GenAI-Driven Personalization Depends on Clean, Structured Data

Contents

The Promise of GenAI in Personalized Content The Symbiotic Relationship Between GenAI and Data Standards Unlocking New Levels of Marketing Success More Resources on Data and AI in Marketing Personalization

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Marketers who are strategically integrating generative AI with robust data standards are achieving unprecedented content personalization at scale.

And marketers who use genAI are reclaiming hours each week—time that can be channeled into strategic initiatives that drive measurable business outcomes.

Yet a critical truth remains: The transformative power of AI-driven personalization in marketing depends entirely on standardized, high-quality data. Without that essential infrastructure, even the most sophisticated AI tools struggle to deliver meaningful results or demonstrate clear ROI.

The Promise of GenAI in Personalized Content

GenAI allows marketers to personalize content at scale for the entire customer journey, eliminating the traditional tradeoff between scalability and personalization.

From personalized email subject lines to tailored social media posts reflecting user interests and dynamic website content that adapts to visitor behavior, genAI enables deeper engagement at scale.

Consider how Mastercard uses genAI to deliver real-time, location-based offers by analyzing spending patterns, location data, and merchant attributes. The resulting insights are used to generate promotions relevant to individual customers’ current situations.

Marketers can use that approach as a playbook for creating truly relevant and valuable offers for each individual customer, moving beyond generic messaging.

And now, strategies once reserved for large marketing organizations are being adopted by agile marketing teams eager to scale relevance and reach.

B2B marketers are increasingly tapping into genAI to deliver more valuable, personalized content experiences to their audiences—adapting messaging by industry, company size, buying stage, job title, or even specific account.

Historically, one of the biggest hurdles to personalization in B2B has been the sheer volume of content needed to make it work. Creating tailored landing pages, emails, product descriptions, or sales collateral for each target segment—or even each key account—was often too resource-intensive to be realistic.

But genAI is shifting that reality. Now, the path to scalable content generation and real-time personalization is not only possible—it’s practical. What once required hours of manual content creation can now be automated, scaled, and paired with firmographic and behavioral data to fuel onsite personalization.

Khoros, for example, combined 6sense intent data with its own behavioral insights to personalize website content in real-time—doubling pageviews and increasing demo requests fourfold.

And there is a lot of room for testing and creativity. For B2B companies with complex product catalogs or solution portfolios, genAI can automate everything from product descriptions to Sales enablement materials—customized by buyer segment, geography, or use case. AI-generated spec sheets, feature comparisons, and customer success stories can be instantly tailored to align with specific prospect needs.

But realizing that potential hinges on structured, standardized metadata.

To deploy and measure the performance of personalization at scale—especially in a B2B environment—it isn’t enough to simply generate content. To reach the right audience at the right moment, content must be organized, tagged, and governed. Without standardized metadata and taxonomy, even the most advanced AI models will struggle to surface the right asset at the right time.

True personalization depends on clean, accurate data and consistent structure to fuel activities.

The Symbiotic Relationship Between GenAI and Data Standards

As genAI capabilities continue to evolve rapidly, their effectiveness hinges on addressing persistent data challenges. Organizations frequently encounter the following:

Data silos: Information fragmented across different systems

Inconsistent taxonomies: Variations in how data is categorized and labeled

Poor data quality: Errors, missing values, and outdated information

AI-generated metadata often lacks contextual understanding, leading to generic or irrelevant tags that hamper accurate content categorization and discovery. For instance, AI tools may assign broad keywords that dilute search relevance, making it difficult for teams to locate specific assets. Despite significant investments in AI tech, many organizations find their initiatives underperforming because they cannot provide AI models with clean, consistent, and comprehensive data. When AI systems process flawed information, they generate inaccurate insights and fail to deliver anticipated returns.

Unlocking New Levels of Marketing Success

The interplay between genAI and robust data standards creates powerful marketing advantages:

Authentic personalization: Creating content that genuinely connects with customers on an individual level

Operational efficiency: Automating content creation and distribution while maintaining brand consistency

Precise measurement: Tracking content effectiveness with unprecedented accuracy

Data-driven optimization: Rapidly adapting messaging and strategy based on real-time insights

Competitive advantage: Responding quickly to market changes and emerging trends

A real-world example that demonstrates this point is the experience of a leading global consumer products company:

Despite investing in advanced analytics platforms, the company struggled with inconsistent taxonomies across 200+ markets, with regional teams each maintaining their own data interpretations. That fragmentation made it impossible to generate cohesive global reports or implement new capabilities effectively. By partnering with its agency to establish a unified yet flexible global taxonomy, the company transformed its data compliance from a mere 2% to nearly 100% within months.

That foundation of standardized, high-quality data not only enabled near real-time campaign insights but also created the perfect ecosystem for future genAI implementation. With clean, consistent data flows now automated across platforms, the company is positioned to leverage genAI for truly personalized content deployment at scale—an opportunity that would have remained inaccessible without first solving their fundamental data standardization challenges.

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As 2025 progresses, forward-thinking marketing leaders recognize that the AI revolution isn’t merely about adopting new technology—it’s about building the foundational data standards that enable these tools to deliver their transformative potential.

Organizations that prioritize both elements are witnessing dramatic improvements in customer engagement, conversion rates, and sustainable business growth.