The best ABM tools for winning high-value accounts in 2026
Explore the 2026 ABM tech stack and discover how AI-driven platforms are transforming targeting, personalization, and ROI across the modern B2B marketing journey to help you choose the best solutions for your team.
As CMOs come under increasing pressure to demonstrate marketing’s value to the business, more and more are turning to Account-Based Marketing (ABM) as their main strategy to drive meaningful and measurable growth.
Compared to other methods, ABM has always been the best course for return on investment. Why? Because it focuses all sales and marketing efforts on a specific list of high-value accounts.
Studies show that marketers report higher ROI with ABM compared to other strategies, and companies using it see a higher success rate overall.
Userled 2025 AI + ABM Trends Report
With ABM, it’s all about quality over quantity – flipping the traditional marketing funnel to find the right buyers first, then building truly unique, exciting, and engaging experiences for them.
Why does this matter now? Well, buyers today expect personalized experiences, and that’s become the cost of entry for brands and businesses alike. It’s no longer enough to create disparate or one-off campaigns. The only way to win over prospects and retain customers is through interconnected moments that consistently meet needs.
How technology unlocks the power of 1:1 at scale
In the past, creating bespoke 1:1 experiences at scale was a challenge, there was simply no way to create campaigns for every target account at speed. Today, however, organizations now have access to platforms and end-point solutions that make creating, distributing, and optimizing 1:1 ABM campaigns at scale a breeze.
But the ABM tech stack for 2026 isn't just about faster execution – it's about creating meaningful content that engages audiences, embedding sophisticated AI to make smarter decisions, empowering sales to act in the moments that matter most.
B2B buyers now demand consumer-level convenience, and only AI-powered platforms, in conjunction with aligned marketing and sales teams, can deliver that level of precision at scale.
So the question now is, with the plethora of tools available to you and your team, what options should you consider?
Here are the best tools, broken down by the stage they dominate in the modern ABM lifecycle.
1. Account research and intelligence: knowing who to target
What it is and why it matters
Account research and intelligence are how you get the full picture of potential high-value accounts.
We’re talking about more than just a company’s industry or size, we’re talking about deep data, firmographics, technographics (what software they use), contact details for key decision makers, and who makes up the overall buying group.
The strategic goal here is precision. Account research and intelligence empower you to move beyond basic variables like business size to identify accounts based on specific, high-value triggers – like knowing a company just raised a new funding round or is actively using an outdated competitor product.
Let’s take a look at some of the top tools for account research and intelligence to see which comes out on top.
Platform
Score (out of 10)
Pros
Cons
LinkedIn Sales Navigator
9.5
Rich social signals: Provides real-time insights (job changes, posts, company updates) to personalize outreach and timing.
Advanced filtering: Powerful search filters by title, seniority, company size, and more to match your ICP precisely.
Account & lead tracking: Save accounts, set alerts, and monitor activity for continuous account intelligence.
Team collaboration: Features like TeamLink reveal internal connections for warm introductions and coordination.
CRM integration: Syncs with Salesforce/HubSpot and enables Smart Links to track engagement.
Scalable personalization: Efficiently manage many accounts while maintaining relevance.
Expensive to use: While LinkedIn Sales Navigator offers unparalleled account research, it’s quite pricey for smaller teams.
Steep learning curve: The advanced search capabilities and filters might be overwhelming for new users.
InMail limitations: Users are restricted to 50 InMail messages each month, so emphasis should be on quality outreach.
Data quality and enrichment: LinkedIn profiles can be out of date, leading to unreliable targeting.
ZoomInfo
9.0
Extensive company & contact data: Large, detailed database with firmographics, technographics, and verified contact info for precise ABM targeting.
Intent & buying signals: Identifies in-market accounts through behavioral and intent data to prioritize outreach effectively.
Data enrichment & hygiene: Automatically fills gaps and updates CRM records with current company and contact details.
Strong integrations: Connects seamlessly with CRMs, marketing automation, and sales tools for unified workflows.
Scalable segmentation: Advanced filters enable dynamic ICP building and high-volume account segmentation.
Market credibility: Widely adopted and trusted across B2B teams for sales intelligence and ABM programs.
High cost & opaque pricing: Cost can be high for full feature set; coverage weaker outside North America.
Steep learning curve: The feature-rich platform can be overwhelming without dedicated ops or enablement support.
Limited ABM orchestration: Strong for data, but weaker for predictive analytics and multi-channel campaigns.
