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Gartner for Marketers Hype Cycle for Digital Marketing, 2021 Michael McGuire VP Analyst Leah Leachman Senior Principal Analyst As markets transition to a post-COVID-19 footing, digital marketing leaders are reemphasizing customer acquisition strategies and encountering new restrictions on customer data use and channel engagement. Use this Gartner Hype Cycle™ methodology to identify technologies to help you manage uneven market conditions. Michael McGuire VP Analyst Leah Leachman Senior Principal Analyst Published 12 July 2021 — ID G00748300 Gartner for Marketers Follow Us on LinkedIn Become a Client Hype Cycle for Digital Marketing, 2021 Excerpt of a full research note; available for limited use 2 Analysis What You Need to Know As parts of the world emerge from the lockdowns and major disruptions resulting from the COVID-19 pandemic, marketers are moving from pure customer retention strategies to new customer acquisition and growth. The past year saw marketing teams accelerating their digital transformations and working to shield their digital marketing initiatives from budget cuts. A full return to normal is not a given. This uncertainty is underscored by the persistent drumbeat of consumer concerns over how marketers use their personal data. Add to that continued concentration of market power in a few mega-walled gardens (Amazon, Apple, Facebook and Google) — each has released a bevy of new policies and platforms with some occasional quick revisions — and you have an environment best described as fraught. Hype Cycle for Digital Marketing, 2021 But the past year’s required moves toward embracing digital commerce and marketing analytics has given marketers a newfound resiliency they lacked prior to COVID-19. Such resiliency puts a spotlight on many maturing technologies and techniques, such as mobile marketing analytics, multichannel marketing hubs and social analytics. Meanwhile technologies with longer times to plateau (e.g., AI for marketing and personalization engines) will likely remain protected in marketing budgets given their long-term importance and incremental value they will deliver over the midterm. Gartner for Marketers Follow Us on LinkedIn Become a Client Excerpt of a full research note; available for limited use 3 Hype Cycle for Digital Marketing, 2021 The Hype Cycle In contrast to the focus on customer retention and market penetration strategies in 2020 and early 2021, the balance of this year and next will feature a greater emphasis on new customer acquisition. Such pressure for immediate growth means that while investment in emerging technologies — like AI for marketing — continues apace, marketers are also grappling with the challenges associated with these powerful yet immature technologies. Consider the following dynamics: • Advanced technologies such as AI for marketing promise transformative capabilities such as personalization of advertising and marketing engagements. Marketing platforms such as multichannel marketing hubs (MMHs), customer data platforms and mobile marketing platforms are integrating AI and machine learning (ML) capabilities. The appetite for such tools only continues to grow as 52% of marketers Gartner surveyed in 2021 were using AI and ML, with another 38% in the planning or piloting stages with AI/ML. However, only 17% have deployed AI across all aspects of their marketing technology stacks. Marketers still struggle to operationalize this emerging technology citing complexity and lack of resources (people and technology), and a certain amount of distrust. Among marketers using AI and ML, 73% find it difficult to trust AI and ML with important decisions. • Emerging technologies in the early stages of their journey to maturity — influence engineering (making its first appearance on the Hype Cycle for Digital Marketing) and customer data ethics — are fueled by a fundamental shift in how marketers acquire and exploit customer data. In particular, consumers, consumer industry groups and government regulators have pushed for greater transparency and consumer control over data collection practices. These include some of the largest tech players in the world. Marketers must find their footing in this new reality. • COVID-19 forced marketers to double-down on marketing technology to accelerate incomplete digital transformations. As a result, technologies such as event-triggered marketing, mobile wallet marketing and MMHs progressed more quickly toward maturity. Mobile marketing analytics — a profile Gartner expected to see mature and graduate off the Hype Cycle — will probably extend for at least another year as the marketers grapple with the practical impact of Apple’s App Tracking Transparency (ATT) framework on mobile advertising dollars and optimizing campaigns with less data. Gartner for Marketers Follow Us on LinkedIn Become a Client Excerpt of a full research note; available for limited use 4 Hype Cycle for Digital Marketing, 2021 Hype Cycle for Digital Marketing, 2021 Visual