number of top predetermined matches, and display the predetermined number of top predetermined matches using a user interface.

Automated company matching

A system to extract relevant keywords or business tags that describe a company's business is provided. The keyword extraction system utilizes a smart crawler to identify and crawl product pages from a company's website. These pages serve to provide textual descriptions of product offerings, solutions, or services that make up the company's business. The keyword extraction system combines these web documents with other textual descriptions of companies, e.g. from third party data vendors or other public data sources and company databases, to form a corpus of documents that describe companies. The corpus of documents and keywords are processed to segment the plurality of companies into subsets by applying a clustering technique and to provide visualization of the clusters with business tags.

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Danville, CA, United States of America

Amit Rai