Data accuracy gaps: Some contact and company info might be outdated or inconsistent, especially outside North America.
Commonroom
9.0
Rich engagement signals: Unifies community, social, product, and web activity into one feed of buying signals.
Contact-level insights: Matches signals to real people via AI (Person360) rather than account buckets.
Custom scoring & prioritization: Allows configurable scoring of accounts/contacts using fit + signals.
Workflow triggers: Set thresholds so teams know when target accounts show notable activity.
Limited contact enrichment: Doesn’t always provide full or direct contact data, and teams often require a complementary provider.
Steep learning curve: It’s not fully plug-and-play; you need to learn the platform to get real value.
Once configured, the effort can pay off with advanced signal capture and account insights.
LinkedIn Sales Navigator isn’t just a contact finder, it’s a full lens into account ecosystems. You can slice by role, seniority, department, technology profile, and shared network context to discover the full buying circle.
Its real-time alerts on job changes, new hires, executive moves, content engagement, and account activity allow GTM teams to jump in at the right moment.
Newer AI features like Account IQ consolidate disparate public signals into digestible summaries, giving you a “single pane” view of key account dynamics.
Because it lives in the same environment your prospects live (LinkedIn), the data is inherently social and relational, meaning you see context you often don’t get via static firmographic/technographic tools.
Real-world impact: TetraScience
A recent example comes from TetraScience, a life-sciences company that needed sharper visibility into complex buyer groups. Using LinkedIn Sales Navigator, their team mapped key accounts by size, geography, and function, uncovering hidden decision-makers through org-chart and relationship insights.
By validating and enriching external data with LinkedIn’s live intelligence, they built a more accurate picture of each account’s hierarchy and priorities. The result was tighter targeting, reduced wasted outreach, and a clearer path to the right buyers, proving how real-time social and professional signals can transform account research precision.
2. Account selection and prioritization: timing is everything
What it is and why it matters
Account selection and prioritization is the process of ranking your target accounts, ensuring sales and marketing energy goes where it’ll matter most.
This capability is powered by algorithms and predictive models that evaluate three key factors: fit(how closely an account matches your ICP), engagement (how actively they interact with your brand), and intent (signals that suggest they’re in the market).
However, in modern ABM, it’s not just about finding the right accounts, it’s also about finding them at the right moment.
Tools like 6sense and Demandbase, for example, can identify when an account is entering a buying cycle – often before any public signal or RFP goes live. These early insights give teams a critical first-mover advantage: the ability to engage sooner, personalize messaging, and accelerate deals before competitors even show up.
Let’s take a look at the leaders in this category.
Platform
Score (out of 10)
Pros
Cons
6sense
9.2
Predictive scoring & fit modeling: Uses AI and predictive analytics to surface high-propensity accounts, not just based on firmographics.
Intent and engagement signals: Combines first, second, and third-party intent data to detect when accounts are actively researching.
Website visitor de-anonymization: Maps anonymous site behavior to target accounts for earlier detection of interest.
Aligned orchestration & sales intelligence: Prioritization ties into execution — scores feed into campaigns, alerts, sales plays, and dashboards.
Strong integrations & workflows: Deep integrations with CRMs, marketing automation, and ABM tooling ensure prioritized accounts stay actionable.
High cost & opaque pricing: Enterprise-level pricing, often not transparent; can be prohibitive for smaller teams.
Steep learning curve & complexity: Many users report the product is dense, with many features and data layers to master.
Performance / UI latency issues: Some users note the interface can be slow or clunky when handling large datasets.
Weaker on raw contact enrichment: Strong in account intelligence, but for contact-level data, a complementary provider may be needed.
Demandbase
8.7
AI-driven account scoring & qualification: Combines fit, intent, and engagement data to rank accounts for smarter prioritization.
Broad intent and engagement signals: Integrates first, second, and third-party intent data, plus “Engagement Minutes,” to detect active accounts.
Unified account view: Merges internal (CRM/MAP) and external data into a single dashboard for full visibility.
Buying group / committee identification: Helps detect multiple stakeholders or decision-makers within target accounts.
Flexible segmentation & selectors: Advanced filtering to define account audiences by verticals, signals, or engagement stages.
High cost & complexity: Implementation, licensing, and operations can be expensive; setup often requires dedicated ops or technical resources.
Steep learning curve & usability issues: Some users find the UI non-intuitive; diagnostics and filtering take time to master.
