Machine Learning (ML) is developing under the great promise that marketing can now be both more efficient and human.
Cognitive systems, embedded or not into marketing software, are powering every single functional area of marketing and each step of the consumer journey.
AI–driven marketing leverages models to automate, optimize, and augment the ransformational process of data into actions and interactions with the scope of predicting behaviors, anticipating needs, and hyper personalizing messages.
Modern marketers utilize user data to deliverhyper–individualized and hyper–contextualized brand communications, in which each subsequent message builds on previous customer interactions. These interactions are seen not as a final stage of a consumer journey, but as a way to orchestrate future experiences in a satisfactory virtuous cycle.
Successful ML–powered companies turn data into seamless interactions with consumers using semiautomated and real–time processes. These predictive and augmented experiences build deeper one–to–
one relationships with consumers, improve omni–channel customer experience, and drive product differentiation.
Designing an AI strategy requires managers to systematically evaluate marketing needs in terms of automation, optimization, and augmentation in relation to the searched benefits of prediction, anticipation, and personalization. Balancing machine–inspired goals with expected benefits forces managers to strategically assess their organization to redesign roles and responsibilities while adequately defining the division of tasks between humans and machines.
This report, developed with the support of 30+ international experts, lays out a model for the definition of AI–driven strategies within the marketing context. And, it explores the critical elements of what, how, and why to infuse AI into the sequential steps of a marketing process.
DIFFUSION OF MACHINE LEARNING
Marketing professionals face increasing complexity due to the explosion of digital and data touchpoints, as well as unprecedented consumers’ expectations in terms of interaction, content, and offer personalization. Sucha degree of complexity is driving the adoption of a large variety of marketing software that marketers require to turn the vast array of historical data into actionable insights. Scott Brinker, VP Platform Ecosystem, HubSpot said, “AI algorithms and technologies are going to be deeply embedded at every layer of what the marketing software is.” Definitely, machine learning will leave no area of marketing untouched. Gianluca Ruggiero, CEO of Massive, agreed that “We must see AI as an allencompassing technology that is applied to every single field of marketing.” At the same time, machine learning models are being used to power and shape every step along the consumer journey.
Inside marketing technologies
Machine Learning (ML) is developing under the great promise that marketing can now be both more efficient and human.
AI–driven marketing leverages models to automate, optimize, and augment the ransformational process of data into actions and interactions with the scope of predicting behaviors, anticipating needs, and hyper personalizing messages.
Modern marketers utilize user data to deliverhyper–individualized and hyper–contextualized brand communications, in which each subsequent message builds on previous customer interactions. These interactions are seen not as a final stage of a consumer journey, but as a way to orchestrate future experiences in a satisfactory virtuous cycle.
Successful ML–powered companies turn data into seamless interactions with consumers using semiautomated and real–time processes. These predictive and augmented experiences build deeper one–to–
one relationships with consumers, improve omni–channel customer experience, and drive product differentiation.
Designing an AI strategy requires managers to systematically evaluate marketing needs in terms of automation, optimization, and augmentation in relation to the searched benefits of prediction, anticipation, and personalization. Balancing machine–inspired goals with expected benefits forces managers to strategically assess their organization to redesign roles and responsibilities while adequately defining the division of tasks between humans and machines.
This report, developed with the support of 30+ international experts, lays out a model for the definition of AI–driven strategies within the marketing context. And, it explores the critical elements of what, how, and why to infuse AI into the sequential steps of a marketing process.
DIFFUSION OF MACHINE LEARNING
Marketing professionals face increasing complexity due to the explosion of digital and data touchpoints, as well as unprecedented consumers’ expectations in terms of interaction, content, and offer personalization. Sucha degree of complexity is driving the adoption of a large variety of marketing software that marketers require to turn the vast array of historical data into actionable insights. Scott Brinker, VP Platform Ecosystem, HubSpot said, “AI algorithms and technologies are going to be deeply embedded at every layer of what the marketing software is.” Definitely, machine learning will leave no area of marketing untouched. Gianluca Ruggiero, CEO of Massive, agreed that “We must see AI as an allencompassing technology that is applied to every single field of marketing.” At the same time, machine learning models are being used to power and shape every step along the consumer journey.
Inside marketing technologies
According to Gartner, marketing technology represents the highest marketing expense, even above labor cost, accounting for 29% of the CMO budget1. The excitement around artificial intelligence has leaped into the field of marketing technology (MarTech), with all the major software providers claiming their ability to make marketing smarter and faster, but, most of all, more relevant. More than 7000 software applications empower every aspect of a consumer journey and encourage seamless execution2. Besides the number of solutions, marketing technology is also growing in its level of sophistication as intelligent algorithms are becoming core to these services.
In addition to the so–called “AI–first” software, which natively incorporates cognitive techniques such as machine learning and deep learning, other “traditional” enterprise solutions are infusing artificial intelligence as part of their natural product development (see Sensei for Adobe or Einstein for Salesforce). IBM’s Watson, for instance, was initially created for variouspurposes but it is increasingly leveraged to fulfill marketing goals. Other standalone software applications follow a similar path with players like MailChimp or Hootsuite upgrading their services with smarter algorithms. Providers, such as Shopify or Hubspot, have developed app marketplaces with thousands of vendors offering perfectly integrated solutions, from SEO to loyalty programs, some of which incorporate ML models. […]
read more: http://www.digitaljournal.com/pr/4290696?noredir=1
by Alex Mari
Research Associate
University of Zürich
Chair of Marketing and Market Research
Twitter: https://twitter.com/Mariketing
LinkedIn: https://www.linkedin.com/in/alexmari/
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