Personalization in Digital Marketing: What It Is, Why It Matters, and How It Works

The one-size-fits-all approaches that once dominated digital advertising no longer cut it.

The internet is flooded with ads, and many fail to resonate. Ad fatigue often gets the blame, but a lack of relevance is what ultimately drives people to tune out altogether.

These days, consumers don’t just want brands to deliver ads and experiences tailored to their individual preferences and interests—they expect it.

According to an oft-cited McKinsey study, 71% of consumers expect companies to deliver personalized interactions, and when that doesn’t happen, over three-quarters of them become frustrated.

Get personalization in digital marketing right, and not only do consumers report higher levels of satisfaction, but organizations often see improvements in revenue and retention.

Get it wrong, and brands risk wasted media spend, declining loyalty, and missed opportunities for further growth.

In response, brands are doubling down on 1st-party data strategies, investing in AI-driven decision-making, and adopting dynamic creative approaches that adapt messaging as audience behavior evolves.

In this research-backed article, we’ll break down what personalization in digital marketing really means, how it works, and the tools brands can use to deliver personalized campaigns at scale.

What Is Personalization in Digital Marketing?

Personalization in digital marketing is defined as the practice of using customer data—such as browsing history, past purchases, and demographic information—to tailor ads and experiences to individual consumers based on their preferences, interests, and needs.

Instead of delivering the same ad to everyone, brands can adapt creative, messaging, timing, and channel selection to reflect what matters most to each user in that moment.

In doing so, marketers can move away from broad, one-size-fits-all targeting and deliver on the long-standing promise of modern digital advertising, allowing brands to reach the right person with the right message at the right time.

Why Is Personalization Important in Digital Marketing?

Personalization now plays a central role in how brands build attention, loyalty, and growth.

According to StackAdapt’s report on The State of Personalization in Digital Marketing, 87% of brands plan to increase their investment in personalization over the next year.

The reason: increased engagement, stronger customer relationships, and more efficient media spend.

Here are some of the key benefits driving that shift:

Higher Engagement and Attention

Consumers encounter thousands of ads throughout each day. Personalization helps brands cut through that noise by being more relevant. 

When messaging reflects someone’s actual interests, behaviors, or actual purchase intent, it’s far more likely to earn their attention. In fact, EMARKETER says that 76% of consumers are more likely to pay attention to ads that are relevant to them.

Stronger Revenue Growth

Personalization isn’t just about optimizing campaigns for higher engagement. Ultimately, it’s about accelerating growth. Research from McKinsey found that fast-growing companies generate 40% more revenue from personalization than their slower-growing peers. 

Similarly, our recent research, The State of Personalization in Digital Marketing, found that organizations see greater gains when activating personalization across more channels and deeper in the funnel. High-performing brands are also more than twice as likely to use personalization in emerging formats like connected TV (CTV), proving that broader, more integrated strategies drive stronger results and higher growth.

Better ROI and Marketing Efficiency

Broad targeting can lead to wasted impressions and underperforming campaigns. Personalization improves marketing campaign efficiency by directing budgets toward audiences and messages most likely to connect, helping marketers maximize performance while minimizing unnecessary ad exposure.

More Understood and Valued Customers

At its core, personalization signals to a consumer that a brand is paying attention to their wants and needs. When messaging reflects real interests or behaviors, consumers are more likely to feel seen and understood, strengthening emotional connection and brand perception over time. 

In fact, a study from the IAB found that 79% of consumers feel more positive toward brands and retailers that tailor ads to their personal preferences.

Increased Loyalty and Retention

Personalization extends beyond the first interaction. When brands consistently deliver experiences that feel thoughtful and tailored to each individual consumer throughout the entire customer journey, they’re more likely to return, engage, and build deeper relationships with a brand or business, translating into long-term loyalty.

Less Irrelevant Noise and Friction

Not every message needs to reach every person. Personalization reduces unnecessary repetition and irrelevant creative, streamlining the customer journey and making ads feel more intentional rather than intrusive (although, as we’ll get into later, it’s important to strike the right balance).

How Does Personalization Work in Digital Marketing?

