By Alison Harding, VP of Data Solutions, EMEA, Lotame
The days of one-size-fits-all advertising are long gone. As Lotame’s Vice President of Data Solutions for EMEA, I see firsthand how today’s digital landscape demands precision, relevance, and measurable ROI. Ad personalisation isn’t just a buzzword—it’s a necessity for making every ad dollar work harder.
Personalized advertising drives real results. “US publishers estimated an average of 37% of their total digital ad revenue in 2023 came from personalized ads”. And consumers want personalisation, too. “71 percent of consumers expected companies to deliver personalized interactions, and 76 percent got frustrated when it didn’t happen.”
But the industry is changing. Third-party cookies are disappearing, privacy regulations are tightening, and consumer expectations for transparency are rising. Businesses can no longer rely on old-school tracking methods. Instead, success in ad personalization now requires a multidimensional approach—one that integrates contextual targeting with first-party data and behavioral insights. By combining real-time relevance with audience intelligence, advertisers can enhance precision, improve engagement, and navigate the evolving privacy landscape with greater flexibility.
In this guide, I’ll break down:
- How to collect the right data for personalized advertising in a privacy-first world.
- How ad personalisation actually works, from behavioral and demographic targeting to AI-driven predictions.
- What the future holds for advertisers looking to stay ahead in an evolving ad tech environment.
By the end, you’ll have a clear roadmap for building effective, privacy-conscious, and high-performing personalized ad strategies that drive real business results.
How Ad Personalisation Works
Ad personalisation isn’t just about serving ads, it’s about serving the right ads to the right people at the right time. Behind the scenes, a sophisticated mix of data, algorithms, and real-time decision-making powers this process.
How Do I Collect Data for Personalized Advertising?
Ad personalisation completely relies on data. Advertisers collect and analyze different types of user information to create relevant ad experiences.
Effective ad personalisation starts with data collection, but with increasing privacy regulations, businesses need to be smarter and more transparent about how they gather and use consumer information.
Here are five key questions businesses can ask and answer to build a data foundation for personalized advertising:
1. Why Do I Need to Collect First-Party Data Now More Than Ever?
With third-party cookies on the way out, businesses are prioritizing first-party data, information collected directly from their own customers. This includes:
- Website and app activity including page views, product interactions, and time spent on different sections of a site.
- Purchase history on past transactions helps predict future buying behavior.
- Email and CRM data collected from sign-ups, customer inquiries, and loyalty programs.
The challenge? Convincing users to willingly share their data. Brands offer incentives like exclusive discounts, personalized recommendations, or premium content in exchange for sharing data.
2. How Do I Use Data Partnerships to Get More Data?
Second-party data is someone else’s first-party data, acquired through direct partnerships. An example would be a fitness brand partnering with a health app to gain insights into potential customers’ activity levels and interests. These data collaborations allow businesses to enhance their targeting without relying on third-party cookies.
Read more about second-party data here.
3. Can Contextual Targeting Replace Third-Party Cookies?
Since tracking users across the web is becoming more difficult, businesses are turning to contextual targeting. Instead of focusing on a user’s past behavior, ads are placed based on the content being consumed in the moment.
- A travel brand might serve ads for beach resorts in an article about tropical vacations.
- A sneaker company could target users reading marathon training guides.
Contextual advertising can help ensure relevance without the use of third-party cookies, making it a more privacy-friendly alternative. But, contextual targeting alone is often not enough to fully find and reach your ideal customers. That’s where a hybrid approach is necessary, utilizing both behavioral and contextual advertising together.
4. What is Zero-Party Data, and Why Are Consumers Willingly Sharing It?
Zero-party data is information voluntarily shared by consumers, often in exchange for better experiences. This includes:
- Quizzes and interactive surveys – A beauty brand can ask users about their skin type before recommending products.
- Preference centers – Allowing users to customize experiences by selecting what interests them.
- Loyalty program insights – Customers willingly provide data when they sign up for exclusive perks.
