Tatev Malkhasyan
October 9, 2025
17
minutes read
Effective marketing now hinges on the strategic implementation of technology. This guide provides the definitive framework for business leaders and marketers to build a powerful MarTech stack that drives efficiency and delivers measurable returns in 2026.
Martech (marketing technology) is the set of platforms that help teams plan campaigns, manage customer data, automate journeys, and prove what worked. Adtech sits alongside it to buy and optimize paid media. Together, they shape how brands acquire, convert, and retain customers in 2026—only now the emphasis has shifted to owned data, tighter attribution, and practical automation.
This guide lays out the essentials. We’ll define martech and how it differs from adtech; show the business case in 2026; map the main tool categories; and share real brand examples. You’ll get a plain-English blueprint for assembling a martech stack, the trade-offs to watch, and the trends that matter next—AI you can actually use, CDPs that standardize first-party data, and omnichannel personalization that respects consent while lifting performance.
Martech (marketing technology) is the software, data infrastructure, and platforms marketers use to plan campaigns, manage customer data, personalize experiences, and measure outcomes across channels. Put simply, it’s the operating stack behind modern marketing—from CRM and CDP at the data layer to automation, CMS, email, and analytics at the execution and measurement layers.
The term itself is a portmanteau of “marketing” and “technology,” and the category is broad by design: anything a marketing team uses to create, orchestrate, and evaluate marketing activity sits under the martech umbrella. That scope now spans thousands of commercial tools plus a growing set of custom, in-house apps. In 2025, researchers counted 15,384 commercial martech solutions, underscoring just how rich—and choice-heavy—the field has become.
In day-to-day use, martech’s role is practical: unify first-party data, coordinate journeys across paid, owned, and earned touchpoints, enforce consent and governance, and tie marketing effort to business results. Teams lean on it to scale routine workflows (segmentation, triggers, creative variants), to get a single view of the customer, and to prove what worked through analytics and attribution.
Martech manages owned relationships and lifecycle marketing; adtech powers the buying, delivery, and measurement of digital advertising. In other words, martech runs the programs that nurture and retain known audiences (email, mobile, web experiences, loyalty, analytics), while adtech provides the systems that acquire and reach audiences through paid media. Typical adtech components include demand-side platforms (DSPs), supply-side platforms (SSPs), ad exchanges, ad servers, and verification.
A quick way to compare them:
Martech matters in 2026 because it’s the system that helps teams grow with flat budgets, privacy-safe data, and measurable results. Average marketing budgets in the U.S. are holding at 7.7% of company revenue for a second year, which puts a premium on tools that raise productivity and prove impact. That’s why investment continues: U.S. B2B martech spend is forecast to approach $14 billion by 2027, and globally the category is projected to surpass $215 billion by 2027.
The data environment also makes martech non-optional. With Chrome’s Privacy Sandbox evolving and timelines shifting, marketers are building on first-party data and consented enrichment rather than third-party identifiers.
In 2024, 71% of brands, agencies, and publishers said they were expanding first-party datasets, with an average planned growth of 35% in the following 12 months—exactly the kind of foundation CDPs and analytics platforms are designed to operationalize.
Finally, 2026 is when AI becomes standard inside the stack rather than a side project. Gartner estimates that more than 80% of enterprises will have tested or deployed generative-AI-enabled applications by 2026, which raises the ceiling on what mainstream marketing tools can automate—from audience discovery to creative variants and on-site personalization.
💡 If you’ve ever felt your dashboard looks healthy while growth stalls, that’s a measurement problem, not a marketing one. We break down how to fix it here: Why your marketing metrics are lying about growth.
Marketing tech earns its keep by cutting manual work, surfacing better decisions from data, improving customer experience at scale, and proving impact with credible measurement. Below are the four benefits you can count on when the stack is set up well.
Martech shifts marketing from busywork to brainwork. Routine jobs—email sends, social posts, lead nurturing, reporting—run on autopilot, cutting errors and giving teams back time for creative, high-value work. Organizations that embrace automation see it pay off: research links it to a 14.5% lift in sales productivity and a 12.2% reduction in marketing overhead.
