Marketers get a lot of insight by learning about the unique ways other marketers think about their marketing technology stack. It’s the point of the Stackies award, organized annually by the MarTech Conference.
While it’s very helpful to focus on individual stacks, we believe it’s also helpful to consider the common types of marketing technology stacks. The themes and patterns around how marketers build their stacks can give us deeper insights on how marketers approach go-to-market strategies and revenue generation.
Also, a typology of martech stacks is something we haven’t seen yet. In this post, we consider the types of martech stacks that exist out in the world by examining the 41 martech stacks submitted to this year’s Stackies awards.
So let’s strip away the fluff and boil it down to its simplest form.
What Your Martech Stack Says About You
How individual companies structure their marketing technology stacks is reflective of the competitive forces they need to address, whether they are B2C or B2B, and the stage of their company.
But there’s more to it.
How companies structure their stacks is reflective of marketers’ goals, priorities, analytic processes, and relationships to customers. And there’s a lot we can learn from this.
So, let’s look at some of the patterns among various stacks.
The One Direction Martech Stack
This kind of stack is characterized by the a strict adherence to the sales and marketing funnel. Each technology is assigned or placed based on its funnel purpose, e.g. awareness/discovery, lead generation, opportunities and success/advocacy.
They are drawn differently, either left to right, top to bottom, or any combination, but they follow the idea that there is a beginning middle and end. And martech has a single purpose.
What does this say about the marketers who have this view of martech?
It shows that marketers are focused on the funnel and making sure each technology makes each funnel stage functional and optimized.
It teaches us to focus on the entire funnel, what we call pipeline marketing, and it can help marketing operations managers understand where technology gaps exist, based on what stage of the funnel is having low conversion rates.
MongoDB, DemandBase and Ion Interactive are great examples of the one direction theme.
As an aside, this type of martech stack is indeed named after the English-Irish pop group, One Direction, whose sound is described as, “necessary for the revitalization of the boy band movement.”
The Core Thinker Martech Stack
This type of marketing technology stack revolves around a single entity. For example, the customer, content, or revenue.
These marketers think about how each piece of martech impacts their north star. For example, content is the core of Uberflip’s martech stack. Uberflip’s go-to-market strategy revolves around being the best in class example of a content marketing organization. Their marketing stack revolves around this idea as well.
Different northstars exist. For example, Solvis Consulting has communities as the core of their martech stack. UVM Marketing has their buyer persona at the core, while Uberflip’s core is content.
What we can learn from this view is the importance of alignment.
Every piece of technology should relate to the core mission. Alignment with sales and the business goals is the the surest path to marketing success.
The Data Flow Stack
The marketers who focus on the flow of data specialize in creating good analytic processes. They know how to generate metrics that are important for decision making, and understand how to manage data collection and analysis.
These marketing operations leaders understand how each software solution, i.e. point systems, connects to the central data platform (whether it’s the CRM, MAP, or DMP).
We can learn a lot from marketers who carefully illustrate how each piece of martech is interconnected to create an analytics process that helps the organization understand the customer and align with the sales team under a common metric like revenue.
Without careful attention to the harmonious connection of data and information, marketers can’t manage the funnel, and deliver reliable and precise metrics to decisionmakers.
These data connections are required for reliable and robust attribution modeling, which helps marketers take control of the marketing and sales funnel.
Datapipe, PTC Marketing submitted martech stacks that illustrate how data gets shared between their point systems and platforms.
These marketers architect a martech stack with the goal of getting the best analytics process.
They think about their stacks as data connections. And these connections equate to analytics as a competitive advantage.
We also interviewed Datapipe on their strong focus on analytics and data flows, culminating in powerful attribution models.
The Tetris Style Stack
The tetris style stack is simply the one direction stack except each piece of martech spans multiple parts of the funnel. In other words, these marketers recognize that a single marketing software has a function in multiple areas of the funnel.
The visualizations of the technologies look like stacks of blocks.
These marketers think of martech stack architecture as a game of tetris. Each tetris piece is a martech tool that fills multiple needs, functioning in different areas of the funnel.
Allocadia, Clear Code, and InsightSquared are good examples of martech stacks that follow the tetris style. Note that Clear Code also includes information on data flow.
What we can learn from this type of thinking is a focus on completeness. Where are the gaps? What tools fit my exact needs? How does everything fit together as a whole?
This kind of thinking leads to a martech stack that utilizes each tool to its greatest potential, without any wasted budget paying for a tool that doesn’t serve a purpose.
We’ve shown a few ways that groups of marketers approach their martech stacks. We’ve been able to observe how they think about the purpose of marketing and how we can apply their thinking to our own marketing operations.
Further research should be done to understand how marketers perceive their martech stacks, which translates into differences in execution and performance.