Unlike traditional tracking, zero-party data instills trust because consumers know they’re sharing information and understand how it will be used.
5. Can I Use AI to Build Predictive Audiences?
Businesses are also leveraging AI-driven insights to improve ad personalisation without relying on direct tracking. By analyzing patterns in first-party data, AI can:
- Predict what products a customer might be interested in.
- Group users into lookalike audiences based on shared behaviors.
- Adjust ad creative in real time based on engagement signals.
The key to success? Balancing personalisation with privacy, using data responsibly while ensuring consumers feel in control of their information.
How Does Ad Personalisation Work?
Ad personalisation isn’t just about who a consumer is, it’s about what they do, what they’re interested in at any given moment, and even what they might want next. Advertisers use a mix of real-time behavior, historical data, and predictive modeling to serve ads that feel relevant rather than random. Here’s how personalisation actually works in practice:
How Does Behavioral Targeting Turn Past Actions into Future Conversions?
One of the most common methods, behavioral targeting analyzes a user’s past interactions, search history, website visits, social media engagement, purchase behavior, and more, to determine what kind of ads they should see.
For example, if someone browses multiple online stores looking at high-end noise-canceling headphones but doesn’t make a purchase, they might start seeing ads for those exact models (or similar ones) across different websites. Advertisers can even adjust the messaging dynamically, showing a “10% off” discount to one user while displaying “Back in Stock!” to another, depending on their browsing patterns.
How Can I Use Demographics to Reach the Right Audience Without Wasting Ad Spend?
Not all ads are relevant to everyone, and demographic targeting ensures that brands reach the right segment of the population. Using data like age, gender, income, education level, and location, advertisers can fine-tune their campaigns for specific groups.
For instance, a luxury car brand isn’t going to serve ads to college students browsing for used cars. Instead, it will target professionals in higher income brackets who have previously shown an interest in premium vehicles. Social media platforms like Facebook and LinkedIn allow for hyper-specific demographic targeting, making it a staple in personalized ad strategies.
How Can You Serve Relevant Ads Without Tracking Users Across the Web?
As privacy regulations evolve and third-party cookies disappear, advertisers must adapt by exploring a mix of targeting strategies. Contextual targeting remains a valuable tool, enabling real-time ad placement based on the content a person is actively consuming. But for a more complete approach, brands are combining contextual with other privacy-conscious solutions that enhance audience understanding and engagement.
For example, an article about the best hiking trails in the Pacific Northwest might prompt ads for hiking boots or travel insurance. While this ensures relevance in the moment, advertisers can also tap into audience abundance strategies to reach consumers in more ways. By leveraging first-party data, second-party publisher relationships, and data collaboration platforms, brands can build a richer, more sustainable approach to targeting.
By balancing contextual signals with authenticated audience insights, advertisers can deliver personalization at scale—without soley relying on cross-site tracking.
Can AI Really Predict What a Consumer Will Want Next?
Powered by machine learning and AI, predictive targeting goes beyond past behavior to anticipate future needs. By analyzing patterns across large datasets, AI-driven models can determine which products, services, or content a user might be interested in, even before they actively start searching.
Think about how Netflix recommends movies you didn’t know you wanted to watch. Ad personalisation works in a similar way. If someone frequently buys organic groceries, predictive targeting might suggest ads for plant-based meal delivery services, even if they’ve never actively searched for one.
How Do Retargeting Ads Re-Engage Potential Customers Without Annoying Them?
Sometimes, consumers need multiple touchpoints before making a purchase. Retargeting, often powered by first-party data and tracking pixels, allows businesses to re-engage users who have already shown interest but haven’t converted.
For example, if a shopper adds a pair of sneakers to their cart but leaves the site without checking out, they may later see a personalized ad on Instagram reminding them about the item, possibly with a limited-time discount or a “Selling Fast!” alert to create urgency.
How Can Businesses Personalize Ads Tactfully?
