Martech and Adtech – Who’s the Daddy?
I consider genetic sequencing one of the great scientific achievements during my lifetime. Genetic sequencing has helped us better understand ourselves as biological creatures. It has explained many aspects of who we are, why we look like we do, why we behave like we do and what makes each of us so unique. It has helped cure diseases and holds the promise of one day helping us cure cancer.
Not only that, it paved the way for paternity testing to become a pop culture sensation. Beginning in the late 90s and continuing to this day, paternity testing provided the irresistible daytime TV cliffhanger that ensured the audience would remain tuned in through the commercial break. Jerry Springer and Maury Povich honed this model and proved it out over years and years of endless who’s-the-daddy episodes.
A few years from now, when the pandemic is hopefully in our distant rearview mirror, we may look back to the years 2020 and 2021 as a defining moment when tectonic changes finally transformed marketing as we used to know it. Clearly, we can already see some of the megatrends accelerated by the pandemic as companies quickly embraced new digital tools and technologies out of pure survival instinct.
On a separate, but no less disruptive, track consumer digital privacy took some surprising turns.
- GDPR and CCPA created new regulations about how consumer data can be collected, stored, and used. They also ushered in new requirements mandating that companies gain explicit consent from customers for various uses of their data. In addition, companies must now provide a means for consumers to access the data collected about them and delete it if the consumer requests. The effects of these changes became visible as companies updated their data collection, consent, and privacy practices.
- A crop of privacy-centric browsers gained traction over the past several years on the promise of blocking third-party cookies and online consumer tracking.
- Apple’s embrace of consumer digital privacy came into full focus. In 2020, Apple blocked all third-party cookies. This directly reduced visibility by advertisers and publishers into who is visiting their pages and effectively ended digital ad targeting in Apple’s ecosystem. In 2021, Apple added App Tracking Transparency to iOS which asked iOS users directly if they want to opt-in for in-app ad tracking; Apple also introduced Mail Privacy Protection that disabled marketers’ ability to track when an email is opened and blocked their ability to track the consumer’s IP address when they clicked through.
- Google announced that Chrome will end support for third-party cookies by 2023. It also said it would no longer support behavioral targeting or allow alternative identifiers in its ad products. These changes are notable because Google is, well, Google – the company who helped pioneer and grow the digital ad ecosystem that enabled ad tracking and targeting in the first place. Building pressure from regulators around data privacy and antitrust forced Google to take such dramatic action.
The combined effect of these changes accelerated talk of a looming martech and adtech convergence. In February 2020, the Association of National Advertisers published a blog post headlined, “Advertising Should Be More Like Marketing, and Privacy Laws Will Accelerate This Disruption.” The author argued the distinction between marketing and advertising was obsolete and the new paradigm is one-to-one conversation with customers and prospective customers.
As email marketers, we have chased the holy grail of 1:1 marketing for three decades. I wondered how exactly this convergence would take shape and what factors would begin to unify the two ecosystems. The prospect of a convergence also inspired a curiosity to discover more about the factors that link martech and adtech and whether they share a common genetic heritage.
Which leads us to the final defining moment of 2021, revealed to the public for the first time in 2022 as you read this. This may cement the legacy of 2021 as the year that finally transformed marketing as we knew it. I will apply the methods and techniques of genetic sequencing and paternity testing in the model proven out by Jerry Spring and Maury Povich to solve an age-old riddle:
Were martech and adtech actually siblings who were separated at birth? And if so, who’s the daddy?
When the dust settles from the startling revelations below, just remember where you read it first – Only Influencers.
In this post, I will summarize the origin stories of marketing technology and advertising technology, explore how new developments in consumer digital privacy have impacted each ecosystem, and explain how these impacts point toward a convergence that is already underway. But that’s not all. By the end of this post, you will know the answer to the greatest riddle of all time – Who’s the Real Daddy?
The Birth Mothers
Before digital technology began to reshape marketing and advertising, they grew up as wholly separate disciplines. In a sense, they were born to different mothers.
Marketing was research-based and slow-moving, with few quick means of driving response. Marketing focused on understanding customer behaviors and adjusting strategy based on customer feedback. Eventually, direct mail marketing emerged to provide a measurable direct response mechanism so campaign effectiveness could be measured. Still, measurement and analysis took a long time.