At a high level, personalization in digital marketing follows a simple principle: use data to inform decisions about who sees what, when, and where.

In practice, especially within programmatic advertising, that process is powered by 1st-party data that captures real customer behaviors and intent signals, marketing automation that activates those insights in real time across channels, and continuous optimization that refines targeting and creative to improve performance.

Here’s how it typically works:

1. Data Collection

Personalization starts with data. This can include 1st-party data such as demographic data (age, gender, or company details), website behavior, past purchases, CRM records, and in-app activity, along with contextual and environmental data like ZIP or postal code, time of day, device type, or the content someone is actively engaging with online.

These signals can be unified inside a centralized data layer or platform, where they’re activated in real time to inform targeting, bidding, and creative decisions across channels.

2. Audience Segmentation and Modeling

Once data is collected, it’s used to define addressable audiences based on shared characteristics, behaviors, or intent signals.

Rather than targeting broad demographic groups alone, marketers can use 1st-party data to activate highly specific segments—such as recent site visitors, online cart or checkout abandoners, high-value customers, or users actively researching a particular product category. These audiences can be dynamically updated, ensuring campaigns reflect real-time behavior rather than static lists.

Many demand-side platforms (DSPs), like StackAdapt, can also apply predictive modeling and machine learning to create lookalike audiences, helping brands scale personalization beyond known users while improving ad relevance through data-driven targeting.

3. Message and Ad Delivery

Personalization doesn’t stop at audience targeting. It extends to the message itself. 

Brands can tailor creative, offers, and calls to action based on audience signals, ensuring that what someone sees aligns with their interests and stage in the buying journey. 

Through technologies like dynamic creative optimization (DCO), marketers can take this a step further: automatically adjusting headlines, visuals, product recommendations, and offers based on audience attributes, context, or even real-time signals, without requiring constant manual updates.

4. Cross-Channel Orchestration

Personalization doesn’t have to be confined to a single touchpoint, or even a single channel type, whether earned or owned.

Using AI-powered marketing platforms like StackAdapt, marketers can coordinate messaging across channels—retargeting users exposed to a CTV ad with display, following up with native placements, or triggering personalized email sequences based on site behavior or engagement.

Rather than treating each channel as a silo, cross-channel orchestration creates a more cohesive experience, reinforcing messaging as users move from awareness to consideration to conversion.

5. Measurement and Optimization

Personalization is an ongoing process, not a one-time setup.

Through A/B testing, foot traffic studies, and cross-channel attribution, marketers can measure how personalized messaging influences engagement, conversions, and revenue.

Those insights can be used to refine audience segments, creative variations, and bidding strategies over time, creating a feedback loop that continuously improves performance.

Common Personalization Challenges and Pitfalls

While the benefits of personalization in digital marketing are hopefully obvious by now, executing it effectively is far more complex. 

From disconnected systems to measurement gaps and creative demands, many teams struggle to operationalize personalized marketing campaigns in a way that is scalable, measurable, and sustainable.

Here’s where the biggest challenges are:

Siloed Systems and Fragmented data

While brands are collecting more data than ever before, putting that data to work across channels is still easier said than done.

Customer information often lives in separate platforms, such as CRMs, adtech platforms, and analytics tools, making it difficult to connect signals and act on them in a coordinated way. 

According to our recent survey, 42% of brand marketers and 47% of agency marketers say fragmented systems and limited platform integrations are the biggest obstacles to effective personalization at scale.

StackAdapt’s Data Hub helps eliminate fragmentation by bringing 1st-party CRM, email, and behavioral data into one centralized environment, making it easier to activate audiences across both owned and paid channels. 

Instead of managing disconnected tools, marketers can segment audiences, personalize messaging, and measure performance across the full customer journey—all within a single platform. To learn more, speak with our team.

Gaps in Measurement

In addition to fragmented systems and disconnected data, the results from our recent research found that brand and agency marketers face another common challenge in personalization: limited tracking and attribution.

Roughly one-third of brands and agencies identify measurement as their biggest obstacle, followed closely by siloed systems. Without clear cross-channel visibility into performance, nearly three in four agency marketers (77%) say they struggle to demonstrate the impact of personalized campaigns to clients, making it harder to justify continued investment.

