Adtech, martech, madtech – what’s the difference? Are they different?
The convergence of adtech and martech dominates current discussions on the topic, but a sound understanding of the difference between the two will help marketers better approach new data projects from start to finish. Graham Plant offers a little clarity on the subject.
This article originally appeared in The Madtech Brief, Marketing‘s second print issue for 2019
Since the terms became part of the marketing world vernacular, there have been many attempts to explain adtech; why it is so awesome? How it will own all our marketing spend? More recently, is it the same as martech?
To answer the question ‘is there really a difference between adtech and martech?’ Yes.
While my answer is definitive, there has been a progressive blurring of the lines in the role of the technologies. This has contributed to some companies making poor tech choices, and generated confusion in the marketplace as vendors jockey for position. There are some clear elements that distinguish each technology from the other, which will probably prevent complete unification or convergence.
In simple terms, adtech’s primary purpose is for reaching and engaging an unknown audience by leveraging paid third-party data and distributed media platforms. This enables the client to communicate on a ‘one-to-many’ basis. The audience may not be known, but that doesn’t mean that they can’t be targeted based on profiles, inferred attributes and observed behaviours. Platforms that you will see in the adtech space include:
- demand-side platforms (DSPs)
- data management platforms (DMPs)
- supply-side platforms (SSPs), and
- ad exchanges.
We can also thank adtech for the emergence of many new acronyms and terms, many of which are simply rebranding of existing terms. When reviewing adtech platforms we focus on impressions, exposure, engagement, viewability, conversion, relevance and when we can attribute a sale or acquisition. From a vendor perspective, adtech is a consumption-based financial model where the client pays based on volume and reach, for example cost per thousand (CPM), cost per click (CPC), click-through rate (CTR) and cost per acquisition (CPA).
In contrast to adtech, martech is primarily used for communicating directly to customers (or prospects) who are known (be that by name, address, login, profile, etc). Martech is more likely to leverage first-party data (and sometimes second-party data) and owned channels (like websites and email addresses) via licensed marketing software to connect directly with known recipients on a one-to-one basis.
There is a huge stack of technology that plays a role in martech, some of the key platforms are:
- marketing automation applications (including email marketing)
- analytics tools
- content management systems (CMS)
- customer relationship management platforms (CRM), and
- social management platforms.
As you can see, there is no one martech application for everything (just check out the martech lumascape), rather it is a suite of best fit tools that will help the marketer achieve their goals. This requires a martech stack that is configured to suit the business and is fully integrated. The goal of martech is to enable businesses to build deep one-on-one customer relationships across all channels they engage. Concurrently, they will be developing rich insights that enable them to anticipate market and customer activities, segment their customers, and build programs that enable them to respond in a targeted, relevant and timely manner. Martech metrics follow a similar line to adtech, but the primary measure is ROI.
From a vendor perspective, martech is most likely licensed applications like software as a service (SaaS), platform as a service (PaaS) or licensed on-premise software that is using client-ingested or provided data. The fees are in the licensing of the technology, not accessing the audience.
While there are many talking up the wow factor of these tech plays, the concepts and methods are not that new. The differences between advertising and direct marketing are not that different from the differences in adtech and martech. Don’t believe me? Pre the internet (yes, there was such a time) advertising was used to create brand awareness and connect with audiences across a variety of traditional channels.
The goal of advertising was to drive customers to engage and act. Companies bought advertising on estimated reach and engagement based on things like readership, listenership, viewership and circulation. Direct marketing was used to connect directly with known customers (or prospects) on a one-to-one basis with a personally crafted proposition, using direct channels such as addressed mail, telemarketing and even door-to-door. Magazines and newspapers would insert advertisement, advertorials and inserts into their publications – now, we have display ads, native advertising, blogs and content marketing. And inserts? Pop-up ads.
Without doubt there is a progressive convergence of adtech and martech and there is already significant overlap in functions and features that lifts the power of both tools. This overlap will increase as the breadth and depth of data that we capture and integrate increases.
Businesses need a holistic approach to customer engagement that integrates digital and offline. Synchronicity of data between the two platforms becomes increasingly important as businesses aim to understand the complete customer journey from interested eyeballs on a website, to random click on a display ad, to an engaged customer who is known to the business. In 2015 David Raab coined the term madtech, which is described as the erosion of the demarcation line between adtech and martech. Data is the key resource for both adtech and martech and as we find ways to bring unstructured online data into frameworks that can connect it with structured offline data (and other disparate data sources), integrating technologies becomes simpler.
In 2016 a report by the Winterberry Group highlighted the rapid growth in customer onboarding estimated at US$250 million in 2016 and expected to grow to $1 billion by 2020 (next year!).
Customer data onboarding is a term for linking offline data with online attributes. You could envision this as matching a first-party dataset with a third-party digital dataset. The goal is to execute a matching program to create a common identifier or primary key to link the records. Sounds simple, but it’s not. But as businesses get better at capturing, analysing and using customer data we can increase its use and matchability.
This is how tech companies will continue to bridge the gap between offline and online data, applying their smarts through increasingly more sophisticated matching tools and expansion in the scope of DMPs. As this occurs, the overlap between adtech and martech increases considerably. The ability to match, integrate data and model data across digital and offline is the Holy Grail for most marketers. We’re getting closer and closer as both adtech and martech platforms develop capability and integrate more easily.
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Image credit:Vincent van Zalinge