The document discusses building economical simulators by using historical data from news articles, social media, and web searches to simulate causal relationships between events. It provides examples of simulating how an oil spill off Singapore in 2010 could cause gas prices to increase using data about the tanker collision and spill that occurred on May 25, 2010. The goal is to connect macro and micro economic factors to enable simulating the market.
14. 14
You Get a Summary Screen
IT Senior Director
IT
Lead Twitter Topic Fit
IT
Low Influencer
Company Interest Trend
High
Integrated in
Salesforce and Marketo
Mainstream economic theory models the world in a single almost static model.
In this talk, I will present how to mine the ever changing web, flows of information in company’s internal data to build dynamic micro and macro economic models. I will share algorithms that mine millions of data points and news to build simulators from text and other unstructured data.
I will share some of our customer results of using those models to boost their revenue by 10-30%.
Many marketers use very limited data to target prospects and segment their lists but the buying cycle is changing and there is a wealth of data and powerful tools out there that you Mr./Ms. Marketers can use to be more effective (uncover hidden "gold" in your Marketo & CRM, deliver more SQLs, increase conversions, make Sales team happy). Then go into Uriel example to illustrate the point.
We are a security company trying to sell to Dell
We did all the work for you behind the scene! You just get this summary page!
Additional Data Points:
Intent
Product installs: online and offline
Additional Data Points:
Intent
Product installs: online and offline
Explain that this is the conversion and how many deals!
Find hidden sweet spots
So I started collecting more and more data – we got all the NYT data from 1850 till today, social and real-time media, and human web behavior.
I was trying to teach the computer to find and understand human text and learn causality patterns from it. It was looking for patterns like:“Colorado Flooding Imperils Oil and Gas Sites, Causes Spill”“Texas oil pipeline fire causes evacuation of town near Dallas”“oil causes war”I believed if we could model the cause and effect of every possible event – we can start predicting better.So for each event reported we started automatically finding who performed the action, who the action was performed on and using what instrument and we started building a model of those causalities.
I was trying to teach the computer to find and understand human text and learn causality patterns from it. It was looking for patterns like:“Colorado Flooding Imperils Oil and Gas Sites, Causes Spill”“Texas oil pipeline fire causes evacuation of town near Dallas”“oil causes war”I believed if we could model the cause and effect of every possible event – we can start predicting better.So for each event reported we started automatically finding who performed the action, who the action was performed on and using what instrument and we started building a model of those causalities.
Causality Graph Building
Built on 20 machines
300 million nodes
1 billion edges
13 million news articles in total
Let’s take this one step further!
Predict the behavior of a complex market.
Learning across industries
What to do when you get to a new industry?
Collaboration between companies/ channels/ distributors
Two distributors are turning to the same customers – prevent this (How do we suggest the same lead to several companies)