Keep pace with your customers and a step ahead of the competition with continuously refreshed data and intelligence at your fingertips. Built for today’s fast-paced business climate, InsideView harnesses millions of individual data points and signals, turning them into the industry’s most reliable, actionable, real-time data and insights. No more stale data, manual data entry, periodic updates, or list purchases.
Discover the methodology behind InsideView’s continuously refreshed data and intelligence.
InsideView turns 40,000+ sources of information into actionable data and intelligence
More than 50 fields of actionable company and contact data, updated in real-time, answer the question of who to target. Use it to identify markets and prospects, score and route leads, segment territories, do pre-call research, enrich leads, clean your CRM data, and more.
Contextual market signals are pulled, analyzed, and curated from tens of thousands of global news and social sources, ranging from the New York Times to Bloomberg to the BBC, and even narrow niche publications. InsideView insights alert you to timely business triggers and answer the questions of why and when to reach out.
Consolidate all your connections, and those of your colleagues, into a private network within InsideView. It shows how you’re connected to your prospects so you can get warm introductions that open doors to close deals.
Leverage your connections from Outlook, Gmail, LinkedIn, Twitter, previous employers, alumni networks, reference customers, board memberships, and more. We’ll even add connections from our database that match your profile.
Of equal importance, we employ multiple data creation and gathering methodologies, unlike a number of commercial data providers. Why is this important? Because each methodology has strengths and weaknesses.
For example, crowd-sourcing produces a large volume of data, but the data tends to have a high percentage of duplicate and stale records.
Click on any of the methodologies to learn what it is and understand its strengths and weaknesses.
Our data scientists teach computers to “think” like humans – to take disparate bits of information, see the relationships between the bits, understand their meaning, and convert the result to useful information. Once “taught,” the computers can process massive amounts of data with human-like logic at infinitely faster speeds. Which is necessary in a world where Big Data is getting bigger every second. And the rate at which it changes is growing even faster.
We use this machine learning and artificial intelligence to triangulate data and analyze text.
The essential premise of triangulation is that consensus among multiple sources is the best indicator of truth. The broader the agreement, and better the source, the more reliable the data.
We pull data from thousands of sources, not simply to amass large amounts of data, but to triangulate each point to determine its validity and usefulness.
Using artificial intelligence, our triangulation technology is able to pick out from enormous volumes of conflicting data what different sources agree about. It’s also able to generate new data. For example, by following contacts over time, we’re able to assemble employment histories used to strengthen our Connections.
Bottom line: Triangulation is our primary means of validating data, which must meet our high standards for relevance and reliability to become part of InsideView’s data platform.
Text analysis – a form of natural language processing – comes into play for unstructured data, such as news articles and blogs. It enables our computers to “read” blocks of text to determine relevance, categorize insights, and extract facts that may be used to inform our data.
Often, news articles are the first to report critical business events that can indicate a change in a company’s name, structure, location, etc. This analyzed text feeds into the triangulation process, along with the structured data.
“With the volume of Big Data growing exponentially, and changing at an ever-increasing rate, data providers need a methodology – processes and technology – that can gather, analyze, and validate massive amounts of data at scale. Otherwise the data will be inaccurate and irrelevant by the time it’s delivered to you.”
– Jason Muldoon, InsideView VP Product Development