Lead Scoring with With CRM, Marketing, Finance and Social Data

We present a comprehensive lead scoring system that will help take guess work out and help your sales machinery focus on the right leads.   What makes this a comprehensive system is the fact that it takes into account thousands of data points across the following systems.

  1. CRM – Salesforce
  2. Marketing Automation – Marketo, Pardot
  3. Social – Twitter, Facebook, LinkedIn
  4. Finance – Public Finance information from Yahooo

Here are the implicit and explicit parameters that we’ll collect information on:

Customer DemographicsPage viewsTwitter sentimentRevenue
Customer RegionNumber of searchesFacebook sentimentMarket capitalization
IndustryNumber of downloadsLinkedIn to mine open jobsYear over Year growth
Lead Job TitleNumber of emails openedProfitability
Lead DepartmentNumber of email clicks
Landing page visits
Webinar attendance
Videos watched
Number of case interactions
Forum posts


Before applying a classification algorithm, we have to first run sentiment analysis on Social data.  Jeffrey Breen has these slides explaining how to do sentiment analysis on Twitter.  The high level overview is to calculate a sentiment score for each tweet.  The main ingredient to calculate such a sentiment score is to create an “opinion lexicon” in English.  Fortunately, Bing Liu, Minqing Hu and Junsheng Cheng have created just this and is available here.

We then need a function to calculate the sentiment score, and for the purpose of this prototype, we’ll use R function created by Jeffrey Breen.