Report

The Future of AdTech

There’s a specific alchemy to advertising. Its success depends on visibility and relevance, but in the wrong doses, these qualities can become off-putting. Advertisers are often walking a tightrope between opposites: personalization and privacy, creativity and automation, and – the favorite refrain of budget meetings – doing more with less.

This push-and-pull reflects the growing pains of an evolving industry; the old playbook no longer applies. So, let’s talk about the new playbook.

Off the bat, there’s privacy-conscious personalization. Businesses' ability to synthesize their zero- and first-party data, and securely share it with third-party tools (as seen with server-side APIs), will set them apart. It will help solve one of the ongoing challenges of advertising (identity resolution), while also building better lookalike audiences to attract new, engaged customers.

Then, there’s the rapid advancement of AI. Conversational chatbots, automatically generated creative content, and fine-tuned predictive analytics are all poised to be key AdTech players.

For businesses, this will bring opportunities for growth along with a mix of old and new challenges. We reached out to leaders in our partner network to better understand what’s ahead; the emerging technologies, consumer trends, and strategies that will define this new era of AdTech.

What are the biggest challenges in AdTech right now?

The most pressing concern for advertisers has always been to know the customer. What products they’re interested in, the channels they prefer, the messaging that will resonate the most. That's become more complicated.

Nearly everyone uses multiple devices in their daily life, and interacts with ads both digitally and in-person. How do you stitch all those interactions together to understand the impact of your ad strategy?

Privacy regulations, the unreliability of third-party cookies, and ad blockers have all curtailed the amount of data that used to come easily to advertisers. As a result, targeting and reporting has become less accurate (if the bulk of your strategy still depends on these old tactics). But remember that the quality of your data will always be the winning piece – and companies have found even greater success today by focusing on data fidelity.

Illustration: Grow customer lifetime value with unified data

Dio Favatas, Head of Identity Solutions and Marketing Clouds

Even though Chrome walked back its decision to deprecate third-party cookies, other browsers and regulators continue to crack down. Advertisers that do not start implementing alternatives will struggle to achieve a holistic view of their customers, which is necessary for personalized targeting, measurement, and attribution at scale.

Crosswalk IDs and currencies are challenging in AdTech because different platforms use proprietary identifiers, making it difficult to unify user data for targeting, measurement, and attribution. Without standardized mapping, ad performance and user behavior across channels or networks can become fragmented and inaccurate.

Traditional managed services (e.g., Google Ad Manager, Citrus) are often resource-heavy and inefficient, eating into ad budgets with high CPMs. In contrast, composable platforms (e.g., Koddi, Kevel) offer greater flexibility and cost efficiency but require technical integration, leading to challenges in migrating from legacy systems and balancing old and new models.

Behavioral and psychographic targeting relies on user data for personalization, while contextual targeting focuses on ad placement based on content, not user behavior. Combining these two strategies in retail media and advertising requires balancing personalization with privacy and relevance, creating challenges in achieving optimal targeting without infringing on user trust or data privacy.

Scaling first-party data and privacy compliance

Traditional ad tracking has been completely upended over the last few years, whether it’s regulations like the GDPR and CCPA or technological changes like Apple’s App Tracking Transparency (ATT), Intelligent Tracking Prevention, or third-party cookie deprecation.

Advertisers are hit twice. First the ad platform’s machine learning algorithms are starved of data, increasing cost per action (CPA). Secondly, advertisers struggle to measure performance consistently across channels, rendering it impossible to make informed spending decisions. There’s no silver bullet solution. To succeed in this environment, advertisers need to combine comprehensive first-party data, a good understanding of privacy law, and a variety of different measurement technologies.

A bald man with glasses smiling, framed by a light green circular border.

Identifying the consumer across channels and devices

The biggest challenge in AdTech right now is connecting a consumer’s identity across multiple touchpoints. The ability to track customers from QR codes to clicks to taps to views on your TV — that’s where the complexity lies.

