How to maximize marketing ROI with a composable stack
The marketing technology (MarTech) landscape grows more complex by the day.
New innovations are constantly emerging, each promising to be the next game-changer for customer data strategy and activation.
But with so many overlapping and often disjointed point solutions, many marketers find it difficult to create cohesive stacks. As a result, critical customer data remains siloed and underutilized.
In this blog post, we’ll explore three key concepts to help you simplify MarTech complexity and maximize your marketing ROI: composability, evaluating your data stack’s interoperability and preparing for generative AI.
Composability focuses on seamlessly integrating best-of-breed marketing technologies to solve specific problems and increase efficiency, flexibility, and control. We’ll look at how modern data platforms are enabling this new approach and discuss whether it’s right for your company.
Next, we’ll dive deeper into architecting your own MarTech data stack. We’ll map common day-to-day tasks to leading vendors, discuss the importance of interoperability between systems and explore a case study on implementing a customer data solution.
Finally, we’ll tackle the hype around generative AI and its potential impact on marketing processes. We’ll look at why data quality and strategy are more important than ever to drive these models forward. And we’ll cover the key considerations around enterprise readiness to adopt AI.
By the end, you’ll take away new perspectives on simplifying MarTech complexity through composability, assembling coherent data stacks, and laying foundations for innovations like AI. Let’s dive in!
The Rise of Composability
Marketing technology is evolving fast. Solutions that feel cutting-edge one day are outdated the next.
So how can teams stay nimble yet stable? Enter composability.
Simply put, composability means the ability to assemble digital solutions from different providers. Think of the providers as Lego bricks and the solution as a castle, a racing car or any other end product you want to build.
Instead of an all-in-one suite, you integrate specialized point tools that are suitable for specific tasks. So instead of buying a pre-built off-the-shelf toy race car, you build a customized car that fits your vision and needs.
A data collection tool feeds an analytics platform that is connected to your CRM and so on. Mix and match to solve complex puzzles.
The benefits of composability are flexibility and control. As requirements change, you can swap out blocks as needed instead of using rigid platforms. And with industry-leading best-of-breed blocks, performance is enhanced across the marketing organization.
Through easy data sharing across different tools, composability eliminates silos and duplication of effort. It also allows marketing teams to focus on what they do best - building personalized brand experiences.
The emergence of cloud data warehouses and lakehouses has accelerated this trend. By centrally storing data, teams eliminate copying and reloading data repeatedly. Purpose-built tools plug directly to these data stores.
Is composability a fit for every company though? Consider your need for interoperability between solutions, and for flexibility for future changes, and weigh this against the degree of tech sophistication within your team.
Every MarTech architecture comes with trade-offs!
Alternatively, the decision for composability may be made for you. If your organization can’t justify replacing all the different components of your MarTech and data stack at the same time, then multiple different solutions from different vendors will have to be integrated together.
Now let’s explore architecting a composable stack by mapping business needs to MarTech capabilities.
Deliberately architecting Your MarTech and Data Stack
When assessing marketing technology, it’s easy to get overwhelmed by vendors pitching every bell and whistle imaginable. Who even knows how to evaluate them all?
Instead, you should start from business needs, or “jobs to be done” — the actual day-to-day tasks driving value. For example: email marketing, lead segmentation, campaign analysis and more.
Next, map these activities to vendor capabilities. Some of these may overlap. The goal is to find specific tools that seamlessly interconnect to fully enable each workflow.
During this process, it's important that you involve your IT and data teams to ensure that the most suitable tools are selected and the necessary integrations are in place.
Strong integrations trump an isolated “one vendor does all” approach. While all-in-one solutions like the Adobe and Salesforce Marketing Clouds may promise simplicity and convenience, they do have several drawbacks:
- A lack of best-of-breed functionality
- Limited customization and flexibility
- Roadmap dependency and vendor lock-in
Take this example from one of Snowplow’s customers in the media industry. By implementing Snowplow for improved data collection, plus ActionIQ for customer data activation, they created an agile MarTech backbone.
Snowplow and other specialized tools feed the Databricks Data Intelligence Platform, which then supplies customer insights to fuel marketing campaigns and personalization.
In the composability model, best-of-breed partners unite through a shared data foundation. Assembling this ecosystem requires forethought on:
- Prioritizing data and marketing workflows
- Selecting tools strategically
- Ensuring end-to-end interoperability
With the right planning, composability simplifies rather than compounds system complexity.
Now let’s see how AI innovation builds on this integrated data foundation.
Preparing for Generative AI’s Impact
Generative AI powered by Large Language Models (LLMs) is capturing attention and sparking endless speculation.
Its potential to transform marketing is real — but so is the learning curve for enterprises to adopt it.
The core of generative AI tools like ChatGPT is not a fancy user interface but the data, rules, and patterns that they are trained on. Their output is only as smart as their input.
This means that quality data infrastructure and strategy suddenly become a competitive advantage. The brands best poised for AI are those that have:
- Flexible, best-of-breed MarTech stacks integrated via composability
- Adequate data hygiene and modeling in place
- Consolidated, rich customer data warehouses and lakehouses
- Clean, structured data and metadata
- In-house data science experts
With this foundation, these organizations can train models on proprietary data while respecting rules reflecting their corporate values and compliance needs. Data scientists and MarTech specialists then work to integrate the outputs of these models into the stack to fuel marketing campaigns and personalization.
So, while the accessible chatbot interface of AI is turning heads now, savvy marketers know the real work lies beyond the surface: using AI responsibly and ensuring you have the right data and MarTech plumbing within your walls.
Cut through the chaos
Between dizzying innovation and fragmented solutions, marketing technology will only grow more complex with time.
But by focusing on the core principles - composability, data quality, the accumulation of proprietary data, and responsible AI adoption - teams can cut through the chaos.
Go ahead: assemble integrated stacks that map to business priorities. Leverage flexible data architectures suited for future AI. And make sure to invest in the infrastructure and governance to fuel these new tools properly.
The bleeding edge of MarTech will continue to move rapidly. But armed with strategic frameworks for adaptability, customer-centricity and data centricity, marketing teams can continue to adapt and win.
Want to take a deeper dive into how MarTech composability can generate greater marketing ROI? Check out our recent on demand webinar with ActionIQ.