That’s beginning to change. As platforms evolve toward more unified and connected measurement capabilities, marketers are gaining the ability to tie engagement across channels back to real business outcomes—moving beyond last-click metrics and toward a more complete view of performance.

Scaling Creative Without Sacrificing Consistency

Another common challenge is scaling creative to match the level of personalization marketers aim to deliver.

Tailoring messaging for different audience segments, funnel stages, and channels can require multiple assets, copy, and creative variations, which is not only resource-intensive but also makes it harder to maintain consistency, quality, and brand voice across campaigns.

Automation helps manage that complexity, but it introduces a new tension: how to scale production without losing the human touch that makes personalization effective in the first place.

Increasingly, generative AI is addressing that challenge, helping teams produce and adapt creative more efficiently while still leaving room for the strategic oversight and nuance that personalization requires. More on that below.

Privacy Concerns and Personalization Fatigue

As personalization becomes more sophisticated, it also raises important questions around privacy, transparency, and consumer comfort.

According to EMARKETER, consumers increasingly find personalized ads invasive, regardless of the type of ad they’re seeing, with a study finding that 44% of Gen Z consumers aged 16–24 and 50% of Gen X consumers aged 45–54 view personalized ads negatively. (Interestingly, elder Gen Zers and millennials showed the highest levels of openness to personalized advertising, indicating that attitudes toward personalization often reflect the digital environments different generations have grown up in and their baseline expectations around data use.)

In some cases, hyper-personalization can cross a line. When messaging references highly specific behaviors or feels overly precise, it can feel intrusive instead of impactful, creating the sense that a brand knows more than it should.

Part of the discomfort around personalized advertising stems from a lack of clarity. Many consumers still don’t fully understand what they’re agreeing to when they share their data, or how that information is ultimately being used to shape the ads they see.

For advertisers, the solution isn’t to abandon personalization, but to approach it more deliberately. That means being clear about data practices, where data is collected and how it’s activated, and reserving deeper forms of personalization for moments where it genuinely improves the experience, not simply because the technology makes it possible.

How Is AI Transforming Personalization in Digital Marketing?

AI is emerging as an essential tool for personalization. But our recent research shows that most marketers are still early in their journey, with nearly three-quarters of brands and agencies using AI on a limited or moderate scale. 

That said, nearly all brands (93%) and agencies (94%) agree that AI is increasing the speed and efficiency of programmatic marketing workflows when it comes to personalization.

To do that, AI is primarily being used in two ways:

• AI-powered audience targeting and segmentation, where AI analyzes behavioral and engagement signals across touchpoints to refine audience segments and anticipate actions such as purchase intent.
• AI-assisted creative, where generative AI helps marketers produce and adapt copy, visuals, and offers at scale—making it possible to tailor messaging for different audience segments and funnel stages without adding significant production time or cost.

But using AI on its own isn’t a strategy. To improve performance, it ultimately comes down to how these capabilities are applied.

What Are Some Personalization Strategies in Digital Marketing?

Here are a few ways brands and agencies are putting personalization into practice across channels using StackAdapt:

Target Users Who Abandoned Carts or Checkouts

According to StackAdapt’s The State of Personalization in Digital Marketing, email remains the foundational channel of most personalization strategies, with 47% of brand marketers surveyed saying personalized email campaigns are their primary method for driving results.

One way to extend that strategy beyond the inbox, especially for retail and e-commerce brands, is through coordinated cart abandonment campaigns.

StackAdapt’s cart abandonment marketing helps brands re-engage shoppers who added products to their cart but didn’t complete a purchase.

Using the StackAdapt Pixel (or a direct Shopify integration), key on-site interactions—such as product views, add-to-cart actions, and checkouts that were started but not completed—can be captured for audience building and retargeting.

When a user abandons their cart, an audience segment can be built automatically and retargeted—not just over email, but programmatically across channels, such as display, native, CTV, audio, and more.

This approach can reinforce consideration and encourage shoppers to return and complete their purchases, with ads dynamically adjusting to show previously viewed products, updated pricing, and new offers.