Consumers are constantly shifting between devices, apps, and platforms, and AdTech has to figure out how to tie all that together into a cohesive identity. Companies like LiveRamp have helped us tie some of this together, but with Apple’s strong privacy stance, it can get very murky. Beyond that, understanding a consumer’s journey is becoming more difficult because people are nomadic and experiential.

That’s why we need to balance data-driven strategies with a deeper understanding of emotions and brand loyalty. We’re all driven by emotion and buy what we feel connected to. Data is critical, as are feelings and community in today’s world. Connecting my marketing efforts to your purchases isn’t getting any easier. Yet, companies that adopt the right reporting and tracking methods gain the insights they need to succeed.

Smiling man in a blue shirt, sitting with a background of an ocean view, framed in a circular mint green border.

What are some of the most promising AI technologies in AdTech?

Advancements in generative AI mark a significant turning point in how we work, how we learn, how we interact with one another…honestly, everything.

Our partners discussed several use cases for AI: from machine learning algorithms tailoring ads in real time, to AI agents walking someone through a purchase. With AI, there's a huge advantage around early adoption, but don't forget that with high potential comes a high level of risk. If AI gets something wrong, it's wrong at scale. So while AI may seem futuristic, it's still dependent on what might be considered "the basics": effective data governance and privacy by design.

Illustration: The predictive AI advantage to stronger ROI

Diagram showing AI in AdTech, highlighting Automation, Machine Learning, and Generative AI.

At its most basic, AI powers the automation behind real-time bidding and audience targeting, making thousands of decisions per second to optimize ad placements and spend.

With advancements in machine learning, AI can now analyze huge datasets to improve search relevance and content discovery, and match ads with the most receptive audiences (e.g., through behavioral analysis).

More recently, generative AI has enabled dynamic ad content generation, helping to better personalize ads moment to moment. Conversational chatbots are also playing a key role, using natural language to help users better convert after clicking on an advertisement.

Predictions and dynamic, real-time personalization

AI is a game-changer in AdTech, no doubt. One of the most promising applications is AI-driven personalization. AI can now analyze massive data sets in real time, predict consumer behavior, and optimize ad targeting in ways we couldn’t imagine before.

The biggest blocker here for companies is clean and accurate data. Suppose your first-party data is badly structured and poorly organized. AI cannot use it well because it needs large amounts of data to perform accurate predictions and make effective personalizations.

AI is now helping us predict which audiences ads should target. As consumers move from one audience to another, they receive a different personalized experience. AI can also help you manage your ad creative, design visuals, generate videos, and then control which audiences your users belong to. You will continue to see highly personalized and tailored experiences for users in ads.

You no longer have to be Citibank or Progressive Insurance to have hyper-personalized ads based on weather, economic status, or previous behaviors. AI, with access to enrichment and reliable behavioral data, will create these types of campaigns for smaller brands.

How can you balance privacy with personalized advertising?

Advertising has always hinged on being perceptive, whether it’s to a person’s interests or influential trends. However, privacy regulations like the GDPR and CCPA set a new standard: customers want more control over who's collecting their data and how they're using it. Companies need to have clear and easily accessible opt-in and opt-out mechanisms for users, along with complete control over how they're collecting and sharing data across their tech stack.

And while genAI and ML have opened up new possibilities in terms of your reach and degree of personalization, there is also a greater risk when it comes to privacy compliance. The future of AI will hinge on this balance of privacy by design, ethical AI, and personalization.

Illustration: Whenever, wherever, your data belongs together

You need a privacy foundation before introducing AI

Digital Advertising has come a long way in the last 30 years since the static banner first displayed in October 1994. Retargeting followed and then real-time auctions sometime after that.

However, the tension between relevance and perceived invasion of privacy has never been greater. So whilst AI will solve the production issues standing in the way real-time ads built just for you, the question might not be about ‘if I can’ but ‘should I?'

