MadTech Explained: 3 Use Cases for Marketers and Advertisers

Data is everywhere. The challenge for marketers and advertisers isn't collecting it — it's knowing how to consolidate and leverage it effectively. MarTech platforms use data to deliver personalized messages through owned media; AdTech platforms use it to serve targeted messages in paid media. The strong push toward a single technology capable of personalization across all media is what drives the concept of MadTech.

AdTech and MarTech have evolved to the point where the boundary between them is increasingly blurry. Today's advertising-technology platforms do far more than build brand awareness and drive reach. Marketing-technology platforms like CRMs are no longer passive customer databases — they run on vast data sets and offer capabilities like social-media integration, AI-driven profiling, and much more. What both categories increasingly share is data, and that shared foundation is precisely where MadTech takes root.

What Is MadTech?

The term MadTech — a portmanteau of marketing, advertising, and technology — was coined by David Raab to describe an accelerating trend in platform development: the hybridization and convergence of AdTech and MarTech. At its core, MadTech is about data, connectedness, and the convergence of multiple technologies and data sources — including big data, the internet of things (IoT), and machine learning.

MadTech is founded on the principle of data synergy and takes the idea of a single customer view (SCV) to another level. This is achieved by de-siloing and combining related data sets into shared, easily accessible repositories enriched with data pulled from every device a single person uses — including emerging categories like IoT-connected devices and wearables. That synergy makes the constituent parts of AdTech and MarTech data more valuable and more actionable. Critically, the data is no longer locked inside individual companies' systems.

Data Synergy and Single Customer View (SCV)

A single customer view is a concept closely tied to — and sometimes synonymous with — user-centric marketing, people-based marketing, and omnichannel marketing. It is built by consolidating various pieces of customer information from multiple sources and is widely considered the foundation of MadTech.

In practice, those data sources are often scattered across different organizational departments: marketing, sales, customer service, product design, and so on. Aggregating all of that into shared repositories is what makes the SCV possible.

What Types of Data Build an SCV?

Behavioral data: total pageviews, completed goals, and URLs visited.

RTB and programmatic data: data collected during online media buys, including interactions with ads (e.g., clicks) and other user data gathered through the bidding process.

Ecommerce and transactional data: number of products purchased (online and offline), cart value, order and renewal dates, product abandonments, and returns.

CRM and offline data: provided address information, telephone numbers, email addresses, social-network data, and similar first-party identifiers.

GDPR-consent data: consent signals from users, which function as a prerequisite for many of the above data-processing activities. Under the GDPR, consent is required for specific purposes — content personalization and remarketing among them — making consent data a non-negotiable input to any MadTech architecture.

With SCVs in place, companies can put them to work across both their advertising and marketing activity.

How MadTech Came to Be

MadTech's rapid rise in prominence isn't surprising given its promised benefits for advertisers and marketers alike. But it isn't an entirely new concept. Data management platforms (DMPs) have existed for some time and already match much of the definition of a MadTech platform.

Here's how the three categories compare:

MarTech uses marketing databases — CRMs, CDPs, and similar tools — that store detailed information about interactions with identified individuals.

AdTech typically relies on DMPs and stores information gathered from many sources. DMPs combine capabilities inherent in both MarTech and AdTech, positioning them between the two realms. A DMP allows advertisers to reach new audiences through lookalike modelling and improve media-buying decisions during real-time bidding (RTB). It also enables marketers to craft and deliver personalized communications to both existing and prospective customers.

MadTech promises the convergence of these technologies and a seamless connection of internal and external (first- and third-party) data for a true SCV. Beyond that, MadTech takes advantage of all available cutting-edge technologies to link users to specific addresses, locations, and devices — including newer internet-enabled categories like wearables and IoT devices.

Below are three practical use cases that illustrate what a MadTech approach looks like in action.

Use Case #1: Personalized Messaging Across All Channels

Enriching third-party data with more accurate first-party data — such as products a user has previously browsed in an online store — and connecting it through the integrated data platforms that MadTech enables, makes granular, personalized targeting and retargeting achievable at scale.

