Real-World ABM Campaign Examples: Case Studies and Best Practices

Account-Based Marketing (ABM) has proven itself as a high return strategy for B2B organizations aiming to drive targeted engagement, align sales and marketing teams, and secure high-value deals. While the theory of ABM is straightforward, implementing it successfully requires strategy, tools, and collaboration. Today, that also means starting from buying signals rather than static lists, so campaigns reach accounts while they are in-market.

To help you visualize ABM in action, we’ll explore real-world examples of successful ABM campaigns and distill the best practices that led to their success. Each example is mapped to an ABM campaign type (1:1, 1:few or 1:many), with the result and the lesson to copy.

ABM campaign examples at a glance

What is Account-Based Marketing?

ABM flips traditional inbound marketing on its head by identifying specific, high-value accounts and tailoring personalized campaigns to engage them as “markets of one. Four elements make it work: account selection, personalization, multi-channel engagement, and account-level measurement. For the full framework, see our Account-Based Marketing guide.

What is an ABM Campaign?

An ABM (Account-Based Marketing) campaign is a highly targeted marketing initiative that concentrates organizational resources on a specific set of high-value accounts rather than a broad audience. By treating individual companies as “markets of one,” an ABM campaign utilizes personalized content and cross-team alignment between sales and marketing to engage key stakeholders within a target account. This precision-based strategy leverages buying signals, intent data and multi-channel outreach to accelerate the sales cycle and maximize return on investment (ROI) for complex B2B deals.

ABM campaign types: 1:1, 1:few and 1:many

ABM campaigns differ mainly in how many accounts each campaign serves. Each example below maps to one of three types.

Most programs blend all three: 1:many campaigns reach the broad ICP, 1:few campaigns serve clusters, and 1:1 campaigns go to strategic accounts. Buying signals, such as an intent surge, a funding round or a leadership change, tell you when an account should move up a tier.

Case Study 1: SundaySky’s 220% Conversion Leap with SalesIntel

ABM type: 1:few · Timeframe: case study published October 2023

Objective:

SundaySky, a leading video platform, aimed to enhance their ABM campaigns by effectively targeting high-value accounts to improve engagement and conversion rates.

Approach:

• Data Accuracy Enhancement: SundaySky integrated SalesIntel’s human-verified data to ensure their outreach efforts were directed at the right decision-makers within target accounts.
• Personalized Campaigns: Leveraging precise data, SundaySky created highly tailored campaigns that resonated with their target audience, improving engagement.
• Strategic Data Integration: SalesIntel’s data solutions were embedded into SundaySky’s ABM workflows to streamline targeting and personalization.

Results:

• A 220% increase in conversion rates from targeted campaigns.
• Enhanced audience engagement through personalized outreach.
• More efficient ABM processes powered by accurate data.

Best Practice:

Integrate human-verified data into ABM strategies to ensure targeted, personalized outreach to the right contacts. Today, pair that data with ICP lookalike modeling and buying signals, so the target list contains accounts that match your best customers and show in-market behavior.

For a detailed overview, you can read the full case study here: SundaySky’s 220% Conversion Leap with SalesIntel

Case Study 2: BetterCloud’s Transition from ZoomInfo to SalesIntel

ABM type: 1:many (data foundation) · Timeframe: case study published December 2023

Objective:

BetterCloud, a SaaS operations management platform, needed to cut data costs and stop managing separate vendors for account, contact, technology and intent data, without losing data quality.

Approach:

• Switch to SalesIntel: BetterCloud transitioned from ZoomInfo to SalesIntel for superior data quality and sales intelligence.
• Enhanced Targeting: With access to reliable, accurate data, BetterCloud’s sales and marketing teams identified and focused on high-value prospects.
• Vendor Consolidation: BetterCloud replaced ZoomInfo and Demandbase with SalesIntel, and used Research on Demand to request the specific data points it needed.

Results:

• Significant improvement in data accuracy, enhancing targeting efforts.
• Increased efficiency across sales and marketing teams.
• Saved over $100,000 compared with the previous ZoomInfo arrangement.

Best Practice:

Prioritize accurate and comprehensive data to enhance ABM effectiveness and team efficiency.

