What Gartner’s Digital Ad Hype Cycle 2024 Shows About Marketing Innovation and Adoption

“Marketers want to increase or stretch fewer dollars across more expensive media and tools,” said Gartner market analyst Mike Froggatt, who co-authored the report. “With marketing technology vendors, it’s pretty safe to say they’re not going to reduce their costs — especially as they started creating AI and other tools that … require a ton of processing power. It’s not cheap.”

The biggest drivers include TV advertising, identity dissolution and customer data platforms, which made their debut on the hype cycle. However, Gartner removed PR NFTs from this year’s list, as the topic has “quietly left the stage as a promotional opportunity” following a “spectacular collapse” of NFT trading platforms, according to the report.

Along with other terms familiar to marketers, newcomers to the hype cycle include AI-related categories such as generative AI for marketing along with other topics such as emotion AI for marketing and influence AI, and visualization AI. Influence AI automates digital experiences to help marketers shape consumer choices more effectively based on user intent and motivation. Emotion AI analyzes and responds to users’ emotional states with tailored experiences based on body language, voice and other inputs.

“Generative AI continues its surge forward, heading into the valley of disappointment,” said fellow Gartner market analyst Nicole Greene. “The increased hype over the past year has led to misplaced expectations of what the technology can deliver. The true potential of genAI will be revealed when combined with other AI technologies.”

The report also detailed Google’s recent plans to not phase out third-party cookies as it had planned. Froggatt noted that the news will affect almost all profiles included in the report including generative AI. Some of the sections that received edits as a result were retail media networks, consent and preference management, data rooms, programmatic segment-based advertising and personalization.

Gartner isn’t the only company updating forecasts as a result of Google’s news. Earlier this month, the Boston Consulting Firm noted that a majority of markets said changes to third-party cookies pose a risk to at least 20% of data used for targeted marketing. Last month, Forrester said 61% of marketers surveyed for its 2024 report had already expressed skepticism of Google’s plans even before the announcement.

Here’s a look at five technologies on Gartner’s 2024 hype cycle:

Generative AI for marketing

• Promise: Generative AI development and adoption has advanced tremendously over the past year and now has an estimated target market penetration of between 20% and 50%. Drivers include improved foundational models, an increased impact on marketing creativity and productivity, more enterprise usage, improved deepfake detection, and increased competition on pricing and security.
• Challenge: Rapid adoption has also led to greater scrutiny of the technology’s ethical and societal implications, ranging from AI disinformation and fraud to social unrest and licensing issues. These concerns and others lead many marketers to still approach the technology with caution despite early results — not to mention other challenges with education, performance consistency and bias. Despite progress, Gartner found that most companies are still in the exploratory phase, with efficiencies in content creation rather than fully transitioning to AI-generated ads at scale.
• View: Marketing managers need to navigate the hype cycles by prioritizing use cases where generative AI is a good fit, Greene said. Marketing managers and others must also account for budgets, data management, time and staff training. She added that areas such as Influence AI and emotion AI for marketing are also supported by first-party data.

Customer Data Platforms (CDP)

• Promise: Customer data platforms (CDPs) are making their debut on the 2024 hype cycle, which is still two to five years from reaching a plateau. Key drivers include more CDP connectivity across marketing suites, organizations’ increased reliance on centralized data, and a greater focus on privacy and compliance.
• Challenge: Key challenges include varying operational costs, overlapping martech functions, integration complexity and a higher level of technical expertise.
• View: CDPs can help companies fine-tune AI models and ground data to improve response accuracy.

Retail Media Networks (RMN)

• Promise: As retail media networks expand, drivers include online sales outpacing in-store retail, increased investment in performance, loss of data signals, concerns about walled gardens – from both brands and retailers. Others include increased retailer use of loyalty apps, in-store mobile scanning and higher presence of digital displays in stores.

• Challenge: Despite all the furor around RMN, the technology is beginning its descent into Gartner’s “valley of disappointment” amid a growing gap between promise and reality. Fragmentation, inconsistent standards, pricing issues, ad spend pressures and general confusion all create new challenges after early rushes to build and adopt. There are also growing pains as teams come together across traditional and emerging channels to adapt to and adopt RMN.

• View: Advertiser relations with RMN and vendors still have a lot of work to do. Some brands feel more pressure to double-pay by advertising with both RMN and online platforms to drive traffic to RMN. “Last year it was done, [adopting] as many retail media networks as possible, maximizing sales for the cheapest [advertisers] we can get,” said Froggatt. “It was about finding the point of diminishing returns for each individual media network. This year we’re getting into the weeds of management a little bit more.”

Data Clean Rooms

• Promise: These are also approaching their peak of high expectations, driven by more data printing, more focus on first-party data, more measurement and the drive for more privacy compliance.

• Challenge: There are still issues such as device ID gaps, regulatory uncertainties – particularly with state laws requiring different levels of privacy – and uncertainty around cost estimates.

• View: Gartner is entering clean rooms with a broader category of “data collaboration tools,” which help with identity resolution, consent preferences, and optimization. One example is the increased focus on improving the reach and frequency of campaigns across online platforms and connected TVs.

Programmatic Segment-Based Advertising (PSBA)

• Promise: A growing technology on the hype cycle, PSBA is still upward on the hype cycle. The technology aims to improve accuracy and accountability in cookie-free environments by targeting clusters of users instead of individuals. Other technologies such as content targeting and verification services fall under the broader category of programmatic segment-based advertising.

• Challenge: While PSBA aims to improve privacy, there is still a risk that segment IDs can be linked to people when combined with other data. There is also still little understanding of the benefits and risks of segment ID traffic in open message streams, which have varying pricing and privacy practices.

• View: With market penetration falling between 1% to 5%, PSBA is still lower on the innovation curve, especially when considering browser usage and the evolution of Google’s Privacy Sandbox. Marketers can improve their effectiveness with PSBA by supporting pre-bid and open source standards, according to Gartner, which also noted that walled garden environments should still be considered. Agencies and data science teams can also help model the potential outcomes alongside the use of data rooms.