If you don’t have proper data governance and user consent frameworks in place, the efficacy of your AI won’t matter because it will come at the cost of customer trust.

Portrait of a smiling man with light hair, wearing a blue shirt inside a circular frame.

What are some privacy-conscious advertising tactics?

Diagram showing data flowing from blocks to a central hub then targeting a group of users.

Lookalike audiences based on zero- and first-party data

Zero- and first-party data refers to the information that customers willingly provide to brands, like through sign-up forms, account registrations, or via web interactions. This data is considered the most accurate and privacy compliant because it's shared with users' consent and unique to each business. This data can be used to create lookalike audiences based on the characteristics of their most engaged customers.

Notification showing user viewed a product for 26 seconds with icons representing a person and a database.

Server-side integrations

In server-side tracking, data is collected and processed on the server rather than in the user's browsers (i.e., on the client-side). This helps avoid issues with cookie deprecation and ad blockers, which make client-side tracking less reliable. Server-side integrations (like Conversion APIs) allow for fine-grained control over what data is tracked and shared with ad platforms like Google, Facebook, and LinkedIn.

Flowchart indicating interest in sports equipment and 75% purchase propensity in 14 days.

Data clean rooms

Data clean rooms provide a secure place for multiple parties – like advertisers and data providers – to share anonymized and aggregated data sets to gain deeper insights into customer behavior without exposing personally identifiable information (PII).

Are third-party cookies still relevant?

The deprecation of third-party cookies has loomed over the AdTech industry for years, threatening to unravel strategies that had once been considered the backbone of digital advertising. Then Google decided to extend their shelf-life. It should have been a collective sigh of relief, but during all this back-and-forth third-party cookies became – in many ways – irrelevant. Aside from the privacy regulations businesses need to adhere to, consumers have more control over opting out of cookie tracking, or using ad blockers and VPNs.

Illustration: hero, wherever, your data belongs together

Bar chart showing US adults' responses to cookie pop-ups

Third-party cookies may not be deprecated, by users are still opting out

While marketers have been waiting to determine their post-cookie future based on what Google does in Chrome, they have completely forgotten that they are missing half the data to support their measurement, activation and collaboration use cases. What most people seem to forget is Chrome has a ceiling in user adoption, and more people are favoring privacy centric browsers. Chrome Mobile has 44% of US market share, while Chrome Web browser has 52%.

Ever since Intelligent Tracking Prevention was introduced by Apple in 2017, North American marketers have been blind on 32% - 49% of their marketing efforts because those cookies have been outright blocked. In the US alone, Apple’s Safari browser dominates mobile phones with 49% market penetration, while Safari web-client has 32% market share, followed by Mozilla’s Firefox with 4.21% (Firefox outright blocks third-party trackers just like Safari does). And now that Google announced that they will not deprecate cookies, but they will unveil a new Chrome experience that allows users to swiftly disable third-party tracking, alongside with Privacy Sandbox rolling out next year. There will essentially be no cookies left to action on.

Marketers seriously need to get with it, and ASAP.

While we don’t know what the new user consent framework will look like in Chrome. I think it will be something similar to what Apple did with App Tracking Transparency. With ATT, developers are required to obtain explicit consent, and subsequently users just opt-out of cross-app and cross-device tracking.

Dio Favatas, Head of Identity Solutions and Customer Data @ Tredence Inc

What advancements in attribution and measurement do you think will be most significant in the near future?

Attribution has always been a headache for marketers. Last-touch attribution can gloss over the influence of every touchpoint that came previously. Or how do you account for the lag that often occurs between a touchpoint and a person making a decision to buy?

When speaking to our partners a couple paths forward emerged, such as mixed media modeling, having the necessary integrations with partners, and iterating quickly based on first-party data.