For more insights, you can explore the full case study here: BetterCloud’s Successful Transition from ZoomInfo to SalesIntel

These case studies demonstrate how leveraging SalesIntel’s accurate and comprehensive data can significantly enhance ABM strategies, leading to improved engagement and higher conversion rates.

Case Study 3: Snowflake : Scaling 1:1 ABM to 2,000+ Accounts

ABM type: 1:1 at scale

Objective: Snowflake, a cloud data platform, needed to keep scaling 1:1 campaigns after its IPO without losing the personal touch: more than 2,000 accounts, run by an ABM team of six.

Approach:

• Account Identification: The ABM team starts from what Sales already knows about an account and uses intent data to find the non-branded topics that cold accounts are researching. It only pursues accounts it knows something about.
• Personalized 1:1 Pages: Ad clicks land on account-specific pages with persona-based messaging and calls to action matched to the sales stage. Most pages include a note and photo from the account’s sales rep.
• Webinars and Events: Field marketing and ABM build 1:1 events for must-win accounts and drive registration among key personas.
• SDR “1-2 punch”: ABM warms an account for two to four weeks, then an SDR engages directly and passes the opportunity to an AE. SDRs also use e-gifts to reach lower levels of an account.

Results:

• 75% increase in SDR-booked meetings in ABM accounts, quarter over quarter.
  38% increase in ABM accounts with SDR meetings booked.
  3x increase in meeting rate in 1:1 accounts where SDRs were closely aligned with ABM.

Best Practice: SDRs and ABM on the same accounts, and pair personalized pages with a human touch from the account’s rep.

(Snowflake case study, published by AdRoll)

Case Study 4: Adobe : Marketing and Sales Alignment on LinkedIn

ABM type: 1:many (target-account paid social)

Objective: Adobe Experience Cloud’s marketing and sales teams worked in silos, which wasted spend on accounts that sales did not care about.

Approach:

• Shared Account Focus: Marketing and sales built one ABM framework on LinkedIn, so paid reach and Sales Navigator outreach targeted the same accounts.
• Collaborative Campaigns: Sales and marketing worked hand-in-hand to create hyper-relevant messaging.
• Targeted Social Media: Adobe utilized LinkedIn to reach decision-makers and influencers across the whole buying committee.

Results:

• 42% of closed-won deals were influenced by marketing on LinkedIn, and 55% by sales activity powered by Sales Navigator (2018 data).
• Improved alignment between sales and marketing teams.
• Deals influenced by LinkedIn were 161% bigger on average than those that were not (2018 data).

Best Practice: Use a shared account list and shared channels to create seamless collaboration between sales and marketing.

(Adobe customer story, published by LinkedIn)

Case Study 5: Wilmington Paper’s 68% Pipeline Boost with SalesIntel

ABM type: 1:many · Timeframe: case study published September 2025

Objective: Wilmington Paper, a provider of customized recycling management solutions, needed a dependable way to find the right companies and decision-makers for marketing campaigns. Its outreach relied on static lists, local prospecting, referrals and event networking.

Approach:

• High-Accuracy Audience Targeting: 95% human-verified contact data, with direct dials and verified emails.
• List Building at Scale: AI-assisted search and advanced filters produced campaign-ready lists quickly.
• Seamless Sales Handoff: Enriched, pre-qualified contacts went to sales for immediate follow-up.

Results: Within the first several months

• 68% increase in net-new pipeline from marketing-sourced campaigns.
• 42% improvement in connection rates for leads passed to sales.
• 7x ROI based on opportunities influenced by SalesIntel data.
• 30% faster campaign launch times.

Best Practice: Make audience building fast and repeatable, so marketing can launch targeted campaigns while accounts are still in-market.

For the full story: Wilmington Paper Increases Pipeline by 68% with SalesIntel

Case Study 6: A Signal-Triggered ABM Play with SalesIntel (Playbook)

ABM type: 1:few to 1:many, with 1:1 for top-tier accounts

This is an illustrative playbook built from SalesIntel’s current capabilities, not a customer result. It shows how the pieces fit when ABM starts from signals.