Advertising, by contrast, involved blasting out a one-way communication to a mass audience of largely unknown prospective customers. Advertising had a measurement problem – because its audience was unknown and not individually addressable, it lacked any direct response mechanism. As a result, relatively meaningless metrics like ad impressions took hold (We all know where that led -- the more ad impressions, the better, right?)
As martech and adtech ecosystems emerged with the internet, the old model remained intact.
Marketing technology and email marketing developed around known addressable customer data -- the house file. Email marketing was a logical internet-age successor to direct mail and, combined with e-commerce, created an efficient and measurable direct response mechanism. Email marketers inherited the disciplines and practices of their direct mail maternal ancestors and focused on core tenets required to build, maintain, and optimize the value of their house file – registering subscribers, collecting email addresses, building out progressive profiles, collecting survey data, aggregating data from disparate sources, creating a unified view of the customer, learning how to use past customer transactions to predict future behavior, and calculating customer value through analytical models.
Advertising technology emerged to contend with new challenges of targeting vast anonymous online audiences, along with how to attribute response, optimize and measure campaign effectiveness. Much like the pre-Internet form of advertising, digital advertisers were still unable to directly address known, individual consumers due to regulations around the use of third-party data, which was the only way to build targetable audiences at scale.
While adtech matured from the days where ad impressions were the primary metric, it continued to strive for massive scale enabled by third-party cookies and, later, addressable mobile device IDs. The adtech industry exploded in size and became the belle of the ball, outshining its demure and humble sibling.
Advertising and marketing are essential components of the marketing conversion funnel. However, adtech and martech evolved to solve different technical challenges within different legal frameworks that governed how first- and third-party data can be collected, used, and shared.
Organizations mirrored this dichotomy in the way they structured teams and internal processes.
Familiar to readers of Only Influencers, teams that use martech platforms to target and measure known customer audiences with marketing messages are commonly referred to as “the email team” to this day in companies across the USA. Some companies have updated their name to be more reflective of new multichannel responsibilities and heightened organizational stature, as primary custodians of customer data. Retention Marketing, CRM, Marketing Operations, and Lifecycle Marketing are also frequently used to describe this organizational function.
Teams that use adtech platforms to target and measure online ads to fill the top of the marketing funnel with prospective customers are commonly referred to as Growth or Performance Marketing. Over the past decade, growth marketing took center stage. Adtech was flashy. It was loud. It was brash. It was a cash-cow.
The old saying, “you have to spend money to make money,” could have accurately described the influx of money that adtech systems attracted into the ecosystem via expensive media buys designed to drive traffic into the marketing funnel via digital ads, which, in turn, were intended to drive business growth.
Adtech was complicated. It has its own set of acronyms that, unless you took the time to learn what each one meant, automatically excluded you from the guest list. I had friends and colleagues who made the career move from working in email over to the adtech side. I would nod in agreement, eyes glazed over, as they described the nature of their new job, gushed about “programmatic” this and “ad network” that, and name-dropped a slew of new acronyms. I never really dug into how adtech worked from a systems perspective.
Since we are discussing a potential convergence, it’s important to take the time to summarize the various components used to deliver, optimize and measure targeted digital ads and understand the role each component plays in the process.
- DSP – Demand Side Platforms are used by marketers and advertisers to purchase ad placements from publishers. Those ads are served to consumers who visit the publisher’s websites or mobile apps. These platforms simplify the ad buying process for advertisers by aggregating available ad placements across many different sources in one single interface. They enable targeted ad buys by combining audience data from publishers, advertisers and external sources. Finally, they can track and optimize campaign performance.
- SSP – Supply Side Platforms are used by publishers looking to sell ad inventory on their websites or mobile apps. The SSP sells ad inventory through DSPs, ad exchanges and ad networks. The platforms enable the publishers to share data with advertisers about the type of content on the site as well as data about the demographics, location, and behavior of visitors. This data is used to segment audiences and target specific cohorts of consumers.
