How AI Will Drive Transformative Change in Marketing

May 10, 2018
Contributor: Chris Pemberton

How marketers can sort through the hype and manage the AI customer experience.

The hype surrounding the use of artificial intelligence (AI) for marketing applications is escalating. AI continues to be the buzzword du jour to describe a host of “intelligent” features aiming to augment the functions performed by marketers in their everyday jobs. Marketers are increasingly creating and managing AI-powered customer experiences, from real-time personalization to conversational voice interactions, at scale.

“AI has the potential to transform the practice of marketing in significant ways over the next decade-plus,” says Bryan Yeager, Senior Director Analyst, Gartner for Marketers. “As hype intensifies, marketing leaders must exercise patience, persistence and long-term thinking in how they approach the use of AI.”

The CMO Spend Survey 2018-2019

What this year’s trends mean for marketers

AI will power insight, intuition and scale to help marketers realize the long-held desire of building individualized, contextual relationships with each prospect and customer. Although several years away from being realized by even the most advanced marketers, the long-term effects of AI should not be underestimated.

“Artificial intelligence has the potential to transform the practice of marketing in significant ways.”

According to a 2017 Gartner Research Circle survey, most organizations are investigating or developing a strategy for how AI will apply to their business. Customer engagement and digital marketing stand out as top areas where enterprises are running early AI experiments. An array of noteworthy applications used today will grow in prevalence over the next several years:

  1. Conversational experiences: Advances in natural language processing (NLP) enable people to have increasingly conversational experiences with computers through text and voice. Current implementations are rudimentary, but as platforms grow more capable and marketers onboard more of their own data, individual conversations based on customer context will be delivered at scale.
  2. Real-time personalization: Context, intent and journey stage are extracted from interactions to inform the delivery of tailored content, offers and promotions using propensity modeling, machine learning, machine vision and NLP.
  3. Identity resolution: Machine learning algorithms help sift through and map billions of ad impressions and hundreds of millions of device identifiers to provide marketers with greater confidence that the right message reaches the right person.
  4. Marketing orchestration: As AI takes on more campaign orchestration duties, the construct of the campaign dialogue or journey management workboard, where marketing specialists connect different triggers, channels and content, may become obsolete.
  5. Augmented marketing analytics: Easier-to-use analytical capabilities continue to expand across the marketing technology landscape, including natural language querying, natural language contribution analysis, prescriptive actions and logo detection.

Next steps

Connect with other leaders in your organization to ensure everyone has the same level of understanding on current AI projects. Prioritize inventorying and sharing data resources.

Focus near-term AI initiatives on data-centric, time-intensive marketing challenges. Predictive and prescriptive analytics that improve accuracy over time or testing and optimizing thousands of different data and content variables are potential initial use cases.

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