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How AI Turns Data into Smarter Marketing Decisions

Bringing data to life across omnichannel campaigns

By Douglas Wiggins    June 25, 2026     6-minute read

Artificial intelligence (AI) is transforming the world of marketing, enabling highly precise customer targeting and more efficient resource allocation. As AI becomes "smarter" and increasingly adept at long-term decision-making, brands are also using it to connect insights across channels, guide strategic decisions, and even assist with creative development.

According to a 2026 report by Jasper, 91 percent of marketers report actively using AI in their work — up from 63 percent last year; further, 95 percent of marketers plan to increase AI spend in the next 12 months.

Many marketers, however, are still struggling to leverage AI in ways that drive measurable business impact. But when combined with clean, enriched data and applied across both digital and physical channels — including direct mail — AI can help marketers turn mere insights into actionable decisions that deliver concrete business results.

The Intelligence Gap in Modern Marketing

AI is only as powerful as the quality of data it takes in. While today's marketers are collecting copious amounts of first- and third-party data, growth is not driven by data alone. Most organizations are not struggling with a lack of data, but rather translating that data into actionable intelligence that drives measurable results.

The brands benefiting most from AI will be the ones regularly cleaning their data, adding new layers of customer information, and focusing on customer-centric deployment to turn insights into measurable engagement and tangible outcomes.

To succeed with AI, fragmented data must be transformed into actionable insights that guide customer targeting, personalization, and resource allocation — helping to create well-connected, coordinated campaigns.

Barriers to Effective AI Adoption in Marketing

Despite the opportunity it presents, many companies still face challenges in fully realizing AI's potential:

• Technical readiness. Marketers may have trouble integrating AI into their existing systems and workflows, and they struggle with establishing the necessary infrastructure for full-scale implementation. Most companies are still in the experimentation or piloting phase: Nearly two-thirds of respondents in a 2025 McKinsey study of more than 1,000 consumers say their organizations have not yet begun scaling AI across the enterprise.
• Data privacy and security. As scams and data breaches become more commonplace, many consumers are increasingly concerned with digital security. According to a 2025 AvePoint report, nearly 86 percent of organizations have slowed generative AI rollouts due to data quality and data security concerns. Marketers need to ensure sensitive customer information is safeguarded in AI applications.
• Ethical considerations. Marketers may also have concerns about bias in AI models and the lack of transparency in how AI decisions are made. Consumers, too, have concerns: In a 2025 Relyance study, 76 percent of consumers said they would switch brands for verified AI data practices, even at a higher cost.
• Company culture. Employees may be skeptical or even fearful of AI. Business leaders must find ways to embrace innovation, answer employee questions directly and honestly, and provide relevant AI training.

How AI and Data Analytics Are Redefining Direct Mail Targeting

As AI becomes more sophisticated and predictive, direct mail continues its evolution from a standalone, uncoordinated channel to a highly targeted marketing tool within a broader omnichannel strategy:

• From broad reach to smarter targeting. Enabling the collection and analysis of a wide range of behavioral and demographic customer data, AI is helping companies of all sizes more precisely target their ideal audiences.
• From static content to targeted, personalized marketing. By analyzing customer data on prior purchases, geography, and overall brand engagement, AI makes it easier than ever to achieve personalization, at scale, in direct mail campaigns.
• From assumption-based planning to data-backed optimization. By identifying the most promising audience segments, AI can assist with targeted messaging and optimized timing to ensure customers receive personalized, relevant mail at the ideal moment.

Increasingly, machine learning is also being applied to the creative side of direct mail campaigns — analyzing the performance of headlines, images, offers, and formatting; adapting existing creative content for other campaign efforts; and enabling iterative campaign refinement.

For example, a retailer may send two versions of a mailer: one with an image of a specific product and one with a more general brand image. AI can then evaluate which version drives more conversions, reveal engagement patterns across various segments, and recommend refinements. All these insights can be fed into a continuous optimization loop, allowing for ongoing testing and improvements at scale.

How to Use AI in Direct Mail Marketing

To move beyond mere data collection and successfully leverage predictive and even prescriptive intelligence, marketers must fully integrate AI into existing systems and workflows.

Following a few key steps can help ensure AI is used to its full potential in direct mail and other marketing efforts:

• Clean up the data. Ensure all data is up to date and error-free and eliminate any duplicate customer profiles. Address validation and standardization systems can help ensure customer records are accurate and up to date while creating quicker, more efficient mailing processes. Next, make sure data is integrated across all marketing channels — social, web, text, sales, support chat, and direct mail — to ensure consistency.
• Target the right customers. Use AI to identify the audience segments most likely to buy, then release mail campaigns based on engagement and purchase history.
• Personalize content and timing. Use predictive insights to optimize direct mail timing and tailor messaging. Variable data printing allows for direct mail personalization at scale. Direct mail elements like copy, imagery, and calls to action to be customized for each recipient during a single printing run.
• Feed insights back into the system. Continually evaluate campaign performance and use results to inform audience segmentation, creative choices, and future activation strategies. Incorporating mail tracking and delivery visibility can add another layer. These services allow marketers to know exactly when mail is delivered, making it easy to coordinate timing with follow-up digital marketing while measuring impact.

Key Takeaway

As AI evolves, marketing intelligence will continue to move from collecting insights to predicting outcomes — and increasingly, guiding decisions. This gives marketers a larger opportunity to target audiences more precisely, optimize campaign timing, and seamlessly integrate their various marketing channels.

Working with clean, enriched data as their foundation, marketers can create more effective, relevant omnichannel campaigns incorporating both digital and print channels, including direct mail. With strategic use of predictive insights and continual campaign optimization, data can be turned into actionable decisions that deliver measurable business results and strengthen customer connections.

USPS is a partner in the ANA Thought Leadership Program.

All information, content, and materials discussed in this article are for general information purposes only and do not, and are not intended to, convey, or constitute advice from USPS.