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

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1 The Science Behind Customer Engagement: How Machine-Learning Drives Action By David Daniels, The Relevancy Group Research Sponsored by Persado September 2016

2 Executive Overview Until now, machine-learning personalization has focused on behaviors and preferences to find the Right Person, Place, and Time. Now, new machine learning technologies incorporate emotions into the equation to make a better connected message. With this study, we set out to learn if executives are using machine-learning technology to optimize for the last piece of the marketing puzzle, The Right Message, and what value these early adopters gained from doing so. In today s marketplace, marketers must deliver a customized experience to customers across digital touchpoints. Machine-learning and predictive technology are gaining momentum in this content revolution. marketing, website optimization, and advertising are just a few practices that are seeing ROI spikes thanks to new tech. With this technology, marketers can generate emotionally intelligent content and improve personalization at scale. In a survey of 301 marketing executives, a huge majority who have not yet used machine-learning for content development said they are ready to start. Those who said that they have already used machine learning for content reported performance lifts of 22%-266% with less grueling effort. These results indicate that marketers and advertisers are growing comfortable with automation and machine-learning technology for content creation. No small part of this trend are the measurable gains in effectiveness and efficiency of customer communication. Machine-Learning in Advertising and Marketing Is Gaining Momentum 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 (Figure 1). 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 Science Behind Customer Engagement: How Machine-Learning Drives Action P a g e 1

3 Figure 1. Advertising Predictive Recommendations Based on Machine Learning 27% 38% 35% Currently use Plan to implement within the next 12 months Do not use and no plans to implement within the next 12 months Question: Please indicate if you currently utilize these tactics in your advertising efforts, if you plan to utilize them, plan to use them or have no plans to utilize them? (Select One) Source: The Relevancy Group Executive Survey n=301, 4/16 US Only. Marketers Are Ready for Content Creation Based on Machine-Learning Natural language processing (NLP) technology has long been used to perfect search engines and classify inbound . Most recently, it's borne fruit in Artificial Intelligence (AI) assistants, such as Siri and Amazon s Alexa. The power of NLP technology has finally come to marketing. In our survey, 80% of executives who haven't yet used machine-learning for content development say they're ready to start. Fifty-five percent say they would test it and 25% that they would put it to work immediately (Figure 2). Twelve percent of those who will not use it cite budget challenges. Only 8% said they would not trust machine-learning technology. This technology has proven a capacity to optimize all types of digital advertising and marketing, such as content and display ads. Machine-generated messages have consistently outperformed humangenerated messages. For this reason, we expect adoption of and confidence in digital marketing automation technology to continue its upward trend into 2017 and beyond. If your organization isn t on board, it s likely to fall behind as machines continue to out-perform traditional methods. Figure 2. Likelihood to Use Marketing Content Developed by Machine Algorithms The Science Behind Customer Engagement: How Machine-Learning Drives Action P a g e 2

4 8% Yes, we would test it Yes, we would No, we don't have budget No, we would not trust it 25% 12% 55% Question: Would you utilize marketing content if it was developed by machine algorithms and had a high likelihood to deliver a return on investment? (Select One) Source: The Relevancy Group Executive Survey n=301, 4/16 US Only. Machine-Learning Gives Marketers an Edge with Personalization Machine-learning optimizes marketing content for a wide array of messaging components. These include product or offer descriptions, calls-to-action, formatting, and positioning. Marketers apply personalization to targeting, retargeting, promotional offers, acquisition, loyalty, and campaigns designed to drive advocacy. Not only that, it speeds up creation. It learns through continued use and discerns behavioral patterns among individuals. In this way, machine-learning platforms can move from broad marketing to a more personalized approach that no team of humans could do at scale. Look forward to the following benefits: Voice of the Customer: The machine-learning approach captures customer preferences and antipathies to generate the best marketing content. Increased Revenue: The Relevancy Group has proven in many research studies that relevant marketing engenders 3-5x more revenue compared to less relevant campaigns. Optimizing language for emotion will increase relevance and by extension, revenue. Improved Time to Market: A machine-learning approach eliminates the guesswork and rework. It speeds the production cycle by generating the most engaging language with the greatest impact. Increased Customer Engagement: Using perfected, machine-generated content across the customer lifecycle will increase engagement and spur loyalty. Consumers are more likely to embrace brands that recognize them. The Science Behind Customer Engagement: How Machine-Learning Drives Action P a g e 3