Search for Marketing Customer Journey Analytics Real-time Marketing Consent and Preference Management Expectations Customer Data Ethics Personi ication Conversational Marketing Shoppable Media Customer Data Platform AI for Marketing Multichannel Marketing Hubs Social Analytics Mobile Marketing Analytics Event-Triggered Marketing In luence Engineering Multitouch Attribution Identity Resolution Location Intelligence for Marketing ABM Platforms Personalization Engines Mobile Wallet Marketing In luencer and Advocacy Marketing Innovation Trigger Peak of In lated Expectations Trough of Disillusionment Slope of Enlightenment Plateau of Productivity Plateau will be reached: Time less than 2 years 2 to 5 years 5 to 10 years more than 10 years Source: Gartner (July 2021) © 2021 Gartner, Inc. and/or its affiliates. All rights reserved. Gartner and Hype Cycle are registered trademarks of Gartner, Inc. and its affiliates in the U.S. obsolete before plateau As of July 2021 Gartner for Marketers Follow Us on LinkedIn Become a Client Excerpt of a full research note; available for limited use 5 Hype Cycle for Digital Marketing, 2021 The Priority Matrix The overall trends in this year’s Hype Cycle noted in the previous section are reflected in the Priority Matrix below. With marketers showing a preference for martech stacks based on integrated suites (instead of an amalgam of best-ofbreed providers), MMHs are moving toward the Plateau of Productivity Meanwhile, mobile marketing analytics — expected to graduate off the Hype Cycle this year — remains on the Slope of Enlightenment due to Apple’s deprecation of its Identifier for Advertisers (IDFA) and the introduction of ATT. These moves potentially eliminate a significant amount of app-behavior data and cause marketers to continuously reevaluate how to leverage supporting technology. Ultimately, with the increased visibility among consumers of how their data is used, marketers must rethink their marketing strategies. Given consumer concern about marketers exploitation of their data and the adoption of machine learning in marketing automation, marketers need to be conscious of the ethical implications of their engagement strategies. These developments pushed customer data ethics toward the Peak of Inflated Expectations, showing that marketers must prioritize articulating and demonstrating an ethical framework that guides use of customer data. Similarly, influence engineering enters this year’s Hype Cycle for the first time as marketers pursue more strategic, marketshaping growth goals and look to get more out of their AI investments. The disruption of in-store shopping brought by COVID-19 propelled mobile wallet marketing and shoppable media along their Hype Cycle journeys. Gartner for Marketers Follow Us on LinkedIn Become a Client Excerpt of a full research note; available for limited use 6 Hype Cycle for Digital Marketing, 2021 Table 1: Priority Matrix for Digital Marketing, 2021 Benefit Transformational Years to Mainstream Adoption Less Than 2 Years High Multichannel Marketing Hubs Moderate Influencer and Advocacy Marketing Social Analytics Low 2 – 5 Years 5 – 10 Years AI for Marketing Influence Engineering Real-Time Marketing ABM Platforms Customer Journey Analytics Event-Triggered Marketing Identity Resolution Mobile Marketing Analytics Shoppable Media Visual Search for Marketing Customer Data Ethics Personalization Engines Personification Consent and Preference Management Conversational Marketing Customer Data Platform Location Intelligence for Marketing Mobile Wallet Marketing Multitouch Attribution Source: Gartner (July 2021) More Than 10 Years Gartner for Marketers Follow Us on LinkedIn Become a Client Excerpt of a full research note; available for limited use 7 Hype Cycle for Digital Marketing, 2021 Influence Engineering Analysis By: Andrew Frank Benefit Rating: Transformational Market Penetration: Less than 1% of target audience Maturity: Embryonic Definition Influence engineering (IE) refers to the production of algorithms designed to automate elements of digital experience that guide user choices at scale by learning and applying techniques of behavioral science. Why This Is Important The abundance of data sources and machine learning capabilities enables new systems of influence. Though still largely theoretical, breakthroughs in areas such as emotion detection and language generation show clear potential to automate influential aspects of communication. Examples have shown how AI can amplify bias and other harmful effects, yet beneficial goals may accelerate positive social change. This suggests a need for new forms of governance to oversee IE research and deployments. Business Impact Alongside profitable growth, businesses face growing demands to deliver on environmental and social goals, responsibly and transparently. The success of transformative initiatives needed to address these demands depends on market adoption. As IE techniques mature, their power to shape opinions and choices will increase to the benefit or detriment