Following MadTech principles, a brand could build audiences from detailed data collected via its own ecommerce properties (first-party data) and upload those audiences to a DSP. From there, it can target online users who precisely match the campaign's criteria. This allows companies to make full use of their own first-party data — provided it was collected lawfully under the GDPR — for online media buying (e.g., retargeting) and broader marketing purposes.

Personalization in MarTech, AdTech, and MadTech: A Comparison

MarTech enables personalized messaging using first-party data from owned media — email, owned websites, CRMs, and similar channels.

AdTech delivers personalized messages to audiences using third-party data and paid media — display ads, paid search, social networks.

MadTech combines both approaches to deliver personalized messages across all available media, including paid. This is increasingly feasible as externally owned channels and internal systems begin to overlap in meaningful ways. Evidence consistently shows that carefully selected, relevant messages add value for customers and improve conversion rates, while also helping companies streamline media spend.

Use Case #2: Improved Attribution, User Journeys, and Reporting

SCV enables marketers to build contextual and personalized customer experiences. With improved attribution reporting, marketers can analyze how users interact with their brand, focus spend on the most efficient touch-points along a user's journey, and automatically reduce spending on touch-points that aren't performing.

A familiar frustration illustrates the gap MadTech addresses: a consumer purchases a pair of running shoes, yet continues to see ads for those same shoes for weeks afterward. Because MadTech operates across all channels and treats them as a unified funnel — similar to how a CRM manages its own pipeline — it enables more accurate conversion reporting and audience management.

For example, a unified MadTech data platform would automatically remove a person from the sales funnel the moment they convert or exit an audience segment, stopping the display of redundant ads across all channels simultaneously. The net result: less money spent on irrelevant messaging.

User Journeys and Attribution in AdTech, MarTech, and MadTech: A Comparison

MarTech systems manage complex, multi-step campaigns and measure their impact on the customer journey using advanced attribution models.

AdTech systems use real-time bidding and behaviour-based recommendations to deliver the highest response at the lowest cost.

MadTech builds on advanced analytics and content personalization. It helps marketers identify the best time and place to deliver messages, and update audiences dynamically based on new data — purchases, conversions, and other signals. Machine-learning algorithms can deliver optimized offers without human decisioning at each step. None of these individual capabilities are new in isolation, but through data synergy, they can be refined, integrated, and made available as a single functional platform.

Use Case #3: AI and Machine Learning

Few terms in AdTech and MarTech generate as much eye-rolling as artificial intelligence (AI) and machine learning (ML). These labels are routinely used to make platforms sound more futuristic than they are, and the full potential of AI and ML hasn't arrived yet.

That said, AI and ML are fundamentally algorithms. When designed for decision-making and process optimization, they improve considerably when supplied with higher-quality, more accurate data. More data synergy means better algorithm performance — and that's where MadTech makes a meaningful difference.

Consider Amazon's use of predictive analytics and machine learning for supply chain optimization. Their predictive-shipping system is designed to move products closer to customers before an order is even placed — based on the vast behavioural data Amazon already holds about its users. The concept has been patented, and its potential implementation rests entirely on the richness of that underlying data.

More broadly, companies with access to centralized SCVs can use AI and ML to optimize complex decision-making processes. DSPs, for instance, can use AI to predict which impressions will drive the best results — and improve those predictions continuously as more user data flows in.

There's also a more modest but immediately practical dimension: using structured rules and lightweight automation to handle labour-intensive tasks like support requests, freeing up marketing teams for higher-order work.

Bringing It Together

From a marketer's perspective, MadTech delivers two complementary benefits. First, it gives companies deeper, more complete insight into existing customers. Second, because those insights are richer, businesses can build sharper targeting models to reach new, prospective customers — reducing the overall cost of advertising and improving its effectiveness.

MadTech doesn't reinvent AdTech or MarTech; it integrates them. As David Raab himself has noted, the shift to MadTech "will result in a renewed focus on core marketing skills such as branding, positioning, value definition and […] creative development."

The technology underpinning MadTech will continue to grow more sophisticated — and more accessible. The practical end goal is a state where marketers can stop thinking about the technology stack entirely and focus on what matters: connecting with the customers who genuinely need and want their products.