The gains now span the entire lifecycle, from brief to launch to analysis. Behavior-based triggers move instantly—abandon a cart and recovery flows fire; hit a score threshold and the lead routes to sales—no queues, no handoffs.
On the ground, that time adds up: employees who use automations report saving about 3.6 hours per week—roughly 23 working days a year. And at scale, the impact is tangible: FCB Global estimates it reclaimed around 1,000 billable hours and trimmed weekly status time by ~2 hours per person after streamlining and automating work in Adobe Workfront.
Modern stacks turn first-party data into decisions you can act on. Marketers say they rely on first-party data (84%) and analytics tools (88%) to plan and optimize campaigns—evidence that the center of gravity has moved from third-party signals to owned data and measurable outcomes.
Measurement is tightening as well. eMarketer notes 49% of marketers worldwide already use MMM, and 56% of U.S. ad buyers plan to lean more on MMM in 2025, reflecting a shift to models that quantify drivers of business outcomes and inform budget allocation.
Personalization at scale is now expected—and martech makes it practical. In Salesforce’s latest customer study, 73% of customers say brands treat them as unique individuals (up from 39% in 2023), yet only 49% feel companies use their data in a beneficial way.
The takeaway: customers notice better experiences, but trust and value exchange must be clear—something a well-governed stack can enforce via consent management, preference centers, and real-time decisioning. Salesforce
Last but not least, marketing tech improves the bottom line by making spend work harder. With unified tracking and attribution, teams can see which channels actually move revenue and shift budget accordingly. Brands that reach higher digital-marketing maturity (data connected, activation automated) report ~29% cost-efficiency gains and ~18% sales lifts versus less mature peers—direct proof that better measurement and activation pay off.
Attribution has also grown up. Moving beyond last-click, marketers blend MMM and multi-touch models to allocate dollars with fewer blind spots—an approach BCG calls the “four-legged stool” of measurement. In practice, better audience and in-flight optimization cut waste: Nielsen documents campaigns saving 25% of costs through daily optimization (and even 5× ROI on the measurement investment itself), while also finding that ads reaching the intended audience deliver significantly higher ROI than those that don’t.
A modern stack spans four layers: management tools for data and content, social and content optimization, analytics and insights, and—when you’re running paid media—advertising and programmatic systems.
These are your systems of record and execution for audiences, content, and orchestration.
Together, these martech platforms form the operational backbone—data in CDP/CRM, content in CMS/DAM, and workflows in automation.
Tools here help teams plan, produce, distribute, and refine content across channels.
This layer turns activity into decisions—reporting what happened and guiding what to do next.
💡 For an intelligence engine that connects planning, activation, and measurement, see Elevate.
When you run paid media, these adtech components plug into your martech data and measurement:
The IAB Tech Lab’s guidance and supply-chain references outline how these pieces interoperate across publishers, DSPs, SSPs, exchanges, ad servers, and verification vendors.
💡 For outcome-driven programmatic with first-party data, see Smart Supply—AI Digital’s offering that delivers KPI-tuned deals across any DSP, premium inventory access, IVT protection, and continuous optimization. For a primer on AI in DSPs, see our deep dive: How demand-side platforms use artificial intelligence to optimize advertising.
Martech pays off when teams connect first-party data to clear use cases—here are U.S.-based examples across enterprise, mid-market, SMB, and B2B:
Why these work: each example ties a specific capability to a business metric—intent data to pipeline (Cisco), loyalty and messaging to retention and orders (Burger King), lifecycle automation to revenue mix (Heat Transfer Warehouse), and unified profiles to conversion (Alaska). That’s the model to emulate: start with the outcome, map the data you need, then choose the smallest set of tools that can execute end-to-end.
A martech stack is the collection of software and data systems a company uses to manage, execute, and measure its marketing. Think of it as the practical toolkit that powers day-to-day work: data hubs (CRM/CDP), creation and orchestration tools (automation, CMS, email/SMS), and measurement (analytics, attribution, BI).