- Ad Exchanges provide a marketplace for buyers and sellers where data can be exchanged to facilitate ad targeting and audience segmentation. They are highly automated and use sophisticated programmatic processes to sell ads to specific targeted audiences across many advertisers and publishers. Unsold inventory can be auctioned off to ad networks.
- DMP - Data Management Platforms enable companies to store, manage and analyze their ad campaign and audience data. They can provide data to the SSP and DSP platforms that is used to facilitate the ad targeting purchased through those platforms but DMPs are not directly involved in the ad buying or selling. For marketers and advertisers, DMP and DSP platforms are used together to optimize ad buys and track campaign performance data.
The Wonder Years - Consumer Digital Privacy Shakes Things Up
As martech and adtech grew up, they each faced issues with consumer digital privacy. While the adtech industry was exploding in size and its cash registers were ringing up massive sales, consumers rebelled. They felt bombarded by digital ads and became increasingly horrified about the ways they were targeted and literally stalked across the internet by ad retargeting campaigns.
Consumers vocalized their distrust of how their data was being aggregated, questioned how they were being targeted, and sparked a public backlash that resembled, in many ways, the uproar over email spam two decades earlier that contributed to government regulations like the CAN-SPAM Act, which required that marketing emails contain a working unsubscribe link, among other things.
New consumer digital privacy regulations, combined with new privacy settings put in place by device and mobile OS makers, created a rapidly changing marketing and advertising landscape.
On the martech side, we have all been affected in ways big and small by cookie-geddon and changes made by Apple and Google that limit marketers’ ability to use third-party pixels, track IP addresses and email opens. We have figured out workarounds for tracking pixels, adjusted analytical models that relied too heavily on open rate signals, and redefined the metrics used to identify “inactive subscribers.” We have created new technical systems and processes to allow consumers to access and delete their personal data and control how it is collected and used. But, by and large, the foundational elements of our systems and platforms remained intact.
Adtech has been shaken to its core by these changes. Third-party cookies and mobile device identifiers (device_ids), have long been primary tools used to collect and store data about consumers and target relevant ads to them online. They are structural elements not easily replaced. Limitations or outright blocking of third-party cookies and mobile device addressable identifiers threaten foundational capabilities upon which adtech was built.
Tracing the Genetic Sequencing
I wanted to know more about the similarities and differences between adtech and martech systems, so I began to analyze their traits in more detail. The first step was understanding how each is used to drive the marketing funnel.
The website or landing page is the obvious starting point where the two systems meet for the first time.
Digital advertising delivers an anonymous lead to the website, where a conversion event occurs, at which time the identity of the customer becomes known. At this critical juncture, collaboration and a seamless handoff often seem to be disjointed.
Data generated from website activity was siloed and difficult to link back to a known customer. This is why marketers have leaned on pixels to gain insight and metrics around conversion and attribution. Over time, websites became a valuable marketing tool when web analytics and optimization platforms emerged that could incorporate digital identification and track website conversions in real-time. They could track anonymous visitors coming to the website and visually report how many of them converted into customers. They enabled sophisticated real-time website and landing page optimization testing and deployment tools. Still, the data existed in a silo.
At the moment an anonymous prospect converted into a known customer, the new customer’s email address and name was transmitted to an email database. That email database also existed in a silo and generated its own data about email engagement. The problem persisted for many years that web data was stuck in the web platform and email data was siloed in the email platform. So, when consumers began to visit websites and emails on multiple devices and interact with mobile apps on those devices, cookies and tracking pixels were no longer enough. Oh…and the App data lived in its own new silo.
Next, I took a closer look at the types of data each system uses.
A sign that a convergence of some form is underway is the recent “buzzworthy” treatment of the first-party data phenomenon. Unless you have been living in a cave, you must have heard recent buzz about first-party data. First-party data is, in many ways, both the past and future of marketing.
When I started in marketing, there were two data types – the house file and everything else. Everything else turned out to be third-party data. And, far from novel, first-party data is none other than the trusty house file.
As marketers, we all know the days of third-party data are numbered. This is how first-party data represents the past and the future of marketing. It has become an asset that can replace and improve upon the ad targeting model proven out using third-party data. It is an asset that helps businesses connect with existing customers and target lookalike audiences in a cost-effective manner, creating more personalized end-to-end customer experiences throughout the marketing funnel.