The most effective ad personalisation feels helpful, not creepy. When users feel like brands genuinely understand their needs, rather than just following them around the internet, engagement rates increase. The challenge for advertisers is to find the right balance: delivering highly relevant content while respecting user privacy and avoiding the “too much, too soon” effect that can turn potential customers away.
Why Should Businesses Like Mine Invest in Ad Personalisation?
When done right, ad personalisation is a win-win for both advertisers and consumers. It transforms digital advertising from a disruptive annoyance into a seamless, value-driven experience.
How Does Personalisation Make the User Experience More Enjoyable?
No one likes irrelevant ads. Personalized advertising ensures that users see content that matches their interests, making their online experience more enjoyable. Instead of generic, mass-market ads, they get customized recommendations that feel useful rather than invasive.
How Does Ad Personalisation Help Businesses Stop Wasting Their Ad Budget?
For advertisers, personalisation means less wasted budget. Instead of spraying ads across the internet and hoping they stick, brands can target the people most likely to convert. This leads to higher return on ad spend (ROAS) and improved use of marketing dollars.
Can Personalized Ads Build Greater Brand Loyalty?
Consumers appreciate brands that understand their needs without overstepping boundaries. When done well, ad personalisation builds trust and long-term loyalty by delivering value-driven experiences instead of intrusive promotions.
What Businesses Can Learn from Spotify’s Personalisation Strategy
Spotify’s “Discover Weekly” and personalized ad placements are a masterclass in using data. By analyzing listening habits, Spotify not only curates custom playlists but also serves hyper-relevant ads. A local concert venue, for example, can target listeners who frequently stream an artist performing there. The result? Higher engagement, more ticket sales, and a better user experience.
Where Is Ad Personalisation Headed Next?
The landscape of ad personalisation is changing fast. Privacy regulations, evolving technology, and changes in consumer behavior are forcing brands to rethink their strategies. Here’s where things are headed:
How Businesses Thrive in a Cookieless World
Google is phasing out third-party cookies, following the lead of Safari and Firefox. This means advertisers will have to rely more on first-party data, insights gathered directly from users who interact with their brand. Expect to see more brands investing in loyalty programs, content-driven lead capture, and AI-driven audience segmentation.
AI and Machine Learning Will Drive Smarter Personalisation
AI already has a big role in ad personalisation, but we’re just getting started. Predictive analytics and machine learning will refine targeting even further, allowing advertisers to serve ads based on real-time behavior rather than past actions alone. The key will be making AI-driven personalisation feel natural, not robotic or intrusive.
Contextual Advertising is Making a Comeback
With third-party tracking on its way out, contextual targeting is re-emerging. Instead of tracking users across the web, advertisers are shifting toward placing ads based on the content being consumed. Think of it as a modernized version of placing an ad for running shoes in a fitness magazine, but powered by real-time AI-driven insights.
Transparent, Ethical Advertising Will Be Non-Negotiable
Consumers are becoming more privacy-conscious, and governments are responding with stricter regulations. The brands that succeed in the future will be the ones that embrace ethical advertising, clear consent methods, transparent data policies and use, and personalized experiences that offer value. Expect to see more brands giving users direct control over their ad experiences, allowing them to set preferences or opt out entirely.
First-Mover Advantage: Brands Need to Adapt Now
The shift toward privacy-first, data-driven advertising is already here, and I see the impact every day in my work with clients. Businesses that act quickly to refine their personalisation strategies will gain a strong competitive edge.
To stay ahead, I recommend brands:
- Prioritize first-party and zero-party data — Build direct relationships with customers through loyalty programs and interactive content.
- Leverage AI and predictive analytics — Use machine learning to anticipate customer needs and automate real-time ad adjustments.
- Embrace contextual targeting — Serve relevant ads without relying on intrusive tracking methods.
- Invest in transparent, ethical advertising — Ensure customers understand how their data is used and give them control over their ad experience.
I work with brands every day to navigate this shift. If you’re ready to evolve your approach and future-proof your ad strategy, let’s talk about how Lotame can help.
By Alison Harding, VP of Data Solutions, EMEA, Lotame