A good stack is integrated rather than simply accumulated. Data should move cleanly from capture to activation to reporting, so teams can segment once and use those audiences everywhere, then see outcomes in a single view. That’s also why “stack optimization” is treated as an ongoing discipline—regularly evaluating components, tightening integrations, and retiring tools that no longer earn their keep.
Two realities shape stack design in 2026:
What trips teams up most often isn’t a missing feature—it’s disconnected data. In MarTech.org’s 2025 stack research, data integration topped the list of management challenges, especially for mid-sized companies. The fix is architectural: define a clear data spine (typically CDP/CRM + event collection), standardize IDs and consent, and connect execution tools to that core rather than to each other ad hoc.
In short, your mar tech stack is not a trophy case; it’s an interconnected system with a job to do. Start with the outcomes you need to prove, map the data and workflows required, then choose the smallest set of tools that can deliver those outcomes reliably.
Start with outcomes, then pick the smallest set of tools that can deliver them reliably. Treat the stack as an operating system you design on purpose: clear goals, capability gaps, clean data flows, and measurement you trust.
💡 For a view of how AI Digital assembles end-to-end advertising ecosystems, see From managed services to Smart Supply.
Anchor everything to specific business results and the customer moments that move them.
Buy for fit and integration, not for feature lists.
Run a proof of concept with real use cases and a deadline. Define pass/fail in advance.
Design the data flow first; tools should snap to it.
Prove what worked, and make it easy to act on the findings.
Done well, this approach yields a compact, interoperable stack that your team actually uses—and that your finance lead trusts when you show the results.
Even good tools stumble without the right design, governance, and skills. These are the four friction points most U.S. teams run into—and the evidence behind them.
Tool sprawl makes integration the hard part. As mentioned, most marketers say data integration is their biggest stack challenge, and nearly a quarter worry most about data silos—signs that systems still don’t talk to each other.
The scale is daunting. The average enterprise now runs about 897 apps, 66% still don’t deliver an integrated user experience across channels, and 95% of IT leaders say integration is a hurdle to effective AI—so brittle connections slow both marketing and AI initiatives.
Fragmentation shows up inside marketing, too. Two-thirds of teams juggle 16+ mar tech tools, and 70% say it’s harder than ever to identify audiences across touchpoints—exactly what siloed data makes difficult.
That’s why “stack sprawl” hurts: overlapping tools, duplicate buys, and legacy platforms add complexity, cost, and error. Only 31% of marketers are fully satisfied with their ability to unify customer data, which means more time wrangling APIs and formats—and less time executing.
The fix is deliberate integration: standardize on shared data models, consolidate redundant tools, and wire key events for real-time sync. Do that, and the stack becomes greater than the sum of its parts.
Privacy rules and rising consumer expectations now shape every martech decision. When customer data runs through dozens of tools, brands have to prove they’re collecting, storing, and using it the right way—everywhere.
Why this is hard in real life:
Retrofitting isn’t cheap—but it pays. The average organization reports a 1.6× return on privacy investment and 95% say benefits exceed costs (reduced sales delays, higher trust, operational efficiency). That’s a strong case for modernizing consent, identity, and governance in the stack rather than patching forever.
The takeaway: good martech is consent-first; minimizes data by design, favoring first-party data and privacy-preserving targeting and measurement; and is kill-switch ready with universal opt-out, deletion, and audit trails that propagate quickly across every tool.
💡 For a deeper dive on life after cookies and privacy-first targeting, see AI Digital’s explainer on the cookie-less landscape: In a Cookie-less World: New Challenges and Opportunities
The bill is bigger than licenses. Integration, customization, data storage, training, and day-to-day ops all stack up—while budgets stay tight. In 2025, 59% of CMOs said they don’t have enough budget to execute their strategy. No wonder teams lean on tech for efficiency—but the cost pressure is real.
Underuse drags ROI. Marketers are tapping only a fraction of their mar tech stack’s capabilities, which means a lot of paid-for value sits idle. The waste shows up elsewhere too: companies now lose an average $21M per year on unused SaaS licenses. If the stack isn’t fully adopted, it’s hard to prove payback.