Reporting & attribution limitations: Spend attribution and reporting can be less flexible or transparent.
Dependence on data completeness & CRM hygiene: Weak or inconsistent internal data can limit model accuracy.
Gaps when accounts not in system: Accounts outside Demandbase’s data universe may not be scoreable or targetable.
Keyplay
8.0
ICP modeling + lookalike generation: Build and refine ideal customer profiles, then surface similar accounts automatically.
Integrated scoring & enrichment: Scores and enriches accounts continuously, pushing data into CRM for prioritization.
Fast setup & intuitive workflows: Users can get started quickly with lightweight setup and intuitive UI.
Transparent, inspectable signals: Easily see which signals or attributes drive scores for clarity and trust.
Good value relative to legacy tools: More accessible pricing compared to large ABM suites, while retaining strong core capabilities.
Filtering/custom segmentation limits: Filtering capabilities can be weaker than manual exports or external manipulation.
CRM sync quirks / routing issues: Occasional misroutes or incorrect account assignments may require manual cleanup.
Pricing can escalate: While initially cost-effective, scaling to multiple ICPs or large account volumes can raise costs.
6sense and Demandbase are the two powerhouses here. While Demandbase earns high marks for overall customer satisfaction and robust advertising solutions, 6sense is often cited as the best platform for AI modeling based on intent data.
Its core strength lies in processing billions of signals to identify which prospects are most likely to convert.
Within three months, 69% of their closed opportunities were accounts 6sense had identified as a strong-profile fit, leading to a 9.5x increase in later-stage accounts.
3. Content personalization and distribution (AI-powered)
What it is and why it matters
Content personalization means tailoring your marketing, messages, assets, visuals, and offers, to the specific needs and pain points of a target account and its key stakeholders. It moves past generic mail merges and into hyper-specific relevance.
Why is personalization so vital? Well, in B2B, tailored content builds trust and credibility by showing you understand their unique challenges. The modern challenge is leveraging AI to achieve this hyper-personalization at scale without sacrificing content quality.
Let’s take a look at the leaders in this category.
Userled is a crucial specialization tool in this category, designed to create scalable, hyper-personalized assets (like sales microsites, ads, event invites, and 1:1 landing pages) for every stage of the buying journey.
The platform’s no-code, AI content editor allows teams to craft hundreds of personalized pages, assets, and emails, drastically speeding up campaign deployment. All without help from the website development team.
This means that one-person marketing teams can replicate enterprise-level campaigns without additional headcount or budget.
What’s more, with the Userled browser extension, sales teams can effortlessly create and distribute 1:1 assets to prospects on the fly, and track engagement without logging into Userled.
Engagement tracking focuses on continuously monitoring buyer behavior, from content downloads (known activity) to anonymous website visits and third-party research (intent data).
Predictive analytics then takes this data and uses AI to identify which accounts are most likely to close. It also acts as an early warning system, using data to detect if an account is stalling or researching a competitor.
Let’s take a look at the top contenders in this category.
Platform
Score (out of 10)
Pros
Cons
Factors.ai
9.0
Visitor de-anonymization & account engagement mapping: Recovers company identities behind anonymous web traffic and links behavioral touchpoints to account profiles.
Multi-touch attribution & journey analytics: Connects ad, website, and CRM behaviors to surface influence and engagement paths.
Predictive signals & account scoring: Uses historical and real-time data to predict which accounts are more likely to convert.
Strong integrations & unified view: Connects natively with CRMs, ad platforms (LinkedIn, Google Ads), and analytics tools for unified tracking.
Actionable alerts & workflow triggers: Enables alerts or automated actions when target accounts cross engagement thresholds.
Accuracy & signal ambiguity: Attribution can be messy when distinguishing ad vs. organic sources.
Steep learning curve & feature complexity: The platform’s predictive and attribution tools require training to master.
Model dependency on data quality & volume: Accuracy drops if traffic or CRM data quality is low.
Reporting / visualization gaps: Some comparative or custom report templates may be missing.
Scaling & feature gating: Higher account volumes or advanced predictive models may require higher-tier plans.
AdRoll ABM
8.5
Ad-level & account engagement reporting: Visualizes account-level engagement with ads, web, and content.
DSP + ML-driven targeting & optimization: Uses machine learning to optimize spend and engagement based on intent and fit.
Spiking account detection & multi-touch attribution: Identifies account “spikes” in engagement and ties to conversion influence.