Scale Personalization Through DCO

Rather than treating creative as a static asset, AI is increasingly being used to personalize campaigns by adapting creative through DCO.

With DCO, a single ad creative can generate hundreds or even thousands of variations, adjusting elements such as messaging, imagery, format, and product recommendations in real time to reflect audience signals and context.

Within StackAdapt, self-serve DCO allows brands and agencies to use AI to dynamically assemble creative using live product feeds and campaign inputs, ensuring that each impression aligns more closely with user behavior and intent.

Instead of manually rotating versions and waiting for results, the platform continuously identifies what resonates with different audiences. It scales those variations automatically, making it possible to personalize creative while maintaining consistency across channels.

Taken a step further, marketers can layer DCO into coordinated campaigns using cross-channel orchestration to personalize messaging in real time. 

For example, after a defined time delay, a reminder email can be triggered while a programmatic campaign runs alongside it, reinforcing the message with updated offers or relevant product reminders on other channels. 

With unified reporting and multi-touch attribution, teams can understand how these touchpoints work together to recover revenue and improve overall return on ad spend (ROAS).

Use Contextual Targeting for Privacy-First Personalization

Not all personalization strategies need to rely on user data.

Contextual targeting places ads based on the content someone is actively engaging with, aligning messaging with what they’re reading.

Instead of depending on browsing history or personal identifiers, tools like StackAdapt’s PageContext AI analyze the language and themes of a page to determine whether a topic is genuinely the main focus of the article. It then prioritizes ad placements on pages that are actually about that subject—not just pages that briefly mention it—to ensure the ad appears where it’s most relevant to the reader.

For example, a running shoe brand can appear alongside detailed marathon training guides or shoe comparison reviews, rather than an article that casually references running.

In doing so, marketers can personalize ads based on the context of the content someone is consuming, rather than relying on historical user data to determine ad delivery.

Examples of Personalization in Digital Marketing

Personalization in digital marketing isn’t theoretical. Here are a few examples of how StackAdapt clients delivered more personalized, high-performing campaigns:

Personalized Creative Drives Performance at Scale

Re-engaging high-intent shoppers is one of the most effective ways to improve campaign efficiency.

Global Industrial, a leading distributor of industrial and commercial products, wanted to reconnect with users who had added items to their carts but never completed a purchase. Working with Vallo Media, the team used StackAdapt’s Dynamic Creative Optimization (DCO) solution to serve display ads tailored to each user’s browsing behavior, increasing click-through rates by 60%.

AI-Powered Travel Targeting Improves Efficiency

Reaching high-intent travelers at the right stage of their planning journey is critical for destination marketers looking to drive both awareness and visits.

The Hong Kong Tourism Board, in partnership with Dentsu, launched a programmatic campaign through StackAdapt to promote the Hong Kong Wine & Dine Festival to international audiences. 

Using contextual targeting across sites like Expedia, Skyscanner, and Tripadvisor, the team delivered tailored messaging aligned with users’ travel intent, reducing cost-per-click by 80% while significantly increasing engagement.

Creative-Led Personalization Improves Performance

Silicon Valley Growth Agents partnered with Travelpro and SWISSGEAR to launch a programmatic campaign through StackAdapt designed to drive online sales among both luxury and value-focused audiences. 

Combining tailored creative built by StackAdapt’s Creative Studio team with 1st-party data and custom audience segments, the brands delivered more relevant messaging at scale, increasing ROAS by 89% and 93%, respectively.

What’s the Future of Personalization?

Personalization in digital marketing is evolving rapidly. Unsurprisingly, AI is largely responsible for that shift.

According to survey respondents in StackAdapt’s The State of Personalization in Digital Marketing, over the next two to three years:

• 63% of agencies say AI will accelerate their creative production and optimization.
• 56% of brands expect the greatest gains to come from improved measurement and attribution.
• Nearly half of both agencies and brands expect cross-channel orchestration to play a critical role in driving more cohesive customer experiences.

But you don’t have to wait that long.

To see how StackAdapt can help you deliver more personalized campaigns and drive stronger performance across channels, speak with our team.

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