• Define the audience: ICP Intelligence analyzes your best customers and surfaces lookalike accounts.
  Watch for in-market behavior. Signal Intelligence tracks 30+ signal categories and 60,000+ intent topics (Bombora and PredictiveIntent) across those accounts.
• Trigger the play: When an account shows an intent surge, a funding round, a leadership change or a visit that Web Visitor Identification catches, a GTMCanvas workflow qualifies it against your ICP, maps the buying committee and drafts role-based messaging.
• Engage across channels: The Digital Audience Solution serves ads to the account while the sales engagement platform sequences the committee and tasks land in your CRM.
• Measure: Pipeline attribution connects signals and campaigns to pipeline and revenue.

Best Practice: Let signals decide when to engage, and let people decide what to say and which accounts get a 1:1 treatment.

ABM in the AI and Signals Era

The biggest change since these campaigns ran is how accounts get chosen and when they get contacted. Demand Gen Report’s 2026 ABM benchmark found that nearly 80% of surveyed organizations are actively running ABM, that 29% name content personalization at scale as the top AI use case, and that 47% rate personalized content as the highest-ROI ABM tactic. (Demand Gen Report)

Four shifts are worth building into your next campaign:

• From static lists to signal-triggered accounts: Growth signals show which accounts are heading into a buying cycle, and intent topics confirm which are in one. PredictiveIntent filters out illogical matches, so your team sees only signals worth acting on.
• From manual follow-up to no-code workflows: GTMCanvas turns a detected signal into a qualified account, a mapped buying committee and role-based messaging, with tasks pushed to your CRM and outreach triggered automatically or handed to reps.
• From AI-drafted to data-verified: AI personalization is only as good as the data behind it. Human-verified contact data keeps AI-assisted outreach reaching the right person.
• From campaign reports to pipeline attribution: Measure the path from signal to meeting to pipeline, not just clicks and impressions.

To see how this works in practice, explore Buying Signals and GTMCanvas, or watch our webinar on turning missed social signals into pipeline with an AI workflow.

Concluding With Key ABM Best Practices

1. Start Small, Then Scale Strategically

Begin with a focused group of high-value accounts to test messaging, channels, and engagement strategies before expanding your program. Use the results to identify what works, then standardize successful processes with reusable templates and workflows.

Example: Snowflake scaled its 1:1 ABM program to more than 2,000 accounts with a six-person ABM team by standardizing page templates, project plans, and SDR handoffs.

2. Build Campaigns on Reliable Data and Technolog

Accurate account and contact data helps marketing teams identify the right companies, reach relevant decision-makers, and personalize campaigns with confidence. Combine reliable data with intent signals, visitor identification, and automated workflows to turn account activity into timely follow-up.

Example: BetterCloud consolidated multiple data vendors with SalesIntel, saving more than $100,000 compared with its previous ZoomInfo arrangement. SalesIntel’s Web Visitor Identification can also help teams identify companies visiting their websites and route them to appropriate marketing or sales follow-up.

3. Align Sales and Marketing Around Shared Account Priorities

Sales and marketing teams should work from the same target-account list, ICP definitions, buying signals, and campaign goals. Clear ownership and coordinated handoffs help ensure that sales engagement follows marketing activity at the right time.

Example: Snowflake warmed target accounts through ABM campaigns for two to four weeks before SDRs engaged directly. Accounts with closely aligned SDR and ABM activity achieved a 3x meeting rate in 1:1 programs.

4. Personalize Engagement Around Account Needs and Buying Signals

Effective ABM goes beyond adding a company name to an email. Use industry context, buyer roles, relevant pain points, and account-level signals to tailor messaging across ads, landing pages, emails, and events. Buying signals and intent data help teams prioritize what to say and when to engage.

Example: Snowflake used account-specific landing pages with persona-based messaging and notes from the account’s sales representative. Demand Gen Report’s 2026 ABM benchmark also found that 47% of surveyed marketers identified personalized content as their highest-ROI ABM tactic.

5. Coordinate Multiple Channels Into One Cohesive Campaign

Combine digital channels, such as personalized advertising and email, with appropriate offline tactics, including direct mail and events. Each touchpoint should reinforce the same account-level message rather than operate as an isolated activity.

Example: Snowflake combined AdRoll ABM and LinkedIn advertising with SDR e-gifts and tailored events for high-priority accounts. SalesIntel’s firmographic, technographic, contact, and buying-signal data can help teams identify relevant audiences and coordinate outreach across channels.