of these transformations. The long-term health of enterprises is thus impacted by their ability to wield these tools effectively in beneficial ways. Drivers Evidence of AI’s power in marketing: • Investments and breakthroughs in AI from global platform providers (such as Google, Apple, Facebook and Amazon) and martech vendors (such as Adobe, Salesforce and Oracle) remove barriers to AI adoption in marketing. • The emergence of technologies such as deepfakes and chatbots illustrate AI’s ability to synthesize lifelike experiences. • Academic work confirms the applicability of machine learning in experiments on influence. • Use of AI is strongly associated with marketing automation, recommendations and personalized digital experience, all high-priority initiatives in marketing, commerce and communication. Gartner for Marketers Follow Us on LinkedIn Become a Client Excerpt of a full research note; available for limited use 8 Hype Cycle for Digital Marketing, 2021 Commercial goals: • Pressure is mounting on marketing organizations to deliver better results with lower costs and the loss of key data sources such as browser cookies. • The shift of consumer behavior toward digital channels for work and commerce creates more opportunity for automated experience elements. Social goals: • Pressure is also mounting on corporations to explicitly address societal impacts, as expressed in investors’ environmental, social and corporate governance (ESG) ratings by nudging consumer choices toward more sustainable and equitable lifestyles. • Social fractures create widespread desire to find common ground and unify digital society in ways beyond the capabilities and scope of regulation. Obstacles • Widespread popular condemnation of manipulative technologies is evident, for example, in the recent backlash against Spotify’s patent on vocal emotion detection and in popular exposés such as “The Social Dilemma” and “The Great Hack.” • The deprecation of popular personal data collection mechanisms such as browser cookies and mobile device IDs that provide behavioral datasets used to train personalization algorithms creates the need to establish new sources of training data. • Government action is increasing, including: restrictions on use of personal data and unexplained profiling; oversight of AI’s role in propagating bias and discrimination. • There is a lack of established approaches or tools. The market is characterized by divergent approaches and conflicting claims as investors and entrepreneurs seek to exploit a building wave of hype. • General skepticism is common, as the actual potential of these technologies remains speculative and many experts question assumptions of viability. User Recommendations • Establish or locate the governance structure within your organization where the opportunities for IE are best investigated. Discover use cases and debate the goals and extent of potential commitments. Assure broad, cross-functional representation and ethics committee participation. • Assure that statements of purpose are translated into measurable goals used to train machine learning algorithms involved in IE. • Recruit friendly user test groups for research and experimental projects. Be transparent about goals and technologies. Be aware when research activities require advance informed consent. continued on next page Gartner for Marketers Follow Us on LinkedIn Become a Client Excerpt of a full research note; available for limited use 9 • Embed longer-term business metrics in operational dashboards and monitoring processes used to measure and motivate performance. Make opinion sampling and goodwill measurement regular features of your organization’s health check. • Build your organization’s knowledge center for IE, and include organizational learning, assessment of competitors’ and platform providers’ activities. Hype Cycle for Digital Marketing, 2021 Gartner for Marketers Follow Us on LinkedIn Become a Client Excerpt of a full research note; available for limited use 10 Hype Cycle for Digital Marketing, 2021 Customer Data Ethics Analysis By: Andrew Frank, Michael McGuire Benefit Rating: High Market Penetration: 1% to 5% of target audience Maturity: Emerging Definition Customer data ethics aligns business practices with moral and ethical policies that reflect a company’s avowed values. The need for a customer data ethics platform arises from the often unintended social and environmental consequences of using customer data with the singular goal of maximizing profits. Why This Is Important Adoption of machine learning techniques in marketing automation and personalization is on the rise. So is recognition of the tendency of these techniques to amplify biases inherent in customer data used to train them. As organizations expand their focus on privacy and social responsibility issues, addressing the ethical challenges of algorithmic marketing practices becomes imperative for corporate governance. Business Impact Ethical concerns over customer data use will force companies to reevaluate