Content has become the growth engine of modern marketing and generative AI is accelerating its transformation. As AI tools quickly reshape how brands produce and distribute content, marketing teams are scaling creative across more channels, formats and customer touchpoints than ever before. But as AI has made it easier to create content at speed, it has also introduced a new challenge — understanding which content drives results.

According to the Content Marketing Institute, only 29% of marketers report being highly successful at measuring content performance. As assets multiply across channels — including websites, mobile apps and paid media — marketing teams are often left piecing together fragmented signals from different tools and platforms. The result is a widening gap between content creation and content intelligence.

In the era of AI-powered content generation, success will not come from creating more assets. It will come from understanding which assets influence engagement, conversions and revenue across the customer journey.

What matters now is not how much content you can produce, but how effectively you can measure and optimise it across channels and touchpoints. Adobe Content Analytics is evolving to make this shift possible.

Expanding content intelligence across channels.

Adobe is expanding Content Analytics to help organisations measure content performance across web, mobile apps and paid media in an integrated framework.

This evolution gives organisations a unified view of how content influences the entire customer journey. Instead of analysing assets in isolation or within a single channel, teams can now understand how content performs across touchpoints — from first exposure through conversion.

By bringing cross-channel behavioural and asset data together, Content Analytics enables organisations to:

• Deliver more cohesive experiences across web, mobile and paid media.
• Accelerate personalisation with AI-driven content insights.
• Increase creative accountability with asset-level performance visibility.
• Optimise media and content investments based on what truly drives results.
• Equip content creators with insights to guide strategies.

New capabilities in Adobe Content Analytics.

With this release, Adobe introduces new capabilities that help content marketers and analysts understand performance at both the asset and journey level.

Mobile content measurement.

Capture in-app content exposure and engagement through the mobile SDK to understand how mobile experiences contribute to customer behaviour and business outcomes, including sign-ups, shares and downloads.

AI-powered content attributes.

Featurisation services automatically generate AI-derived attributes from images and text — such as subjects, scenes and colours — enabling deeper analysis of creative patterns that drive engagement.

Cross-channel asset identity.

Identity services recognise similar assets across web, social and mobile experiences, providing a single view of how content performs across environments.

Advanced cross-channel metrics.

Combine paid and owned content signals to create unified performance metrics, such as total asset views or revenue impact across channels.

Paid media data integration.

Native Adobe Experience Platform connectors ingest publisher data from platforms like Meta, enabling scalable, automated measurement of paid media content performance.

AI-accelerated insight templates.

Prebuilt analysis templates quickly surface trends in engagement, spend efficiency and conversion drivers, highlighting top-performing creative and optimisation opportunities with inline images in charts and graphs for additional context.

From content creation to content intelligence.

As generative AI dramatically increases content supply, the competitive advantage will shift to content intelligence — the ability to identify winning creative patterns and continuously optimise experiences in real time.

With Adobe Content Analytics, organisations can move beyond static reporting to understand how content influences customer behaviour. By connecting asset insights with engagement and revenue outcomes, teams gain the clarity needed to amplify what works, refine what doesn’t and invest in the content that drives growth.

Industry examples.

• Finance: A financial services company promotes loan and credit card offers across its website, mobile app and marketing campaigns, but struggles to identify which visuals and messaging drive applications. With Content Analytics, the company can connect asset exposure to customer behaviour and application starts, revealing which content influences sign-ups. For example, imagery featuring everyday lifestyle moments paired with “low introductory APR.” messaging may generate significantly higher credit card application rates than generic product banners.
• Retail: A retailer promotes new collections with fashion photography across its website, mobile app and social media, but lacks visibility into which visuals drive purchases. With Content Analytics, the brand connects asset exposure across channels to engagement and sales, revealing which images perform best. For instance, product photos featuring models in outdoor settings may drive significantly higher add-to-basket rates and order value, helping the retailer prioritise similar creatives across campaigns.
• Media and entertainment: An entertainment platform promotes new shows and premieres with rotating artwork across its homepage and app but struggles to understand which visuals drive viewers to start watching. The company connects artwork exposure to viewing behaviour with Content Analytics, revealing which shows and creative styles generate the most engagement. For example, bold character close-ups from a new drama series could be driving significantly higher clicks to stream than generic show banners.
• Travel and hospitality: A travel brand promotes destinations and limited-time offers across its homepage, destination pages and mobile experiences, but finds it difficult to understand which visuals inspire travellers to book. With Content Analytics, the company can easily connect content exposure to booking clicks across experiences, revealing which creative drives intent. For example, destination images featuring beach sunsets paired with “limited-time getaway” messaging may generate significantly higher click-through rates.

In a world where content is growing exponentially, the question is no longer how much content you can create, but how intelligently you can optimise it.

Dial up personalisation. Dial up performance. Dial up engagement and conversions — with Adobe Content Analytics. Book a demo today to see it in action.

Danielle Doolin is a Principal Product Marketing Manager for Adobe Analytics, where she leads go-to-market strategy for Content Analytics and customer journey measurement. With over 18 years of experience in digital analytics, Danielle specialises in helping brands understand how content drives engagement, conversion and revenue across web, mobile apps and paid media. Prior to Adobe, she worked at comScore, focusing on advertising measurement and campaign effectiveness. Danielle is passionate about bridging the gap between content creation and performance, empowering organisations to make smarter, data-driven decisions. She lives in Aldie, Virginia, with her golden retriever, Oliver.

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