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Machine Learning Meets Marketing

  • April 2015
  • -
  • Frost & Sullivan
  • -
  • 21 pages

Executive Summary

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. 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 moreinformed 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 MLbased 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.

Table Of Contents

Machine Learning Meets Marketing
Executive Summary 4
Introduction 5
Machine Learning for Lead Scoring 5
Four Startups That Provide ML-Based Lead Scoring 7
Mintigo Identifies CustomerDNAâ„¢ 7
Radius Intelligence Targets Enterprises Selling to SMBs 8
Infer Challenges Enterprises to Compare Predictive Modeling Results 9
Fliptop Takes a Consultative Approach 11
New Lead Sources, Old Software, and Vendors of Record 12
A Simple Solution from PerfectLeads 13
How LeadiD Addresses Lead Quality Issues 13
Repurposing Existing Software, Waiting on Vendors of Record 13
Salesforce Has the IP and the AppExchange 14
Adobe Enhancements Are Most Relevant in B2C 15
Oracle Gears Up through Acquisitions 15
How Ready Are Buyers and Sellers for ML-based Lead Scoring? 16
The Last Word 20

List of Exhibits

Exhibit 1: Generating a Machine Learning Model (Classifier) 6
Exhibit 2: Mintigo Process 8
Exhibit 3: Radius Segment-Building Screen 9
Exhibit 4: Infer External Signals Screen 10
Exhibit 5: What Fliptop Signal Data Looks Like in Marketo 12
Exhibit 6: External data sources are not very popular with large enterprises 17

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