First-party data has driven the success of loyalty programs and email marketing in general. With adtech scrambling to develop alternative solutions to enable digital ad targeting, first-party data now provides a privacy-compliant option that is becoming increasingly relevant to marketing’s future-state.
A first-party data strategy ensures that a company recognizes the value of this data and creates a permission framework for consumers to opt-in to programs that will allow it to be used to make the ads they see more relevant and personalized.
In digital advertising, the definition of first-party data varies depending on which side of the advertising transaction a company falls.
- To advertisers, first-party data is the house file – all the information from the CRM system, website analytics, purchase history, and more.
- To publishers, first-party data refers to their visitors’ website activity, subscription data, social media activity, and relevant offline data like print subscriptions or event attendance, and more. Publishers can sell this data to advertisers to help them segment audiences and optimize campaign success.
Zero-party data is essentially first-party data by a different name. It is data owned by the organization that creates it. The key difference may be in its explicit nature. Communication preferences, configurable profile settings, and interests explicitly communicated by the consumer to an organization all comprise what has become known as Zero-party data. For legislative and consumer privacy purposes, it is treated and used mostly in the exact same ways as first-party data.
Third-party data is collected from many sources, purchased and aggregated by third-party data aggregators. In this process, the sources and methods of aggregation often cannot be verified, diminishing its reliability and accuracy. The primary advantage of third-party data, historically, has been scale. Many data points can be aggregated across a wide swath of addressable consumers and attached to a cookie, making it addressable to digital advertising platforms for segmentation and targeting. The usefulness of third-party data has been dramatically reduced by recent and impending crackdowns on third-party cookies and device ID targeting.
Second-party data is collected by its owner organization (to whom it is first-party data) and sold to partners who can use it to target and personalize their digital ads. It may contain data from website, app or social media activity, purchases, surveys – essentially the same things that would comprise first-party data. The data is sold via a direct interaction or private marketplace between buyer and seller, without the involvement of a data aggregator, so it retains the trust and reliability factor of first-party data.
Second-party data has been around for a while in various forms but did not gain significant traction until GDRP brought it to the forefront of the discussion. With the new restrictions imposed on third-party data by GDRP and, later, CCPA, second-party data was discussed as a regulation-compliant way to target ads to people without requiring explicit consent. Growing dissatisfaction with third-party data and new challenges presented by the decline of third-party cookies has accelerated its adoption in the digital advertising ecosystem.
GDPR and CCPA created ripple effects to adtech and martech systems that are still being felt. Privacy regulations for data collection, consent, and usage — combined with the decline of third-party cookies and loss of access to addressable mobile device_ids — spurred interest in and demand for alternative ways to target digital ads.
If you want to track the next big battleground in digital privacy regulations, it is likely to be fought here. Some of the biggest issues being sorted out include fears of future regulations around second-party data and how publishers can sell the data without turning it over to advertisers outright.
Second-party data is acquired by advertisers in two main ways:
- Publishers sell the data and combine it with data owned by the advertiser’s data, which can be used as a combined dataset to reach known audiences and lookalike audiences across the publisher’s sites.
- Publishers isolate a specific dataset from its inventory and sell it to advertisers directly who can then use it to target consumers on sites other than those owned by the publisher.
The biggest challenge facing advertisers is that second-party data still can’t achieve the scale previously possible by using third-party data to target digital ads. Adtech vendors, publishers, and advertisers are forming alliances, partnerships, and marketplaces to help build scale.
With data, data sources and data types multiplying and legal requirements to obtain and store consent and enable consumers to directly control consent, a patchwork of data management challenges arose and continues to grow in complexity with each new consumer digital privacy development.
A Paternal Clue
Adtech and martech have always been more similar than they were different. While they were born of different mothers and demonstrated distinct traits common with their maternal lineage, they also shared clear genetic expressions in the ways they use data for targeting campaigns, optimizing, and measuring their performance.
In my genetic sequencing of adtech and martech, I encountered a fascinating twist, a clue to their common lineage that might finally help answer the paternal riddle and illustrate what convergence might look like.
