  For sales and marketing organizations within large enterprises, identifying
and understanding new markets, customers and partners is a key challenge.
Intel's Sales and Marketing Group (SMG) faces similar challenges while growing
in new markets and domains and evolving its existing business. In today's
complex technological and commercial landscape, there is need for intelligent
automation supporting a fine-grained understanding of businesses in order to
help SMG sift through millions of companies across many geographies and
languages and identify relevant directions. We present a system developed in
our company that mines millions of public business web pages, and extracts a
faceted customer representation. We focus on two key customer aspects that are
essential for finding relevant opportunities: industry segments (ranging from
broad verticals such as healthcare, to more specific fields such as 'video
analytics') and functional roles (e.g., 'manufacturer' or 'retail'). To address
the challenge of labeled data collection, we enrich our data with external
information gleaned from Wikipedia, and develop a semi-supervised multi-label,
multi-lingual deep learning model that parses customer website texts and
classifies them into their respective facets. Our system scans and indexes
companies as part of a large-scale knowledge graph that currently holds tens of
millions of connected entities with thousands being fetched, enriched and
connected to the graph by the hour in real time, and also supports knowledge
and insight discovery. In experiments conducted in our company, we are able to
significantly boost the performance of sales personnel in the task of
discovering new customers and commercial partnership opportunities.
