The Science Behind Customer Engagement: How Machine-Learning Drives Action

Market research authored by David Daniels (formerly of Forrester Research and Jupiter Research), and other analysts from The Relevancy Group. Sponsored research can be downloaded with registration. Other titles can be purchased for a fee or save and subscribe to our research. Get access. Ask the analyst; Get Access to Answers.

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  • Sponsored by Persado

    Written by David Daniels NLP, Persado, Email Marketing Machine-Learning, Congnitive

    About: Published in September 2016, We investigate the power of natural language processing for use in marketing and advertising. Marketers who had not used machine-learning to develop persuasive content state that they are ready to implement it. There is a great opportunity to leverage language sciences and emotion to optimize display ads and other forms of digital advertising, such a language within apps. New machine learning technologies allow marketers to create engaging emotional intelligent content in order to improve personalization. Persado’s Cognitive Content Platform is one such example. Marketers leveraging machine learning content automation have experienced 22-266 percent performance lifts.

    We address these topics:

    Likelihood to use marketing content developed by machine algorithms
    - The role of machine-based learning in personalization
    - Tips to consider when selecting emotional language machine-based learning solution
    - Case studies and results that can be expected when emotional language machine-based learning is applied to marketing and advertising

    Report Excerpt: 
    Thirty-eight percent of executives use machine-learning and predictive technology as part of their Data Management Platform (DMP) in advertising. Another 35% expect to implement within the next 12 months. Campaign Management solutions based on machine-learning are now common practice to help scale a marketer’s ability to segment audiences. For example, a platform can leverage behavioral data to find customers likely to cancel their subscription so action can be taken to mitigate losing a customer. The likes of Amazon, Facebook, and Google use machine-learning to recommend relevant products or content. Though theirs may be proprietary, machine-learning recommender systems are widely available and used across the internet. More on the cutting edge, Acquisio uses machine-learning to create a service they refer to as Bid & Budget Management. The platform monitors paid search campaigns, sets bids, adjusts bids for mobile, and ultimately achieves the lowest spend for the greatest outcome, all with minimal human input.
     
    The Research: 9 pages, 5 graphics, 1975 words.
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