Illustration: Personalize communications

Deeper integrations with platforms and mixed-media modeling

We’re increasingly seeing ad networks push advertisers to build deeper integrations with their platforms. Previously you only needed a Tag Manager and a few pixels. Now each ad network seems to have a different solution for optimizing campaign performance: conversion APIs, CDN proxies, and first-party data-matching to name a few. This can add significant overhead for advertisers who now have to maintain a large catalog of integrations. The ongoing maintenance cost is often underestimated with semi-regular updates usually required. A CDP can be a great way of outsourcing this ongoing maintenance whilst ensuring you can continue driving improvements in advertising efficiency.

As traditional tracking becomes harder, we’re seeing more advertisers try to combine multiple solutions: starting with multi-touch attribution based on strong first-party data, then adding media mix modeling to fill in the gaps, and finally, using experiments like geo-testing to measure the incremental impact of advertising. Currently combining all three is challenging and so we see many brands struggling with a confusing mix of conflicting signals. Hopefully combining the three will become easier by continuing to use first party data as a foundation, then layering on other approaches to fill in the gaps and measure incrementality.

Retarget Customers with the Facebook Conversions API

Flowchart showing the process of collecting user preferences and sharing data with downstream tools.

Step-by step guide

How will consumer behavior shape the future of AdTech?

Just as AI will reshape the way businesses advertise, it will also influence the way people buy things. Repeatedly, we’ve heard leaders talk about the idea of personal AI assistants that people could use to shop for them, or even plan a vacation (from buying flight tickets to providing an itinerary). While it seems futuristic, businesses need to consider the ways that channel preferences and touchpoints are changing.

Meta, for example, has been making big investments in wearables and augmented reality. How would that influence advertising and commerce down the line? While no one can predict the future, keeping a pulse on trends like this will allow you to anticipate where AdTech is headed (and ensure you won’t be left behind).

Illustration: Deepen understanding with a 360° view of your customers

AI assistants for consumers

When it comes to consumer behavior, the big shift is that AI will separate the consumer from the company. We’re moving toward a world where AI will make decisions for consumers on their behalf. Imagine this: instead of consumers clicking ads or choosing products themselves, they will rely on AI to make those decisions based on past behavior and preferences.

Not only will this happen based on past experiences, but many people are now saying, “Hey Siri, what are the 5 best Italian restaurants near me?” Siri AI will reply with a list and even book your reservation. This behavior change is a considerable unknown for advertising. The challenge for AdTech is adapting to this new intermediary between the consumer and the company. Consumers’ emotions and choices are being abstracted away, and brands will need to figure out how to influence those AI-driven decisions in the future.

Diagram of an AI chatbot assisting Brenda Jones with pizza place recommendations in San Francisco.

Twilio’s AI assistants are customer-aware, autonomous agents. Learn more about AI Assistants and our Twilio Alpha program.

Conclusion

Advertising is often viewed as necessary but expensive, but the truth is: businesses can’t buy into the idea of advertising as a budget deficit. Marketing is meant to be a growth center, not a cost center. To get there, businesses need to make sure they’re focusing on the right metrics. Like Simon mentioned above, so much of marketing in the past has been an educated guess. We think this person should be interested in this. While there will always be some element of uncertainty, technology has advanced to a point where we can have even greater precision in our hypotheses.

Here are the greatest takeaways I’ve noticed in conversations with our partners:

Focus on collecting the right data and data fidelity. This is the first-party data that customers knowingly and freely exchange with your business. It’s the most accurate, and also builds trust. This is how you ensure personalization feels spot-on, not invasive.
Synthesize this data into customer profiles to better understand a person’s cross-channel journeys. From there you can start to pinpoint which channels have the greatest ROI, and which ones are a cost sink.
Focus on having the right integrations with the right partners, and that you have complete control over the data you share with them (e.g. server-side APIs). This is crucial to comply with privacy regulations.
Don’t underestimate the impact of generative AI. Figure out how it can best serve your team and your customers, and know that early adoption will have a compounding effect in what you learn and what you can accomplish.

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