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On this subject, here is an article about the marketing hype cycle: Chief Marketing Officers (CMOs) are transitioning from a focus on customer retention last year to now looking to new customer acquisition and growth as they navigate into a post-pandemic world, according to Gartner, Inc. As the COVID-19 pandemic led many marketers to shift focus to pure customer retention strategies, it also brought an acceleration to digital transformations for many marketing organizations.
“The past year required moves toward embracing digital commerce, and marketing analytics has given marketers a newfound resiliency they lacked prior to COVID-19,” said Mike McGuire, vice president analyst in the Gartner Marketing practice, during his Gartner Marketing Symposium/Xpo session on the Hype Cycle. “This resiliency puts a spotlight on many maturing technologies and techniques, such as mobile marketing analytics, multichannel marketing hubs, and social analytics. Technologies with longer times to plateau – like artificial intelligence (AI) for marketing – will likely remain protected in marketing budgets given their long-term importance and incremental value they will deliver over the midterm.”
Figure 1: Hype Cycle for Digital Marketing, 2021
AI for marketing comprises systems that change behaviors, without being explicitly programmed, based on data collected, usage analysis and other observations for marketing use cases. AI for marketing promises to make insight generation and prediction faster, more accurate and more actionable. For example, optimizing customer-journey mapping, segment building, send-time optimization and product recommendations are among AI techniques finding their way into multichannel marketing hubs and personalization engines.
Scaling content operations also benefits from AI-enabled techniques. During his Gartner Marketing Symposium/Xpo session, “All AI is Not the Same: What Marketing Leaders Should Know About Deep Learning,” Jason McNellis, senior director analyst in the Gartner Marketing practice, noted how deep learning is a marketer’s most powerful way to extract insights from unstructured data and use AI to generate new content.
Multichannel Marketing Hubs
The multichannel marketing hub (MMH) orchestrates a company’s communications and offers to customer segments across multiple channels – from email and direct mail, to mobile, social and website landing pages. This can also extend to integrating marketing offers/leads with sales for execution in both B2B and B2C environments.
MMH solutions are core to marketers’ efforts to build customer relationships, by unifying customer data and deepening audience insights. They also enable marketers to deliver personalization at scale and align customer, channel, timing and content preferences.
Influence engineering (IE) refers to the production of algorithms to automate elements of digital experience that guide user choices at scale by learning and applying techniques of behavioral science. The abundance of data sources and machine learning capabilities enables new systems of influence and breakthroughs in areas such as emotion detection and language generation show clear potential to automate influential aspects of communication.
“Alongside profitable growth, businesses face growing demands to deliver on environmental and social goals, responsibly and transparently,” said Andrew Frank, distinguished vice president analyst in the Gartner Marketing practice. During his session “Can Machines Learn Persuasion? Evidence and Implications of Influence Engineering,” Frank discussed how as IE techniques mature, their power to shape opinions and choices will increase to the benefit or detriment of these transformations.
Customer Data Ethics
Customer data ethics aligns business practices with moral and ethical policies that reflect a company’s avowed values. During the Gartner Marketing Symposium/Xpo session “What Marketers Need to Know About Big Tech and AI Ethics,” Frank outlined how over the last few years, ethical concerns over customer data use has forced many companies to reevaluate goals and metrics used to train machine learning and measure success.
While modifying goals to account for social consequences and privacy-related data reductions may diminish short-term ROI on marketing initiatives, longer-term, ethical oversight will minimize risks of brands and enterprises becoming tainted by allegations of discrimination or ethical hypocrisy. This could yield benefits in customer, employee and investor relations.
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