The digital advertising industry and the dynamics that shape it are powered by a set of software collectively referred to as “AdTech” (Advertising Technology). In a macro-sense, this technology facilitates the buying and selling of online ad space between advertisers and publishers. In this context, advertisers are brands or agencies (representing brands), and publishers are vendors with digitally accessible ad inventory.
A related but historically distinct set of platforms referred to as “MarTech” (Marketing Technology). If AdTech facilitates the transaction of paid ads, then MarTech is most commonly thought of as the technology that enables brands to plan, activate and manage marketing campaigns across owned channels to engage existing, known customers. Common owned channels include SEO, onsite/app messaging, email marketing, and brand social media accounts. Other terms that are used include “non-paid” or “organic”. For consistency, we’ll continue to refer to these channels as “owned”.
Historically speaking, AdTech systems are most commonly operated by agencies and publishers whereas MarTech platforms are directly used by brands. There’s also differences in billing as AdTech is generally charged as a % media spend (via CPM) plus commission or flat fee. MarTech on the other hand, is often charged on a contracted and/or consumption-based SaaS model.
The Convergence
Over time however, the line of demarcation between AdTech and MarTech has become increasingly blurred as some of the software have evolved to be deployable across both paid and owned marketing activity. Reasons include but are not limited to the following:
- Unified User Experience – Brands seek to provide users with a more holistic, unified experience across both paid and owned channels/touchpoints
- Personalization/Relevance – Increased user expectations for more personalised, relevant messaging/content across paid and non-paid channels.
- Data Centralization – Actioning on the previous two points requires centralizing data that historically sat in siloed systems. This involves new/updated technology that combines the functions of legacy AdTech and MarTech data systems.
- Customer Data – A greater emphasis on 1st party data given the degradation of pseudonymous third party identifiers (ie. 3rd party cookies, mobile device IDs) brought about by data privacy legislation such as GDPR (EU) and CCPA (California).
This growing convergence has seen the term “MadTech” gain prominence within the industry in recent years. Coined in 2015 by David M. Raab (in this prescient blog post), MadTech represents the fusion of AdTech and MarTech into a more holistic framework. Not only does it better represent the current state of the industry, but it’s also how marketers should approach data and tech deployment across both paid and owned channels to drive marketing objectives. It’s important to keep in mind that end users don’t think of a brand as having separate paid and organic messaging identities. Rather, all touchpoints combine to paint a singular brand image. It only makes sense for the technology powering marketers’ digital brand messaging to work in a similar way.
Based on this convergence, I recommend organizing the technology into the following macro-categories:
- Paid Ads – Tech that facilitates the transaction of paid ads
- Owned Messaging – Tech that facilitates owned brand messaging
- Data Systems – Tech that collects/deploys data across the other two buckets
This diagram arranges the various MadTech platforms/solutions into different categories and types within each macro-category. As an example: Paid Ads = macro-category, Advertiser/Demand = category, Ad Server = type)
We’ll dive deeper into the platform types contained within each category and the principles/concepts at play in future series entries. For now, here’s a high-level outline of the categories within each macro-category along with a few caveats.
Caveats
- I’ve included what I consider to be the major categories within each macro-category. There of course exists niche categories that aren’t included.
- Platform types that have features applying across multiple categories are included in each relevant category.
- Certain platform types have overlapping features. For simplicity, I’ve left these types distinct in this representation.
- The dotted arrows represent the general flow of operations when working with this tech. Ex: Publishers “place” available ad space on Ad Inventory Mediums for Advertisers to bid on/purchase (this all occurs in real-time)
Paid Ads
- Advertiser / Demand – Represents technology that facilitates media planning/buying either by advertisers/agencies (we will refer to advertisers/agencies as simply advertisers for the remainder of this post). “Demand” in this context references advertisers’ “demand” to bid on/purchase ad space. In AdTech parlance, this is aptly referred to as the “Buy-Side”
- Ad Inventory Mediums – The metaphorical space where ad inventory is “placed” by publishers to be purchased by advertisers. Inventory here can be any digitally accessible media channel and is generally transacted on via real-time bidding (RTB). Note that advertisers and publishers are also able to buy/sell directly with one another without this middle layer using certain methods (some of which do not involve RTB).
- Publisher / Supply – Represents technology that helps publishers monetize their ads. “Supply” references publishers’ available ad space. This combines with advertiser demand to form the demand/supply dynamics that shape the industry. In AdTech, this category is referred to as the “Sell-Side”.
Owned Messaging
- Content + Messaging – Tech used to plan, activate, manage and analyze non-paid marketing campaigns across owned channels.
- Messaging Channels – The primary owned communications channels that MadTech platforms directly modulate.
Data Systems
- Collection + Management – Tech designed to or having the functionality to collect and manage/organize user data in its many forms. Collection methods often involve the use of javascript tags, server-to-server data feeds, or data uploads.
- Storage + Harmonisation – Tech that aggregates, deduplicates and stores data from a variety of sources (including those within the previous category). Many brands today elect to use managed cloud services (e.g. Amazon Web Services, Google Cloud Platform, Microsoft Azure) along with more traditional MadTech software within this category. This highlights another important convergence: that of traditionally IT-managed cloud software with that of MadTech.
- Activation + Analysis – Tech involved with “using” data for marketing purposes. This can include direct processing and output within the system types included or sending the data back into software types within the “Paid Ads” and “Owned Messaging” categories for a variety of use cases.
Notes
- Given that user/customer data is the fuel that powers MadTech, the software in this macro-category largely spearheads the AdTech/MarTech convergence. Most platforms included here can be deployed for both paid and owned marketing use cases. Think of it as the incursion point where AdTech and MarTech meet. It’s also worth mentioning that the lines between categories and types here aren’t as clean as is represented as many data systems can be deployed across multiple categories. It’s one of those things where most data systems can be used for most use cases, but certain systems are more suited for certain use cases.
- For those who are familiar, I’ve bucketed Data Management Platforms (DMPs) within the CDP bucket given the growing use case overlap and decreasing utility of traditional 3rd party identifier reliant DMPs.
The question that comes to mind for marketers is how much of this tech is actually necessary and useful? As one would expect, the answer is nuanced and needs to be calibrated based on an organization’s digital maturity, marketing objectives and operational context. It’s important to have concrete use cases in mind prior to paying for any piece of software (in my experience, it’s often the opposite unfortunately). The challenge is that a baseline level of understanding is required to make the assessment. Folks don’t know what they don’t know, and it’s hard to think outside the box unless one knows their way around it. In the entries to come, we’ll work to solve this by demystifying and contextualizing the different topics in this space along with the principles and concepts that underpin them.
