Ready to Apply These 4 Tech Trends to Your Marketing?

Marketers should explore four emerging tech trends and how they impact customer data management and consumer privacy.

For brands, the pandemic’s initial disruptions are easing, if not absent, while spiraling inflation, talent scarcity and lingering supply chain challenges continue to contest marketers’ best laid plans.

Against this conflicting backdrop, marketers seek to balance between tried-and-true, personalized campaigns with novel digital experiences that differentiate their brands.

In contrast to the new customer acquisition strategies of 2021 and early 2022, the rest of this year and next will emphasize a more comprehensive view of the customer to unify cross-functional data to improve customer experience (CX), drive conversions and ensure retention.

New to this year’s Gartner Hype Cycle for Digital Marketing are four key technologies that will help marketers with this renewed focus of integrating customer data to drive innovation: generative AI, emotion AI, digital twin of a customer and customer data ethics.

Here’s how digital marketing leaders can incorporate these crucial technologies into their strategies.

Generative AI: Determine Initial Marketing Use Cases

Generative AI is a disruptive technology that impacts content development, CX enhancement and the generation of synthetic data. It learns from existing artifacts to generate new, realistic artifacts (e.g., video, speech) that reflect the characteristics of the training data without repetition.

In spite of third-party data depreciation, enterprises are still charged with both delivering a strong CX and influencing customer decisions. Generative AI can help marketers identify the core characteristics of customers to then target them with custom content in a privacy-compliant way.

In fact by 2025, Gartner expects 30% of outbound marketing messages from large organizations will be synthetically generated.

We see generative AI take hold in digital commerce; for example, where brands can generate human images for customers to try on clothes or makeup virtually. Avatars and virtual influencers can also engage customers on social media and in the metaverse to provide customer support.

Obstacles in digital marketers’ use of generative AI include potential government hurdles that seek to limit associated research, or the unfortunate reality of the technology being used for deepfakes, fraud and disinformation.

What can digital marketers do? Start by investigating how generative AI techniques benefit your industry and determine initial marketing use cases where you can rely on purchased capabilities or partnerships. Document the opportunities synthetic data could bring in terms of facilitating data monetization and lowering the cost of data acquisition.

Related Article: If You Want to Succeed With Artificial Intelligence in Marketing, Invest in People

Emotion AI: Explore Vendor Capabilities

Emotion AI uses computer vision, audio/voice input and more to translate behavioral attributes into human emotions, helping marketers better personalize digital communications. This is part of a larger trend we call “influence engineering,” which seeks to automate elements of digital experience that guide user choices at scale by learning and applying techniques of behavioral science.

Emotions play a key role in all phases of customer journeys. Access to emotion data delivers insights into motivational drivers that help them test and refine content, tailor digital experiences and build deeper connections between people and brands.

By 2024, 30% of marketers will use emotion AI, up from less than 5% today. Yet privacy concerns remain an obstacle to rapid adoption of many use cases, especially in live situations (versus lab/research environments). Hesitation around the manipulative power of emotion-aware algorithms and potential bias are prevalent, too. To avoid bias when using facial expression analysis, models must be retrained in different geographies to detect nuances due to different cultural backgrounds.

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