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Machine Learning Meets Marketing

Machine Learning Meets Marketing

Machine learning is the latest in a series of data-driven technology developments that are disrupting and transforming the Customer Experience, Marketing and Sales Analytics category of the Big Data and analytics (BDA) market. Stratecast|Frost & Sullivan has identified more than vendors who supply solutions in this category.1 Competition will require each of them to develop or partner to deliver machine learning (ML) capabilities for lead scoring.

The basic idea is that ML algorithms, in the right hands and with the proper data, can enable more-informed gathering and evaluation (scoring) of marketing leads. The higher-level value proposition is that, if businesses apply machine learning in this way, they will be able to adjust their sales and marketing efforts to address customers and prospects with the highest propensity to purchase.

Machine learning algorithms have been used by academic and scientific researchers for decades to discover patterns in new data based on previously processed datasets. Now, vendors are commercializing these algorithms in cloud-based applications that combine ML with additional functions, new data sources, and user-friendly interfaces. Marketing departments can use these new solutions, which are essentially ML applications that have been trained with data on existing customers, to score sales leads based on their propensity to buy. The variety of ways in which ML-based lead scoring solutions are coming to market means that there truly is an option to satisfy every level of budget, analytic skill and marketing automation maturity.

This report explains why these new solutions represent a major improvement over existing marketing automation measurements, how they work, and how different vendors are exposing machine learning capabilities in their lead scoring solutions. This report should be of interest to buyers, sellers, and current users of marketing automation (MA) and customer relationship management (CRM) solutions.

About this report

This report explains why these new solutions represent a major improvement over existing marketing automation measurements, how they work, and how different vendors are exposing machine learning capabilities in their lead scoring solutions. This report should be of interest to buyers, sellers, and current users of marketing automation (MA) and customer relationship management (CRM) solutions.


  • Executive Summary
  • Introduction2
  • Machine Learning for Lead Scoring
  • Four Startups That Provide ML-Based Lead Scoring
    • Mintigo Identifies CustomerDNA
    • Radius Intelligence Targets Enterprises Selling to SMBs
    • Infer Challenges Enterprises to Compare Predictive Modeling Results
    • Fliptop Takes a Consultative Approach
  • New Lead Sources, Old Software, and Vendors of Record
    • A Simple Solution from PerfectLeads
    • How LeadiD Addresses Lead Quality Issues
    • Repurposing Existing Software, Waiting on Vendors of Record
      • Salesforce Has the IP and the AppExchange
      • Adobe Enhancements Are Most Relevant in B2C
      • Oracle Gears Up through Acquisitions
  • How Ready Are Buyers and Sellers for ML-based Lead Scoring?

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