Key Takeaways
- Most B2C stacks are overbuilt: teams actively use only 49% of the martech they own, per Gartner.
Most B2C stacks are overbuilt: teams actively use only 49% of the martech they own, per Gartner.
- The martech market grew roughly 100× since 2011 to 15,505 tools in 2026, per chiefmartec, and growth has now plateaued.
The martech market grew roughly 100× since 2011 to , per chiefmartec, and growth has now plateaued.
- In Twilio’s 2025 survey, 75% of businesses report higher customer spend from personalization.
In Twilio’s 2025 survey, 75% of businesses report higher customer spend from personalization.
- A composable CDP keeps customer data in your warehouse and adds an activation layer on top.
A composable CDP keeps customer data in your warehouse and adds an activation layer on top.
- The winning move is consolidation: fewer, connected layers that turn stored data into action.
The winning move is consolidation: fewer, connected layers that turn stored data into action.
- Your data foundation and your engagement platform are peers, not a hierarchy.
Your data foundation and your engagement platform are peers, not a hierarchy.
Most B2C marketing teams don’t have a tools problem. They have a coordination problem. Years of buying point solutions have left stacks that are wide, expensive, and half-used, full of customer data that never reaches the decision it should inform. The fix isn’t another platform. It’s a leaner architecture where your customer data and your engagement layer work as one. That’s the real promise behind the composable customer data platform (CDP): keep data where it lives, and activate it fast.
Why the B2C MarTech Stack Has Outgrown Its Usefulness
For a decade, more martech meant more capability. That equation has broken. Marketers kept buying, but usage kept falling, and now the stack itself is the bottleneck.
The numbers make the problem concrete:
- The martech landscape has grown roughly 100× since 2011 to , and chiefmartec reports growth of just 0.79%, effectively a plateau after 15 years of expansion.
The martech landscape has grown roughly 100× since 2011 to , and chiefmartec reports growth of just 0.79%, effectively a plateau after 15 years of expansion.
- Gartner’s 2025 Marketing Technology Survey found teams use just 49% of their martech stack, with martech absorbing nearly 22% of the marketing budget and only 15% of organizations qualifying as high performers.
Gartner’s 2025 Marketing Technology Survey found teams use just 49% of their martech stack, with martech absorbing nearly 22% of the marketing budget and only 15% of organizations qualifying as high performers.
The lesson for B2C teams is that the answer to a bloated stack is rarely one more tool. It’s fewer, better-connected layers.
What a Modern B2C MarTech Stack Needs
Strip a modern B2C martech stack back to its essentials and four layers remain. Each has a distinct job, and the stack works only when they connect.
Layer
What it does
Example
Data foundation
Stores and unifies customer and event data as the source of truth
Cloud data warehouse or composable CDP
Activation and engagement
Turns unified data into cross-channel experiences as behavior happens
Iterable
Channels
Delivers the message where the customer already is
Email, SMS, push, and in-app
Intelligence
Decides who to reach, what to send, when, and on which channel
AI decisioning
The goal isn’t to own every layer inside one suite. It’s to connect them so data actually moves. That connection is what makes personalization pay off: in Twilio’s 2025 survey, 75% of businesses report higher customer spend from personalization, and that depends on a real 360-degree customer view rather than a name dropped into a template.
Where the Composable CDP Fits
So where does the composable CDP fit? At the data foundation, not as a rival to your engagement platform, but as the layer that feeds it.
Traditional customer data platforms were built to store and activate customer data at scale. Because they copy data into a separate system, they can add cost, duplicate storage, and long implementations. A composable CDP takes a different path: it leaves data in your cloud data warehouse and adds an activation layer on top, syncing audiences to downstream tools through reverse extract, transform, and load (ETL).
Traditional CDP
Composable CDP
Copies data into its own database
Leaves data in your warehouse
Bundled, fixed feature set
Modular, best-of-breed components
Longer setup on a new data model
Faster start on existing data
Pay to store data you already own
Reuse the storage and compute you have
This warehouse-first model is why “composable” has become a common reference point for modern data architecture. For your team, the practical win is simple: you think in use cases, not migrations.
How to Activate Warehouse Data Without Copying It or Changing Its Schema
Zero copy means a tool reads your warehouse data where it sits instead of storing a duplicate. Schema control means the tool accepts your fields and events as you already define them.
Approach
Where the data lives
Who defines the schema
How custom fields and events are handled
How fast changes reach journeys
Switching cost
Traditional CDP
A copy in the vendor’s database
The vendor, through its data model
Mapped into the vendor’s predefined fields
Depends on vendor sync schedules
Typically high: you migrate stored data and rebuild models
Composable CDP with reverse ETL
Your warehouse, with audiences synced to downstream tools
Your team, in the warehouse
Modeled in the warehouse, then mapped to each destination
On each scheduled sync
Typically moderate: models stay put, but you remap each sync
Zero-copy data sharing
The source account, which other tools query directly
The data owner
Shared as they exist in the source tables
As fast as the reading tool queries the shared data
Typically low: you end the share, with no duplicate to migrate
Engagement platforms like ours typically keep a working copy of profile data on hand, so sends and journey decisions don’t wait on the warehouse. We activate the data your warehouse already holds, and it reaches journeys in three ways:
Lands on the profile: Smart Ingest brings warehouse data onto the profile on a schedule, the core of how we work with warehouses.