An obvious common thread of the adtech and martech ecosystems is data. Both ecosystems depend on data to keep the lights on. This common reliance on data makes adtech more similar to marketing than to its maternal advertising parent. That has always been the case and, as such, was too easily dismissed. Even though both systems used data, they used different kinds of data.
I was surprised to find recently published advertising content that resonated with me in familiar ways:
- Best practices for building (or restoring) consumer trust
- Fostering consumer relationships and dialog
- Progressive profiling to target relevant advertising
- Creating a unified consumer profile centered around first-party data
- Incentivizing consumers to register and provide an email address (wait…what?)
I was astonished to read content about digital advertising that sounded like it could have been written about email marketing. The reason for this is that the foundational elements of the industry are undergoing unprecedented change.
Threatened by an extinction-level event, digital advertising was scrambling to find suitable and scalable replacements for the third-party data, cookies and device ID targeting that could sustain the scale and addressable consumer reach they have enjoyed in the past. In a sense, adtech was going back to its roots and learning how to apply data in new ways.
The Tie That Binds
On the martech side, CDPs (Customer Data Platforms) emerged to unite data that existed in organizational silos and stitch together a single view of the customer. CDPs enabled various types of data — structured and unstructured — originating from many distinct platforms to be ingested and added to a customer’s unified profile. I wrote a blog post in 2020 titled, My First CDP: Why I Chose a CDP as My ESP Replacement. In that post, I recounted a story from 2016 during which I ended up selecting a CDP to replace my company’s ESP. From that experience, I was familiar with the data management challenges for email marketers that contributed to the CDP emergence.
What I did not know is that a similar story had unfolded in adtech. In my search for evidence of an adtech/martech convergence, I discovered an example that demonstrates how a common genetic linkage created a scenario that resulted in an evidentiary point of convergence.
The explosion in growth marketing created enormous demand for actual performance. Growing dissatisfaction with third-party data and mounting regulatory pressure created the need for digital advertisers to seek better methods for targeting their ads. First-party data and second-party data provided promising potential.
Historically, first-party data was used in limited fashions for ad targeting. Retargeting your existing customers on the open web required a process of matching your first-party data to third-party cookies through user syncing, or cookie matching. As we head toward a cookie-less future, new ways of applying first-party data and activating it for use in digital ad targeting began developing.
The problem was that DMPs, the adtech data management platforms, only worked with non-PII and anonymized data, the kind of data traditionally used for digital ad targeting, optimization and measurement. To use first-party data in targeting digital ads, the data must undergo a special activation process that masks its personally identifiable nature for use in ad targeting and delivery systems. That process requires a new kind of DMP called a first-party DMP which specializes in this process.
As advertisers turn to first and second-party data to offset the third-party data void, DMPs have been forced to retool or pivot on their strategy. Consolidation or convergence of feature sets began to close the gap between CDPs and DMPs. Some DMPs pivoted to include tools for leveraging first-party data, some have shifted strategy to become identity solution providers. Likewise, some CDPs evolved in ways that may make them more suitable alternatives to a DMP by adding on core first-party DMP functions.
CDPs also provided a convenient means to store and update consumer consent preferences for digital advertising, creating a way for organizations to become compliant with new digital privacy regulations.
Thus, the emergence of CDPs represents a unique point of convergence for adtech and martech ecosystems and provides an illustrative example of the unifying power of data between what had been two disparate industries. For retention marketers, CDPs provided a way to unify data from the web, email, apps, and many other sources into a single unified view of the customer and make the data accessible for marketers to use in targeting and personalizing their campaigns. For growth marketers, they provided first-party data management capabilities and the ability to meet legal requirements for storing and updating consumer consent permissions.
Here’s my conclusion about the paternal heritage of adtech and martech. After analyzing the distinct and common genetic traits of each and exploring how each contributes to drive the marketing funnel, it turns out that Data is the common paternal parent. Too-long overlooked and disguised by the fact that each system uses different kinds of data, recent industry-wide moves by adtech providers to appreciate the value of first-party data, accommodate ways for it to be more easily used to target digital ads and efforts to foster a new era of consumer trust prove beyond a doubt that data is, in fact, the real Daddy. And remember — you read it here first.