Smooth integration with CRMs & ABM workflows: Embeds engagement signals into tools like HubSpot for activation.
Ease of use for campaign setup: Easier entry for small and mid-size teams compared to complex ABM suites.
Limited depth to predictive analytics: Simpler modeling than AI-first platforms like 6sense or Demandbase.
Reporting customization constraints: Engagement and ad reports can be restrictive or less flexible.
Terminus
8.2
Account-level engagement aggregation: Combines ad, web, and anonymous data into a single account dashboard.
Factors.ai stands out for its ability to unify every engagement signal, from anonymous web visits to CRM interactions, into one predictive intelligence layer.
Its AI models score and rank accounts based on real buying intent, alerting teams when prospects move deeper into the funnel or stall out.
Unlike static dashboards, Factors.ai turns analytics into action through automated alerts, audience segmentation, and CRM workflows. Its transparent multi-touch attribution reveals which channels actually drive revenue. The result is faster, data-driven prioritization and sharper visibility into every stage of the buying journey.
Real-world impact: AudienceView
AudienceView employed Factors.ai’s account scoring, engagement tracking, and intent signals to gain full visibility across multiple touchpoints.
They leveraged that insight to drive pipeline: 15% of their pipeline in Q4 2024 was generated from accounts flagged by Factors.ai as behaving in-market. These hot accounts were converted 8x faster.
5. GTM orchestration
What it is and why it matters
GTM orchestration is where go-to-market data is turned into real business outcomes. It’s the operational bridge that converts intent, engagement, and account scoring into timely actions for reps. Think real-time alerts, automated routing, and prescriptive guidance – all delivered from inside the CRM or wherever sellers work.
At this stage, the goal is speed and precision. When a buying signal appears, every second counts. Effective GTM ensures that when high-intent accounts show activity, they’re transitioned over to the right owner, with full context and actionable next steps, keeping sales and marketing in sync.
Platform
Score (out of 10)
Pros
Cons
Cargo
9.2
Modern revenue orchestration: Built to activate data and automate GTM workflows (scoring, routing, enrichment) on top of your data stack.
Composable & headless architecture: Integrates as a “headless interface” into your CRM or data warehouse without rigid UI constraints.
AI / automation orientation: Supports automating segmentation, routing, and next-step decisions using logic and AI.
Agents & GTM assistant: Enables ops teams to build workflows that surface insights and triggers directly in Slack and other tools.
Predictable cost: Usage-based pricing with an accessible entry point.
Newer product / unproven at scale: Limited public case studies for very large-scale deployments.
Complex configuration & technical dependency: Highly flexible but requires GTM ops or engineering resources for modeling and workflow setup.
Feature maturity gaps: Some advanced orchestration or edge cases may lack polish compared to mature players.
LeanData
9.0
Strong routing & matching logic: Known for lead-to-account matching, SLA-based routing, and lead distribution workflows.
Auditability & debugging tools: Visual graphs, audit trails, and retrospective views simplify tracing and fixing logic errors.
Ease of adjustment: Users can easily modify routing rules or update logic for changing GTM processes.
Complexity and cost with scale: As routing logic expands, maintenance and complexity can increase.
Primarily focused on routing: Strong routing capabilities, but weaker on multi-channel orchestration or predictive plays.
Dependence on data quality & CRM hygiene: Duplicates or stale CRM data can degrade routing accuracy.
Chili Piper
8.5
Instant booking & lead routing: Automates meeting booking and lead routing instantly after form submission.
Smooth CRM & calendar integrations: Syncs with Salesforce, HubSpot, Google Calendar, and others for seamless handoffs.
Cargo brings a modern approach to GTM orchestration, built to activate data rather than just report it. Its composable and headless architecture lets teams design custom GTM workflows for scoring, routing, and enrichment directly on top of their data stack.
The platform integrates natively with CRMs and data warehouses, connecting sales and marketing systems without forcing rigid UI layers. Cargo’s AI and automation capabilities enable dynamic segmentation, intelligent routing, and predictive “next-best actions,” driving faster and more consistent follow-up across teams.
By operating as a flexible, AI-powered orchestration layer, Cargo allows GTM Ops and RevOps teams to model their ideal process logic while maintaining transparency, auditability, and speed.
Real-world impact: Gorgias
Gorgias’ GTM engineering team used Cargo to optimize their email outreach. They replaced static segmentation with dynamic, AI-driven logic, built conditional flows for real-time customization, and integrated with external APIs for automated outreach.
