With personalized ads outperforming the more traditional kind, ad personalization has become the standard for e-commerce brands. At the same time, though, some consumers are resistant to the perceived intrusion that often comes with it. How can marketers balance ad personalization with data privacy? Let’s explore the basics of ad personalization and then dive into the best ways to deploy it in a rapidly changing digital landscape.

What Is Ad Personalization?

Ad personalization is the practice of tailoring ads to specific consumers based on their demographic data, past online behaviors, or interests. It goes beyond simply addressing a prospect by their name in an email, or recommendations of relevant products that may match thematically with items already in their online shopping cart: Ad personalization serves up ads based on context, lookalike audiences, and other demographic data to deliver highly relevant content when consumers are ready to buy.

Although, as stated, these ads can rub consumers the wrong way — on average, 45.6% of consumers across generations in the U.K. and the U.S. have a “negative” reaction toward personalized ads, according to a survey from Verve and Censuswide — they’re still highly effective. Indeed, the same survey shows that 76% of consumers said they pay attention to relevant ads, while 66% said they’ve helped them discover products.

Per Attentive, 81% of shoppers said they ignore ads that aren’t relevant to them, while an overwhelming majority (96%) said they are more likely to purchase from a brand that shares a personalized message.

How Does Ad Personalization Work?

Ad personalization works slightly differently based on the platform or media, such as social media vs. display ads, SMS, paid search, paid social, or other formats. Essentially, though, ad personalization collects data, often using cookies or pixels to track user behavior and actions across platforms. In some cases, it can bridge the gap between social media, e-commerce sites, the open web, and mobile.

Ad platforms and AdTech tools collect demographic, geographic, and contextual data to serve relevant ads at the right time. This way, featured ads closely match users’ interests and behaviors.

Benefits of Ad Personalization

Ad personalization has proven to be an effective means to reach consumers with relevant messages, especially as AI and large language models (LLMs) become better at understanding how specific content relates to user data.

For instance, through contextual clues, LLMs can determine whether an article about “apple” refers to a computer, the tech company’s stock, or a fruit. In relation to advertising, highly relevant ads can reflect a specific location, time of year or season, or product category. Ad personalization can also reflect broad or specific knowledge about the user, such as their age, interests, gender, occupation, or income level.

Greater Audience Engagement

As noted above, more than three-quarters of consumers in the U.S. and U.K. pay attention when ads are relevant to their interests, while 81% ignore irrelevant messaging. Capturing engagement in today’s fast-paced, scroll-happy world represents a chance at success versus languishing in obscurity.

Improved Conversion Rates

Once you’ve captured attention, you’ve increased the odds of a successful conversion — whether that means an opt-in, a share, or a sale. When brands use personalized ads, conversion rates can rise by 10% up to 275% or more. Amplifon (a hearing device retailer), e.g., saw a 29% boost in conversion rates using personalized ads.

Publishers hosting ads for brands also see improved results through AI personalization. For instance, the USA Today Network increased their click-through rate by 47% using personalization features.

Customer Loyalty

The benefits of ad personalization can also be measured through intangibles, or metrics that are slightly harder to quantify, like customer loyalty. Brands who use personalization often see more repeat customers, per LinkedIn.

Types of Ad Personalization

Predictive Personalization

Predictive analytics use past consumer behavior and AI to deliver relevant ads based on predictions that the user will behave in the same way in the future. For instance, someone who just purchased an electric bicycle might have a high likelihood of also buying an app-enabled smart helmet to go with it.

Predictive personalization can also work well for upsells; someone who just purchased season tickets to a regional theme park may also be interested in a meal package. E-commerce retailers can use predictive personalization to suggest clothing of the right size and style based on past purchases. The more historical data you have, and the better the analysis technology, the more relevant your results.

Lookalike Marketing

Lookalike marketing is a variant of predictive personalization, where marketers look at various characteristics shared by current customers, and serve ads to similar consumers in the hopes of expanding reach. Lookalike audiences can be used for ad personalization, but the success of a campaign hinges on whether or not consumers in similar demographics behave the same way.

Behavioral Targeting

Behavioral targeting is another form of predictive personalization based on a user’s past behavior, such as opened emails, abandoned carts, web browsing, or prior ad clicks.

Retargeting

Retargeting is a form of ad personalization based on user behavior. It focuses specifically on abandoned carts, people who engaged with an ad but didn’t convert to becoming a buyer, or someone who visited your website and didn’t make a purchase. Marketers can track their actions on-site to target them with highly relevant, personalized ads and even money-saving deals.

Contextual Targeting

Contextual targeting can help marketers serve up personalized ads without any specific demographic or user behavioral data. Instead, these personalized ads appear adjacent to related content. For instance, an ad for a Nissan Pathfinder might appear next to an article highlighting the 10 best SUVs for families.