Fetched for a journey step: Journeys Live Data, part of Journeys, pulls live data into a journey step when needed and writes it to the profile only when you choose.
Read in place:
Our Snowflake Secure Data Sharing gives your Snowflake account read-only access to the engagement data we share. For accounts in the same Snowflake region, you query that data in place without copying it into your account. Shared database objects are read-only, as described in Snowflake’s data sharing documentation.
Schema flexibility gives your teams more control over how they bring data into Iterable. By default, we automatically create new custom user fields, custom events, and event fields as data arrives, as described in our schema docs. We also support JSON objects, so you don’t have to predefine every custom field before sending data. You can use your own field names in personalization and journey logic, subject to Iterable’s naming and data-type requirements.
Three vendor questions test lock-in before you sign:
- Can I keep my schema? A good answer confirms the platform accepts your custom fields, events, and JSON objects as they are.
Can I keep my schema? A good answer confirms the platform accepts your custom fields, events, and JSON objects as they are.
- Where does my data live? A good answer separates data that lands on the profile from data that stays in your warehouse.
Where does my data live? A good answer separates data that lands on the profile from data that stays in your warehouse.
- What happens to my data if I leave? A good answer explains how you export your data and what you would rebuild.
What happens to my data if I leave? A good answer explains how you export your data and what you would rebuild.
Whether you still need a customer engagement platform (CEP) alongside a composable CDP is a separate question, and we cover it in our Composable CDP vs. CEP article.
Turning Stored Data Into Real-Time Engagement
A composable CDP gets trusted data ready to use. Activating it is our job. We’re the AI customer engagement platform that takes the data in your source of truth and turns it into cross-channel experiences the moment a customer acts.
Inside Journeys, our cross-channel orchestration product, we coordinate email, SMS, push, and in-app from one place, so a signal in your warehouse becomes a relevant message without engineering tickets. And with Nova Intelligence, our native AI layer, we bring decisioning into that same flow: Predictive Audiences, part of Nova Intelligence, flags who is most likely to convert, so you engage the right people first.
Activated data unlocks what a static stack can’t:
- Trigger re-engagement the moment churn risk rises, not a week later.
Trigger re-engagement the moment churn risk rises, not a week later.
- Send product recommendations built from your own propensity models.
Send product recommendations built from your own propensity models.
- Adjust timing and channel for each individual as behavior shifts.
Adjust timing and channel for each individual as behavior shifts.
Consider how Glassdoor handles this. It lifted channel conversion for a site with 50M+ monthly visitors by unifying siloed email and push data through its Snowflake integration with us.
That is the thinking behind our approach to smarter data activation.
Frequently Asked Questions
1. What Should a B2C MarTech Stack Include?
At minimum, four connected layers: a data foundation that unifies customer data (a cloud data warehouse or composable CDP), an activation and engagement platform that turns that data into cross-channel messages, the channels themselves (email, SMS, push, and in-app), and an intelligence layer that decides who to reach, when, and where. The priority isn’t more tools; it’s making these few layers work together.
2. What Is a Composable CDP?
A composable CDP is a customer data platform you assemble yourself: you choose each component, and your warehouse stays the trusted home for customer data. That lets you upgrade or replace one piece, such as identity resolution or audience syncing, without rebuilding the rest.
3. How Does a Composable CDP Differ From a Traditional CDP?
Choose a traditional CDP when you want packaged speed and one vendor running storage, identity, and activation. Choose a composable CDP when you already have warehouse and data engineering maturity and want each layer to fit your existing data.
4. Do You Need a Composable CDP if You Already Have a Data Warehouse?
If your team has a mature warehouse and data engineering support, a composable approach usually delivers the most value, because you activate data you already store. What matters more than the label is whether that data reaches an engagement platform that can act on it as behavior happens.
5. What Is Zero-Copy Data Activation?
Zero-copy data activation means a tool acts on data where it already lives instead of moving a duplicate into its own storage. Snowflake’s documentation describes this model for Secure Data Sharing, where one account reads another account’s data without a transfer. For your team, the outcome is fewer duplicate datasets to reconcile and a cleaner exit if you change tools.
6. Which Marketing Platforms Avoid Rigid Data Schemas?
Shortlist platforms that take the fields and events you already track, including JSON objects, without making you define a fixed schema up front. We’re one example: we work with your data in the shape your business already uses. Ask each vendor to demo on a sample of your own data, and watch whether its model adapts or forces a remodel.
7. How Do You Avoid Vendor Data Lock-In?
Run the exit test before you sign: ask each vendor how you would get your data out and what you would rebuild. Keep your warehouse as the home for models and history, so any engagement tool works from a copy you can recreate. Where a tool only needs to read data, favor read-only sharing over another copy.
Build a Leaner, Composable Stack
The brands pulling ahead aren’t adding more tools. These brands connect fewer, better ones. A composable data layer feeding a real engagement platform turns your stack from a cost center into a growth engine, and it’s a shift you can start on the data you already own.
Next step: See the architecture in practice in our composable martech whitepaper.