6. Measure Account-Level Progress and Pipeline Impact

Measure ABM performance beyond clicks, impressions, and individual lead conversions. Track account engagement, buying-committee coverage, meetings booked, pipeline created, win rates, deal sizes, and sales-cycle progress. Connect these outcomes to campaigns and buying signals to understand which activities contribute to revenue.

Example: Snowflake tracked anonymous account engagement, account identification, and the progression of sales touches into meetings and opportunities. Wilmington Paper reported a 68% increase in net-new pipeline from marketing-sourced campaigns after implementing SalesIntel’s data and list-building capabilities.

Turn ABM Best Practices Into a Repeatable Growth Strategy

The strongest ABM programs combine precise targeting, reliable data, timely engagement, and measurable outcomes. Start with accounts that match your ideal customer profile, use buying signals to prioritize in-market opportunities, and coordinate marketing and sales around a shared plan.

SalesIntel brings these capabilities together through ICP Intelligence to identify the right accounts, Signal Intelligence to understand when and why to engage, and GTMCanvas to automate workflows that connect account signals with buying-committee activation.

See how ICP Intelligence, Signal Intelligence and GTMCanvas work together to turn buying signals into pipeline.

Frequently Asked Questions

What are the main types of ABM campaigns, and which one did each example use?

There are three: 1:1 (a custom campaign for one account), 1:few (one campaign for a cluster of similar accounts) and 1:many (a personalized campaign across a larger ICP list). Snowflake ran 1:1 campaigns at scale, SundaySky a 1:few program, and BetterCloud, Adobe and Wilmington Paper 1:many programs. The signal-triggered playbook above spans all three.

Can mid-market companies run ABM campaigns like Snowflake, or Adobe?

Yes. Scale the approach to your team rather than copying it. Start with 1:few and 1:many campaigns, use ICP lookalike modeling and buying signals to keep the account list short, and use templates and no-code workflows instead of custom builds. Snowflake’s ABM team of six ran 2,000+ 1:1 campaigns by relying on page templates and tooling. For a step-by-step approach, see How to Build an ICP Model for Mid-Market ABM.

How long does it take for an ABM campaign to show results?

Engagement signals, such as more target accounts visiting and more contacts responding, usually appear within the first 30 to 90 days. Pipeline typically follows over one to two sales cycles, so judge revenue impact over six to twelve months for most B2B deals. Wilmington Paper reported measurable pipeline and execution gains within its first several months.

What early signs show an ABM campaign is working?

Look for leading indicators before revenue shows up:

• More anonymous target accounts engaging, and more of them becoming known (Snowflake tracks both).
• More contacts engaged per account, covering more of the buying committee.
• Repeat visits to key pages from target accounts, caught by Web Visitor Identification.
• A higher share of target accounts with meetings booked (Snowflake saw a 38% increase in ABM accounts with SDR meetings).

How do you personalize ABM campaigns without building custom assets for every account?

Personalize in layers. Build modular content by industry, persona and use case, then swap in account-specific details such as the account name, a relevant signal or a note from the account’s rep. Snowflake runs one page template and updates messaging across thousands of campaigns with tooling. Signals tell you which message to send, and AI-assisted drafting with human review speeds up the writing: 29% of marketers in Demand Gen Report’s 2026 survey named content personalization at scale as AI’s top ABM use case. GTMCanvas can generate role-based messaging from signals and personas.

How do you prove ABM campaign ROI to leadership?

Report on accounts, not leads. Compare pipeline, win rate, deal size and sales cycle for target accounts against a non-ABM baseline or control group, then connect the full path from engagement to meetings, opportunities and wins, as Snowflake does. Pipeline attribution links signals and campaigns to revenue, so you can show which plays moved which accounts. Present a simple view for executives: revenue influenced, cost per opportunity and ROI by campaign type.

What should an ABM campaign brief include?

A one-page brief keeps sales and marketing aligned. Include:

• Objective and success metrics (pipeline, meetings, win rate).
• Target accounts and their type (1:1, 1:few or 1:many), and how they were chosen: ICP fit plus buying signals.
• Buying committee roles and the pain point for each.
• Core message and proof points by persona or cluster.
• Channels, cadence and assets: ads, email, direct mail, events.
• Sales plays and SDR hand-off rules, such as how long ABM warms an account before an SDR engages.
• Budget, timeline and owners.
• Measurement plan and review cadence.