Scott Brinker’s “Marketing Technology Landscape Supergraphic” has become a global reference for marketing executives, marketing technologists, digital strategists, IT executives or investors. Having grown in size and complexity, many of us in the “MarTech” space are often asked to provide our own basic guidance with regards to the generic rules under which its many categories coexist with (or replace) each other.
In our case, rearranging the existing sections and categories on the basis of the manner in which data flows between them or is used by them has done the trick. After all, most of the (as of today) 43 categories listed consist of either making sense of data or using it (directly or indirectly) to create marketing experiences (ads, content) for the consumer.
The following chart and supporting ideas are an attempt at making this same guidance available to others. We could call it a “data-aware Marketing Technology Landscape” or, simply, a “Marketing Data Technology” ecosystem.
For the sake of simplicity and ease of consumption I will kick it off by laying out the seven basic assumptions sitting behind the chart above. I will then follow that up with some overall thoughts as well as a vendor-populated version of the same chart.
The Backbone is made of technologies and media that allow us to gather, process and store data. This will not only consist of stand-alone solutions, but also of the architecture behind many of the solutions clustered elsewhere.
(Data) Discovery encompasses a wide range of human-driven functions, from predictive modeling to digital analytics. Of those, certain functions are not exclusive to the marketing space.
(Information) Delivery refers to the ultimate brain-powered task: decision-making. It represents a bridge between data and organizational change; between performance management and marketing outcomes. While in its purest form it is represented by executive scorecards and dashboards, this layer will also encompass data governance, “insight management” and even “built-in marketing know-how” (in the form of cross-channel intelligence).
Activation relates to putting data to work at the most tactical level, establishing a seamless connection with the marketing experiences it helps generate. As one would expect, it is closely intertwined with the fifth and last piece, Automation, as multiple tasks are eventually systematized, then automated. Many of these tasks will already fall within the realm of the so-called “AdTech”.
(These layers have been labeled as “Backbone”, “Discover”, “Decide”, “Activate” and “Automate” respectively in our chart)
There will be a data lake. And there will be a DMP (“data management platform”). And there will be advertiser-side data marts or a data warehouse, but agencies and media will also maintain separate brand-related repositories and systems, directly connected to the marketing campaigns or initiatives they define and manage.
Furthermore, aspiring to a fully integrated scenario will pretty much destroy the value of certain categories of data for medium-specific analysis, delivery, activation or automation purposes.
Drilling further on the Backbone layer, the “house and garden” shape shown in the Map illustrates other important data-architecture constraints:
As it happens, marketing data (mostly digital) represents a serious challenge to traditional Business Intelligence (“BI”). Whereas the former becomes predominantly semi-structured or unstructured, BI was built around structured data (i.e. data models associated with relational databases). While much of the marketing data now available does not leave room for anything beyond correlations, BI aims for good old causality.
Various analysis and delivery environments will thus coexist in an apparent show of inefficiency, but in fact reflecting the natural disparities of the underlying data models. Social Analytics functions will, for instance, be best performed by tools specialized in the retrieval, storage and processing of social feeds. On the other hand, analyzing customer properties in a data warehouse will be best left to predictive, descriptive and visual discovery tools.
[More on this point: How Digital Data disrupts Business Intelligence]
What is the ultimate frontier of marketing actions? The unpredictable mind of the Customer.
What is the ultimate frontier of internal marketing processes? The unpredictable behavior of people and teams.
The former explains that “attribution” models are not the holy grail we once hoped for. Or that a true “customer journey” will never become a reality. Unless, that is, we get to fully understand the human brain – at which point the entire marketing process will be ripe for full automation (and human beings ripe for replacement by robots).
The latter explains the differing speeds of technological progress and organizational change. Or the fact that all the metrics in the world cannot replace a strong piece of storytelling (“the API to the human brain”).
It is a fact that an ever-growing proportion of customers is either disabling cookies (third party or all), or deleting them more often, while mobile access renders many of them useless. People are increasingly becoming fully aware of the data we collect about them… and acting in consequence.
Making it worse, the regulatory framework (not only in Europe) is shifting its focus from PII (Personally Identifiable Information) data to specific methodologies used in the collection of any data (be it PII or not). Hence the impact of:
The primary consequence of all this is our inability to keep exploring a purely deterministic, “user-centric” approach, giving way to a “user-driven” or “intelligent audiences” scenario in which marketing experiences are automatically personalized on the basis of a combined deterministic/probabilistic approach.
[More on this point: Revenge of the silos: how privacy compliance is cutting the customer journey short]
Information Delivery is tied to productivity, marketing performance and even “Insight Management”. Neither are the terms “dashboard” or “reporting” exclusive to this function anymore, nor would they be enough to give coverage to every one if its key components.
Elaborating on the first point, “dashboards” are now everywhere, at every level and every category. Every tactical function has a dashboard for human supervision or direct control. Every Data Discovery tool (medium-specific or generic), provides a means to reach out to data consumers, most likely in the form of a dashboard. However, none of these belong in the performance management (or marketing performance/intelligence) space. As a result, the term has ceased to be a valid label for any given category or task.
Further to the second point, it is the following additional pieces that really complete the Information Delivery picture -even more so when we consider the role of this layer as a bridge to the rest of the organization:
In essence, although it may be true that the marketing technology space is gaining complexity, it would also seem like its key components are simultaneously maturing, with many functions quickly falling into place under an ever-clearer landscape that cannot escape some basic constraints: data (it comes in limited forms); people (individuals and teams); media/services (the canvas of our experiences); maths; and technology itself.
As points 2 (irreconcilability of data types) and 5 (inevitability of the privacy-aware customer mindset) above reflect, obsessively breaking data silos is not the answer. I believe we should instead adapt ourselves to working with multiple silos: connecting them where possible, overlaying them when not.
All that said, let’s now share a vendor-populated version of the same “Marketing Data Technology Map”.
Please notice that this is only provided for illustrative purposes, adding the specific tools we are most familiar with.
I look forward to receiving your comments.
Not Another Dashboard.