5 Deepen Analytical Value: Exposing the emotions and language that motivate customers to engage and buy provides organizations insight into the essence of a conversion. The Personalization Triumvirate: Behavior, Preference, Sentiment Until now, personalization technology has relied heavily on behavior, leveraging clickstream data, tags, and real-time event data. New machine-learning technologies take into account preferences and sentiment, giving marketers the power to create engaging, emotionally intelligent, experimentally validated content to improve personalization. Persado s Cognitive Content Platform is one such example. Persado uses natural-language processing and machine-learning to generate engaging copy automatically based on behavioral and attitudinal data. In the same survey of 301 executives, The Relevancy Group found that marketers are looking beyond behavioral predictive technologies to incorporate emotional language science (Figure 3). Predictive personalization is growing. Fifty-two percent of marketers use behavioral data. Forty-nine percent use real-time data. At this point, 17 percent use machine-learning to develop compelling language for targeting customers. Figure 3. Methods Utilized to Develop Marketing Content and Predictive Models Utilize past behavior Utilize real-time information 49% 52% Third party data that combine market trends 36% Cognitive algorithms 26% Categorize content tagging by themes Use emotional information, machine learning that develops persuasive language Packaged vendor solutions such as Certona None of the above, we don't utilize predictive models Question: What approaches are you utilizing for developing marketing content and predictive models to target customers? (Select All) Source: The Relevancy Group Executive Survey n=301, 4/16 US Only 8% 10% 17% 22% 0% 10% 20% 30% 40% 50% 60% What You Need to Know About Machine-Learning Technology Given the black box nature of machine-learning technologies, marketers should have a basic grasp of the science and methods used to optimize marketing content. Marketers should understand the methods prospective vendors use. The Science Behind Customer Engagement: How Machine-Learning Drives Action P a g e 4

6 Below are a few key NLP methods to inquire about: Algorithm Method: Algorithms put the learning in machine-learning. For NLP platforms, you should understand what system is in place to guarantee that the machine knows how to use language, use it effectively, and automatically refine its usage based on campaign performance. Behavioral Triggers: Determine how the system interprets and learns from audience behavior. With a proper language classification system in place, the platform should be able to pinpoint specific behavioral triggers and learn in an iterative manner over time, based on aggregate previous performance. This ensures gains in future efficacy. Segment Differences: Vendors should illustrate how they roll up the data for categorized rankings of words and phrases. Such categories might include industry, channel, geography, and engagement type (one-off or subscription). Information on these differences can further segment and enrich language classification when, for instance, the same message performs well in one context but poorly in another. Language Classification: The vendor should be able to show you how they break down marketingspecific language. There should be a handful of classifications, which enable the platform to test and analyze the discrete elements of any message. Such classifications might include functional language (like a call to action) or descriptive language (like a product offer). Emotional Rankings: Effective communication becomes personal when infused with specific emotional valence that can be used as data to better understand and speak to your audience. A basic version of this is called Sentiment Analysis, which classifies messages as positive or negative. More nuanced and informative versions dig down to diverse emotional categories. The more specific the platform can be with emotional valence, the greater your chances of succeeding to meet your audience on their level. The Science Behind Customer Engagement: How Machine-Learning Drives Action P a g e 5

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9 The Future of Machine-Learning in Marketing is Inevitable The application of machine-learning technology allows marketers to scale their practice like never before. Marketers have been quick to adopt the cognitive computing revolution to solve for The Right Person, Place, and Time. Only recently has the new machine-learning era gained momentum for The Right Message. With machine-learning, marketers can develop content to leverage emotional triggers that resonate with any customer. This method creates a deeper connection with customers and improves performance across channels. It also gives marketers a huge advantage in the race to true one-to-one personalization. Those who are not ready to take steps toward implementing and assessing this technology will find themselves struggling to compete. In just a few short years, the amount of machine-generated content published daily will rival that of human-written content, and the data-backed content generated by computers will consistently outperform its human counterparts. The Science Behind Customer Engagement: How Machine-Learning Drives Action P a g e 8

10 About Persado Persado s cognitive content platform generates language that inspires action. Powered by cognitive computing technologies, the platform eliminates the random process behind traditional message creation. Persado arms organizations and individuals with smart content that maximizes the efficacy of communication with any audience at scale while delivering unique insight into the specific triggers that drive action. About The Author David As CEO of The Relevancy Group, David directs market research and advisory services essential to digital marketing. Direct Magazine said David is one of the most influential experts in marketing, if not the most influential. Co-author of the book Marketing An Hour A Day, David has held senior level positions at Forrester, JupiterResearch, Apple, Anthropologie and other top brands. David is also the President and Publisher of The Marketer Quarterly a digital magazine and app for marketers by marketers available for free with registration at and Apple, Google, Amazon app stores. About The Relevancy Group Measuring consumer and executive behaviors, The Relevancy Group (TRG) provides market research and advisory services that deliver strategies to optimize a return on marketing investments. Each Analyst has a minimum 15 years of experience in digital marketing and many are highly sought after public speakers. In addition to working with some of the top brands and vendors in the digital marketing economy, TRG produces dozens of surveys, research reports and webinars each year. TRG also publishes the digital magazine for marketers by marketers, The Marketer Quarterly. About The Research The Methodology In April 2016 The Relevancy Group conducted a survey that resulted in 301 completed and qualified advertising and marketing executives. We qualified respondents based on the size of their customer database, sending volume, familiarity of their company's marketing efforts, and other attributes such as the individuals role/title. Respondents self-identified their company size and market sector category. We collected descriptive information about these organizations including revenue, CRM and Advertising. The survey utilized skip ordering and randomization and screener questions. The survey design and final analysis was provided by a team TRG of analysts. For more information on The Relevancy Group s services, visit call (877) , info@therelevancygroup.com or on Reproduction by any method or unauthorized circulation is strictly prohibited. The Relevancy Group s reports are intended for the sole use of clients. For press citations, please adhere to The Relevancy Group citation policy at All opinions and projections are based on The Relevancy Group s judgment at the time of the publication and are subject to change. The Science Behind Customer Engagement: How Machine-Learning Drives Action P a g e 9