Measurement is the chokepoint. Even when campaign metrics improve, tying results back to specific tools is tough because measurement is fragmented. That frustration is showing up in sentiment: reports of martech “disappointment” have climbed as usage and payoff fall out of sync.
Net effect: martech can pay—if you control total cost of ownership, drive adoption, and fix measurement. Without that, you’re paying premium prices for partial performance.
Marketing has gotten deeply technical. Teams need people who can translate strategy into data models, automations, and integrations—and those hybrids are scarce. Two-thirds of managers say recent hires aren’t fully prepared for today’s work, which stretches ramp times. Standardized onboarding helps, but most companies still underinvest; new hires typically need 6–8 months to reach full productivity.
When only one or two specialists truly understand your automation platform or CDP, you get bottlenecks and operational fragility. Platform change or turnover can knock campaigns offline and stall roadmaps. It’s also why stacks with low utilization struggle to scale—expertise is concentrated, so advanced features never see daylight.
What to do about it: Build a skills map and fill it by defining the critical competencies for your stack, de-risk key platforms by creating playbooks and cross-training; make onboarding a performance system with 30/60/90 goals; and treat adoption as a product with clear owners, measurement, and enablement sprints.
Here’s where the next 18–24 months are headed: AI becomes a built-in feature of every major platform, first-party data and CDPs anchor decisioning, and personalization stretches across channels in real time. Add privacy-preserving activation and sharper measurement, and the stack you design in 2026 will be leaner, faster, and easier to prove out. The sections below outline the shifts to plan for—and how they change day-to-day work.
AI is threaded through modern martech. 78% of companies use AI in at least one business function, and 71% say they’re regularly using generative AI, with marketing and sales among the top adopters.
The payoff is showing up in the numbers: in business units using gen AI, a majority report cost reductions and growing shares report revenue increases; 17% say gen AI drove 5%+ of EBIT over the past year. That traction explains why leaders plan to more than double the share of marketing work powered by AI over the next three years (17.2% → 44.2%).
What this means: AI won’t replace marketers—it’ll amplify them. Teams that wire AI into segmentation, forecasting, testing, and creative will spend less time crunching and more time directing strategy.
For our point of view on practical AI in advertising, see The future is now: how AI Digital embraces AI technologies to change the programmatic game. Gartner+2Salesforce+2
First-party data is now the center of gravity. 84% of marketers say they use first-party data in their programs, while reliance on third-party data has fallen to 61% in 2024 (down from 75% in 2022)—a clear, ongoing pivot toward data you control.
By 2026, a CDP—or an equivalent first-party data layer—will be table stakes. The category itself is gaining momentum: the CDP Institute’s July 2025 update reports the strongest organic growth since 2022, with employment across CDP vendors up 3.4%.
CDPs are also shifting from “collect and unify” to “decide and act.” Modern platforms ingest streaming data and feed AI models for instant personalization and next-best-action. In one 2025 study, 84% of CDP users said their platform makes AI projects easier, 92% reported success against business objectives, and 45% saw CDP ROI within 3–6 months (and 88% within 18 months).
Privacy-safe enrichment is expanding alongside the first-party push. Clean rooms let brands and partners analyze overlap without exposing raw data. Adoption is rising—about two-thirds (66%) of U.S. ad/data pros say they’re using clean rooms in some capacity—yet industry bodies note it’s not mass adoption across client-side marketers just yet, so integration maturity still varies.
Bottom line: as third-party signals fade, teams are standardizing on a CDP to own identity, orchestrate personalization, and measure outcomes—faster, and with better governance.
Customers expect brands to connect the dots. Salesforce finds 76% of customers expect consistent interactions across departments, and 74% say they use multiple channels to start and finish a transaction—so continuity is expected.
Customer journey orchestration makes that continuity real. AI-driven engines coordinate messages in real time across channels; a support chat reshapes the next website visit; an in-store purchase suppresses irrelevant emails; the mobile app remembers what was browsed on desktop. When done well, personalization moves the needle—and the majority of leaders (89%) agree that it will be crucial to business success over the next three years.