First-Party Data Personalization

First-party data personalization relies on information collected from the brand, publisher, or website without relying on the use of cookies or tracking pixels. First-party data eliminates privacy concerns and enables highly targeted advertising even in cookieless environments.

Where to Use Ad Personalization

Search Engine Results Pages

You can personalize Google Ads campaigns, as well as paid search on other search engines. Google allows you to personalize campaigns based on keywords, audience demographics, user preferences, web activity, location, and more.

Google also allows publishers and users to change their ad experience in My Ad Center; people can select interests, limit sensitive ad topics, or even turn off their personalized experience. By allowing users greater control of their ad experience, it can help advertisers serve the right ads to Google, YouTube, and Maps users.

Social Media

LinkedIn, Facebook, Instagram, and other social channels also allow marketers to personalize ads through retargeting or based on specific demographics. It often takes trial and error for marketers to dial in on the right parameters: If a group is too large, you’ll target users who don’t care about your brand; if the group is too small, you won’t see any results.

Your Website

Personalization on e-commerce websites takes the form of product recommendations and upsells, as well as messages enticing consumers to revisit their abandoned cart.

The Open Web

While search and social platforms operate within closed ecosystems, the open web enables advertisers to reach users across a broad network of trusted publishers and environments. Personalization for programmatic ads, for example, leads to better results by serving up highly relevant content at the time a user is most likely to make a buying decision. Realize, as another example, uses powerful AI algorithms that rely on context cues to place relevant, personalized ads without needing to harvest user data.

How to Personalize Ads

Understand Your Demographic

Before you can find relevant users to share your ads with, you have to understand their shared characteristics. Understanding your audience is key to a successful campaign: Dive deep into your ideal buyer personas, including age, location, interests, income, and the type of content they typically consume.

Choose How You’ll Find Your Ideal Audience

Which characteristics matter most to find your ideal audience online? Are you personalizing ads based on the context of information your ideal consumers are reading or viewing? Are you serving up personalized ads based on their interests or other demographic data? Are you using cookies to track consumers across the open web? Or, are you relying on lookalike audiences to find people with similar interests and characteristics as your best customers?

Develop Creative That Resonates With Your Audience

Once you’ve found your audience, the key to a successful campaign is creating content that resonates with them. It doesn’t end with the ad in their social media feed or on a search engine results page: The landing page connected to the ad should be compelling, easy-to-read (and act on!), and should mimic the look and feel of the ad so users feel a sense of continuity and familiarity.

Transparency Is Key to Building Trust

Remember that stat saying that 45% of web users react negatively to personalized ads? If ads hit too close to the mark, they can feel creepy — almost as if advertisers are following a consumer’s every move. Yet, the vast majority of consumers also acknowledge that highly relevant ads are useful. Advertisers need to find a balance, delivering the exact content shoppers need without seeming intrusive. By revealing how you’re collecting data and what you’re using it for, you can set people’s minds at ease. As ever, transparency builds trust.

How to Leverage Personalized Ads for Cross-Channel Campaigns

Design a Consistent Cross-Channel campaign

Consistency is key to brand recognition and building trust. Your cross-channel campaign should use a similar look and feel across channels, including the same branding, a cohesive color palette, and similar copywriting.

Use Programmatic Advertising to Reach Audiences on Multiple Channels

Programmatic advertising follows users across the open web based on contextual signals. For instance, someone researching new SUVs might visit sites like Consumer Reports, Edmunds, and car review websites. Ads placed on each of these sites build brand familiarity; if someone researching new SUVs sees the Nissan Pathfinder enough times, they are likely to consider purchasing the vehicle.

Data-driven insights can help marketers scale their most successful campaigns across channels to expand reach, reach consumers in the decision stage of the sales funnel, and increase their click-through rates and ROAS.

Key Takeaways

Personalized ads in multichannel campaigns reach audiences in the action phase of the sales funnel, when they are ready to buy. Transparency in how you’re harvesting and using consumer data is important to maintain trust. Ultimately, consumers agree that highly relevant advertising is effective, but they don’t want to feel as if brands are tracking their every move, either.

Frequently Asked Questions (FAQs)

Where does personalized ad data come from?

Personalized ad data can come from cookies or pixels placed on websites, first-party data collected by the brand, or demographic data collected by social media sites or search engines.

What are some marketing tools used for ad personalization?

Platforms like Realize are powerful marketing tools for ad personalization across channels, including the open web. Realize offers AI-driven Audience Matching to identify and serve ads to the audiences most likely to convert; Predictive Audiences to target high-intent users; and Dynamic Creative Integration to deliver tailored creative formats (display, vertical video, etc.) across diverse placements, which helps to match creative to user context and preferences.