This led to a 70 percent increase in conversion rates compared to their previous campaigns.
6. Pipeline insights and revenue attribution
What it is and why it matters
Revenue attribution is the systematic measurement of every single marketing and sales touchpoint across the entire account lifecycle, from first awareness to closed revenue. You assign credit to the GTM activities and campaigns that actually influenced the deal.
Due to the complex nature of B2B buying, simple first- or last-touch models don't cut it. You need granular, account-level attribution to prove the ROI of your ABM strategy.
Modern tools use sophisticated multi-touch models that centralize data at the account level, helping RevOps leaders forecast pipeline accurately.
Let’s take a look at the leaders in this category.
Platform
Score (out of 10)
Pros
Cons
Hockeystack
9.1
Unified data foundation: Natively connects CRM, ad platforms, and web analytics without manual stitching.
Actionable multi-touch models: Provides clear attribution paths at the account level, not just user-level touchpoints.
Strong visual analytics: Dashboards and journey maps make insights accessible for RevOps and GTM teams.
Fast report creation: Includes pre-built templates and an AI agent (Odin AI) that generates reports from text input.
Data model complexity: Custom event tracking setup may be challenging for fragmented data environments.
Limited custom models: Some advanced users may find customization restricted to predefined attribution logic.
Learning curve for analysts: Analysts must understand multi-touch attribution frameworks to interpret results effectively.
Dreamdata
8.7
Mature attribution framework: Proven, battle-tested attribution logic trusted by mid-market and enterprise teams.
Deep CRM and marketing integrations: Syncs with Salesforce, HubSpot, and major ad networks for consistent revenue tracking.
HockeyStack delivers full-funnel visibility across marketing, sales, and product touchpoints, allowing teams to see exactly which actions lead to revenue. Its strength lies in its unified data model that connects CRM, web analytics, and ad platforms without complex integrations.
The platform automatically builds account-based journey maps, revealing which campaigns, channels, and reps influence each deal. Its visual dashboards make it easy for RevOps and marketing leaders to assess pipeline health, optimize spend, and prove marketing’s direct impact on revenue.
HockeyStack also includes advanced attribution modeling, cohort tracking, and AI-powered insights, helping teams forecast pipeline and uncover hidden drivers of deal progression.
Real-world impact: RudderStack
Using HockeyStack, RudderStack unified its attribution data across multiple models, eliminating the need to switch between separate analytics tools. The team can now see spend, cost per lead, and cost per pipeline in one platform, its single source of truth for attribution. This visibility enables faster, data-driven budget adjustments and immediate tracking of outcomes.
As a result, RudderStack achieved a 3x increase in attributed pipeline from organic search, reduced ad spend, and gained 100% attribution visibility across all touchpoints.
Selecting your ABM technology stack for 2026
As the ABM landscape evolves, one thing is clear — the next wave of growth will come from teams that can connect data, technology, and human insight into a single, orchestrated motion. The tools highlighted in this guide show just how far the ecosystem has come: from AI-driven account intelligence to predictive analytics and dynamic content personalization, the ABM tech stack of 2026 is smarter, faster, and more interconnected than ever before.
But technology alone isn’t the differentiator — how you use it will define your success. The most effective revenue teams are already shifting from static campaigns to living, adaptive programs that learn and evolve with every signal.
To stay ahead, consider how you’ll:
Unify your data foundation – Connect CRM, intent, engagement, and attribution tools into a single source of truth for every account.
Align marketing and sales activation – Ensure that insights flow seamlessly into sales workflows so reps can act on engagement signals in real time.
Personalize at scale with AI – Use intelligent content and automation platforms to deliver 1:1 experiences across every channel and buying stage.
Prioritize precision over volume – Focus energy and spend on the accounts most likely to convert, using predictive models to guide your next move.
Measure, learn, and optimize continuously – Adopt attribution and analytics tools that make ROI transparent and feed insights back into your GTM engine.
The next era of ABM won’t be defined by who has the most tools, but by who uses them to create meaningful, measurable connections with their most valuable customers.
Now’s the time to build your stack, align your teams, and shape the future of how your business wins high-value accounts.
Generated £1.3M pipeline by focusing on UTM parameters personalisation.
Pedro Costa
,
Growth experimentation
Generated £1.3M pipeline by focusing on UTM parameters personalisation.
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