Bottom line: omnichannel personalization is shifting from aspiration to standard. The question is how well you’ll harness it to deliver consistent, context-aware experiences customers can feel.
Beyond the big pillars, several shifts will change how you activate data, place media, measure impact, and assemble your stack. Start by planning for the four below:
Taken together, these trends point to a practical blueprint: invest in first-party data and decisioning (often via a CDP), automate what can be trusted, design for privacy by default, and upgrade measurement so wins are visible across channels.
Martech is a cornerstone of modern marketing because it connects your data, your tools, and your customer experience into one working system. The right stack helps teams automate routine work, act on first-party insight, personalize responsibly, and prove impact with credible measurement. There isn’t a universal stack; the best one mirrors your goals, data maturity, and resources—start with the outcomes you need, then assemble the smallest set of interoperable tools that can deliver them.
Looking ahead, AI will be built into almost every tier of the stack, and data discipline will separate average programs from high performers. Invest in a clean data spine, trustworthy decisioning, and measurement that guides budget moves, and you’ll keep gaining efficiency and return as channels and policies change.
Ready to put this into practice? AI Digital helps marketing teams design and operate high-leverage stacks: unifying first-party data, orchestrating journeys, and tying activation to results. Talk to us about planning and measurement with Elevate and programmatic performance with Smart Supply—we’ll help you build a stack that your team actually uses and your finance lead trusts.
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Inefficiency
Description
Use case
Description of use case
Examples of companies using AI
Ease of implementation
Impact
Audience segmentation and insights
Identify and categorize audience groups based on behaviors, preferences, and characteristics
Automated ad campaigns
Automate ad creation, placement, and optimization across various platforms
Brand sentiment tracking
Monitor and analyze public opinion about a brand across multiple channels in real time
Campaign strategy optimization
Analyze data to predict optimal campaign approaches, channels, and timing
Content strategy
Generate content ideas, predict performance, and optimize distribution strategies
Personalization strategy development
Create tailored messaging and experiences for consumers at scale
Start with outcomes. If you can trace campaigns to revenue, see reliable lift from experiments, and launch new journeys quickly without manual data patching, your stack is pulling its weight. A simple test: pick three KPIs (e.g., qualified pipeline, repeat purchase rate, cost per acquisition) and confirm you can attribute changes to specific programs and audiences from a single reporting layer. If you can’t, you have a design or integration problem, not a tool problem.
Quarterly for performance and adoption, annually for strategy. Each quarter, check feature usage, data quality (freshness, join rates, consent coverage), and overlap with other tools. Once a year, revisit goals and retire anything that isn’t earning its seat. Tie renewals to a clear business case and a short list of must-have capabilities.
CRM is the system of record for people and accounts. It captures sales activity, pipeline stages, and commercial outcomes that marketing needs to prove impact. Connected to your CDP and automation platform, it closes the loop: audiences flow out for activation, results flow back for attribution and forecasting.
There isn’t a universal winner. The most effective platform is the one that cleanly integrates with your data spine (CRM/CDP/analytics), supports your core use cases out of the box, and can be operated by your team. Favor fewer tools that do more, strong APIs, and a vendor you trust for enablement.
1. Write three measurable goals and the customer moments that move them. 2. Choose a source of truth for identities and events (CRM + analytics, often with a CDP). 3. Add one activation channel you’ll use immediately (email/SMS or on-site personalization). 4. Stand up a basic measurement plan with dashboards and at least one test-and-learn method. 5. Prove lift on a narrow use case, then expand—don’t buy everything at once.
Set a procurement rule: no new tool without a written use case, an integration plan, an owner, and a decommission plan for any overlapping product. Measure feature adoption, not just logins. Standardize IDs and events so you can swap components without breaking everything. When in doubt, consolidate to platforms that integrate natively with your core data.
The martech space is the market of software and data platforms that marketers use to plan, execute, and measure marketing—covering CRM and CDP for customer data, automation and CMS/DXP for orchestration and content, analytics and attribution for insight, and integrations to ad platforms when paid media is in scope.