goals and metrics used to train machine learning and measure success. Modifying goals to account for social consequences and privacy-related data reductions may diminish short-term ROI on marketing initiatives. Longerterm, ethical oversight will minimize risks of brands and enterprises becoming tainted by allegations of discrimination or ethical hypocrisy and yield benefits in customer, employee and investor relations. Drivers • Awareness of the problem. Popular films and books like Coded Bias and Weapons of Math Destruction have brought the potential for machine learning to amplify bias in data into the cultural mainstream. • Consumer values. Gartner’s 2020 Consumer Values and Lifestyle survey showed “equality” rising to become the top-ranked consumer value for the first time, rising from No. 6 in 2019. Other surveys show a growing consumer belief that companies have a responsibility to address social issues even if it might hurt their profits. • Corporate response. Cultural trends have led to the widespread establishment of senior executive roles focused exclusively on ethics. AI ethics is a top priority for these new roles. Gartner for Marketers Follow Us on LinkedIn Become a Client Excerpt of a full research note; available for limited use 11 Hype Cycle for Digital Marketing, 2021 • Privacy laws and technology changes. Following the enactment of GDPR in Europe and similar privacy laws elsewhere, Apple and Google have moved decisively to limit access to personal data from digital interactions. This is causing a broad corporate reassessment of customer data collection and analysis techniques with growing sensitivity to privacy-related ethical concerns. • Pressure from toolsets. Responding to demand, major AI platform providers — including Google, Facebook, Amazon, and Microsoft — have released tools and datasets to assist analysts and data scientists with the detection and correction of bias in algorithms, raising expectations of adoption. Obstacles • Lack of consensus on defining fairness. Despite widespread accord with abstract principles of justice, concepts of moral correctness vary widely among cultures, communities and even within organizations. These differences become acute when organizations attempt to assess and encode fairness in algorithms. • Profit motive. Whatever commitment an enterprise may have to its avowed values, most corporations and investors prioritize growth above other considerations and have embedded systems of governance that effectively suppress initiatives that don’t support the financial bottom-line. • Suppression of data. Even organizations that mobilize to detect and eliminate algorithmic bias face the challenge of requiring protected class data to detect and measure it. Racial and ethnic proxies such as zip code and language preferences have limitations in situations where inference and lack of precision can go astray. • Lack of skills. The data science skills required for this work are specific and scarce. User Recommendations • Extend customer data ethics beyond compliance and treat it as an ethos that your company publicly shares with customers, employees and other stakeholders in its outbound communications. Even if it’s a work in progress, the public must know your priorities. • Anticipate that all algorithms trained on pure economic objectives will present potential ethical challenges. Operationalize the ethical evaluation of all automated decision making and tailor global brand or corporate frameworks to specific geographies, audience groups and societies. • Hunt for evidence of disparate impact in automated targeted promotions and advertising, and apply calculated constraints to compensate. • Establish and monitor long-term metrics that tie customer data ethics to economic factors like environmental, social and corporate governance ratings and brand equity measures. • Seek like-minded change agents throughout your organization and find opportunities to align efforts at escalating ethical initiatives. Gartner for Marketers Follow Us on LinkedIn Become a Client Excerpt of a full research note; available for limited use 12 Hype Cycle for Digital Marketing, 2021 Actionable, objective insight Explore these additional complimentary resources for marketing leaders: Research The State of Marketing Budgets 2021 Access key findings from the annual CMO Spend Survey Download eBook Research Digital Shakes Up Marketing Strategy and Tactics Discover emerging digital marketing strategies and priorities. Download Research Research Gartner Marketing Predictions for 2021 and Beyond: Marketing Hits Reset Uncover critical trends and key actions for CMOs. Download Research Webinar Create a Marketing Innovation Program That Balances Risk With Return Ewan McIntyre, VP Analyst View on Demand Already a client? Get access to even more resources in your client portal. Log In Gartner for Marketers Follow Us on LinkedIn Become a Client Excerpt of a full research note; available for limited use 13 Get More. 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