The 2025–2026 Marketing Technology Stack Landscape

1. Reports

By Saroj Jha · 18 min read

Five layers, three stages, and a shrinking share of the budget

Martech's share of the marketing budget is at a five-year low while AI spending rises, so the stack has to earn its place. The teams that get value from it design the layers first and choose tools last.

I wrote this report to answer one question founders keep asking: what should a marketing stack look like at my stage, and what is it worth paying for? It combines four figures from Gartner's CMO spend surveys with my own map of the stack, by layer, stage, industry and channel. The map is my synthesis, and it is marked as such.

How this report was made

This is a landscape review, not a survey. The budget figures come from Gartner's CMO Spend Surveys of 2021 (400 respondents) and 2026 (401 respondents), each read from Gartner's own release or a named report of it. The layer, stage, industry and channel maps are my synthesis of how the tool categories fit together; they carry no measured figures, and every tool named is an example of its category, not a recommendation.

Full method and limits

In Gartner's 2026 CMO Spend Survey, martech took 19.4% of the average marketing budget, the lowest in five years, against 26.6% in Gartner's 2021 survey, when it was the largest single category.^[1],[2] Paid media reached 31.4% in 2026, funded in part by cuts to agencies.^[3]

The grey column is martech in 2021, the blue column martech in 2026 and the gold column paid media in 2026. Martech lost 7.2 points of the budget over five years, and paid media is now more than one and a half times its size. A shrinking share does not mean a smaller stack is better. In my view, it means each tool has to show what it changes in CAC, conversion or retention, because the CFO now compares it with the ad budget it competes with.

About this data

The two surveys asked different samples: the 2021 survey covered North America, the UK, France and Germany; the 2026 survey North America, the UK and Europe, mostly companies above $1 billion in revenue. Read the direction, not the exact gap, and note that Gartner's own 2026 pages do not publish the martech split; the 19.4% comes from Chief Marketer's coverage of the same survey.

Whatever the industry, the same five layers recur. They are easiest to read from the bottom up, because each layer depends on the one below it.

Read the stack from layer 1 at the bottom to layer 5 at the top. Each row names the layer, the job it does and example tools, and data flows up while results flow back down. The tools are examples, not recommendations; what matters is that every layer above 2 runs on the data the bottom two collect.

Data and identity

Event collection, identity resolution and long-term storage. When it works, segments reflect real behavior and revenue can be traced back to campaigns; when it does not, every report above it is argued over.

Systems of record

CRM, commerce and billing hold the facts about customers, orders and subscriptions. They are often bought as sales or finance tools, but they are what lets marketing tie a campaign to pipeline, bookings and retention.

Engagement, acquisition and optimization

The top three layers act on the data: lifecycle tools message existing customers, the acquisition layer reaches new ones, and the optimization layer tests, governs and reports on both. Salesforce's Pardot, a common engagement tool, has been called Marketing Cloud Account Engagement since 2022.

In my view, the cheapest stack improvement at almost any stage is in layer 1 or 2: one customer ID, one event schema, one CRM everyone trusts. Tools in the top three layers cannot fix data they never receive.

Early-stage teams need speed and learning more than data depth. Growth-stage teams add the data and lifecycle layers. Late-stage companies standardize, and their main work becomes governance.

Read each row across to see how one layer grows from the first team to a late-stage company; read each column down to see a whole stack at one stage. The growth-stage step, adding an event pipeline, product analytics and a real lifecycle tool, is, in my view, where the most money is either well spent or wasted. It pays back only with someone who owns the data and the journeys; without that owner the tools go unused.

Two things shape an industry's stack more than its size: how much product data feeds marketing, and how regulated the data is.

The horizontal axis runs from light to heavy regulation, the vertical from little to a lot of product data in the marketing loop, and each quadrant names the layer that comes first there. B2B SaaS and marketplaces sit high on product data; ecommerce sits low on both; regulated sectors sit far right. The positions are my model, not a measurement. In my view, a founder should find their quadrant and invest in its first layer before buying anything in the other three.

What the stack is built around

• Industry What the stack is built around
• B2B SaaS CRM tied to marketing automation and product analytics; product usage flags product-qualified leads and expansion; campaigns report in pipeline and ARR
• Ecommerce and retail Commerce and payment data feed lifecycle messages on browsing, cart and purchase behavior; attribution and testing decide profitability
• Marketplaces Product analytics and experimentation; lifecycle messages split by buyer and seller; fraud checks tied to identity and payments
• Regulated sectors Enterprise CRM with audit and security features, consent management and clear data lineage; a slower pace of change

Each channel uses different tools, but all of them need the data layer to show what they caused. Paid channels now rely on first-party conversion signals sent server-side; owned channels on behavior-triggered journeys; organic channels on patience and content analytics.

Each row is a channel group. A filled dot marks a layer of the stack it depends on, and the last column names the measure it should be judged by. Read down the first column: data and identity is filled in three of the four rows, and measurement and governance lean on all five layers. In my view, a channel that cannot be measured from first-party data should get a small, time-boxed budget until it can.

Gartner's 2026 survey found CMOs allocating 15.3% of the marketing budget to AI, and 21.3% at AI-mature organizations, while only 30% said they were ready to scale AI capabilities.^[4] Most of that AI arrives as features inside platforms companies already pay for: generated ad copy, predictive scoring, anomaly alerts.

The first two cards are shares of the budget; the third, in navy, is a share of CMOs. Put together, they say money is moving into AI faster than teams can use it: 15.3% of the budget on average, 21.3% at the most mature, yet only 30% ready to scale. AI makes the fundamentals more important, not less. A scoring model trained on a messy CRM scores the mess. Start with one narrow use case on data you trust, keep a person approving the output, and judge it by the business metric it was meant to move.

The same discipline applies to every purchase. Decide what each layer must answer before you look at vendors.

Read it top to bottom. Steps 1 to 4 are decisions you make on paper, each ending in something you can write down; only step 5 involves vendors, and the four answers become your shortlist criteria. In my view, most stack regret comes from starting at step 5. The five practices below are how I apply the sequence.

1. Design the architecture first. Name the layers you need and the question each one answers, then evaluate tools.
2. Fix data quality and integration early. A shared event schema, one ID strategy and monitored pipelines into the CRM or warehouse.
3. Right-size to stage and industry. A few integrated platforms early; data and lifecycle tools at growth; standardization at scale.
4. Give every platform an owner. Someone responsible for its data, its training and its yearly audit.
5. Judge tools by business metrics. CAC, conversion, churn and expansion revenue, with a set time to show results.

• Pre-seed — Keep to a website, analytics, one CRM with email, and the ad platforms. Write down your event names and customer ID now; it costs nothing and saves a migration later.
• Seed — Before adding tools, connect the CRM to billing and product data. Add a dedicated lifecycle tool only when someone owns the journeys.
• Series A–B — Audit the stack once a year against business metrics, retire what nobody uses, and pilot AI on one narrow use case with a named owner.

To turn this into a plan, the Analytics and Attribution Playbook builds the measurement layer in 90 days, and the Agentic AI Playbook covers a first AI pilot.

@techreport{jha2026martech,
  author = {Jha, Saroj},
  title = {The 2025--2026 Marketing Technology Stack Landscape},
  institution = {AAJ},
  year = {2026},
  url = {https://aajconsult.com/reports/marketing-technology-stack-landscape-2025-2026}
}