Account Based Marketing Data: 2025 Trends & Insights

Major Takeaways: Account Based Marketing Data

ABM Is Now a Data-First Strategy

ABM in 2025 runs on firmographic, technographic, and behavioral data, with over 70% of companies using dedicated ABM platforms to manage it.

Intent Data Unlocks High-Value Targets

Only 5% of B2B buyers are in-market at any time—intent data helps identify and prioritize those accounts early for higher win rates and faster outreach.

AI Supercharges ABM Decision-Making

84% of marketers use AI to enhance ABM personalization, with predictive models increasing conversion rates by 22% across key accounts.

Hyper-Personalization Increases Conversions

Personalized messaging and account-specific content drive up to 20% more engagement and 10–15% higher conversion rates in targeted ABM campaigns.

Omnichannel Engagement Drives Higher ROI

Multichannel ABM strategies improve engagement by 72% and coordinate efforts across email, LinkedIn, calls, and ads for a seamless account experience.

Analytics Prove ABM’s Pipeline Impact

84% of companies see measurable pipeline growth from ABM, with improved deal size, win rates, and velocity tracked through account-level metrics.

Data Quality and Integration Remain Critical

40% of marketers cite poor data hygiene as a barrier—clean, integrated data is essential to activating ABM insights and measuring outcomes effectively.

Strategic ABM Requires the Right Skills and Partners

Internal expertise gaps challenge 40% of teams; outsourcing to skilled ABM partners can increase ROI by 72% and improve execution across tiers.

Introduction

Account-Based Marketing (ABM) has evolved from a niche tactic into a mainstream B2B strategy, and data is at the heart of this revolution. By 2025, ABM is more data-driven than ever – powering highly targeted campaigns that deliver impressive returns. In fact, ABM adoption is widespread (about 70% of marketers have an active ABM program in place) and it’s renowned for ROI (around 97% of marketers say ABM yields higher ROI than other marketing strategies) (4). But what exactly is fueling this success? The answer lies in how organizations harness account based marketing data – from intent signals to AI analytics – to zero in on the right accounts with the right message at the right time.

In this comprehensive report, we’ll explore the state of ABM data in 2025, examining key trends and offering strategic insights for B2B marketing and sales leaders. We’ll cover how ABM intent data is identifying in-market buyers, how AI and predictive analytics are elevating ABM, the rise of data-driven personalization and omnichannel engagement, and the importance of ABM analytics and data quality. Each section includes compelling statistics and practical takeaways you can use to inform your ABM strategy. Let’s dive into the data-powered trends shaping ABM in 2025.

Understanding the Evolving ABM Data Landscape in 2025

Companies implementing ABM have seen revenue increases of over 200% when strategies are executed effectively.
Reference Source: Revnew

ABM’s growth in recent years has been nothing short of remarkable. Companies are not only embracing ABM but also investing significant resources into it. On average, 29% of marketing budgets are now dedicated to ABM initiatives (4), reflecting its priority in B2B strategy. This investment is paying off: businesses implementing ABM have seen substantial uplifts in results – one analysis found revenue can increase by over 200% when ABM strategies are executed effectively (4). ABM’s ability to focus on high-value accounts with personalized outreach drives bigger deals and faster revenue growth than traditional broad-based marketing.

Data is the key enabler of ABM’s precision and impact. ABM turns the marketing and lead generation funnel on its head by starting with specific target accounts, and success hinges on rich data about those accounts. Modern ABM programs leverage a wide array of data types to pinpoint the best targets and tailor engagement:

Firmographic Data: Company attributes such as industry, size, and revenue, used to identify high-value firms that match your ideal customer profile.
Technographic Data: Insights into the technology stack and tools a target account uses, helping you tailor pitches that align with their existing systems.
Demographic & Role Data: Information on key decision-makers’ job titles, roles, and locations, allowing personalization that resonates with specific personas.
Behavioral & Engagement Data: The actions individuals at the account take – website visits, content downloads, email opens, event attendance – indicating their level of interest and engagement.
Intent Data: Signals that an account is actively researching or showing intent to purchase a solution in your category (e.g. frequent searches, reading relevant content).

Businesses in 2025 are increasingly adept at gathering and integrating these data points into their ABM platforms. Not surprisingly, 72% of companies use a dedicated ABM platform to manage account data and outbound campaigns (2). Marketing automation is also ubiquitous in ABM: roughly 71% of ABM marketers employ marketing automation tools to scale their personalized campaigns efficiently (4). By unifying data from CRM, marketing automation, intent providers, and other sources, teams can get a 360° view of target accounts. This comprehensive data foundation is what enables the next-level targeting and personalization that make ABM so powerful.

Key Insight: ABM is fundamentally data-driven. Ensure your team is aggregating diverse data about target accounts – firmographics, technographics, intent signals, and engagement metrics. A solid data infrastructure (ABM platform or integrations between CRM, sales engagement, and analytics tools) is essential to turn raw data into actionable insight. The more connected and accessible your ABM data, the more precisely you can focus your resources on the accounts most likely to drive revenue.

ABM Intent Data: Identifying In-Market Accounts

Only 5% of B2B buyers are actively in-market at any given time, making intent data essential to pinpoint sales-ready accounts.
Reference Source: LinkedIn Marketing Solutions

One of the most game-changing developments in ABM is the rise of intent data. In traditional demand gen, you might cast a wide net and hope to snag a prospect who’s ready to buy. In contrast, ABM intent data lets you zero in on accounts actively researching or showing buying signals, so you can engage them before your competitors do. Given that, at any given time, only around 5% of B2B buyers are actually “in-market” for a purchase (5), the ability to spot those buyers is incredibly valuable.

So, what is ABM intent data exactly? It’s information collected about a target account’s online behavior that indicates potential intent to purchase. This could include spikes in research on specific topics, frequent visits to certain product pages, content consumption patterns, or engagement with competitor sites. Sources of intent data range from first-party (e.g. tracking which whitepapers a prospect downloaded on your own website) to third-party (e.g. a data provider like Bombora or ZoomInfo monitoring broader web activity). By aggregating these signals, intent data providers can flag accounts that are showing interest in solutions like yours.

And marketers are eagerly embracing this capability: 91% of B2B tech marketers now use intent data to prioritize accounts and build target lead lists for ABM (2). With intent data, your sales and marketing teams can prioritize outreach to accounts when they’re actively in a buying cycle. For example, if an account surges in intent around “network security solutions,” the vendor in that space who acts on that insight first – with a well-timed call or personalized email offering help – gains a huge advantage in winning the deal.

Why is intent data so critical in 2025? The B2B buying process has become more self-driven and anonymous; buyers do a ton of research online before ever talking to a vendor. Intent data shines a light on this dark funnel. It helps answer questions like: Which target accounts are researching our solution right now? What topics are they interested in? Which competitors’ content have they engaged with? Armed with these insights, ABM teams can craft hyper-relevant messaging and reach out at the optimal moment.

Consider these strategic benefits of leveraging ABM intent data:

Focus on Hot Prospects: Rather than guessing, you can focus your marketing and SDR outreach on accounts that are exhibiting purchase intent right now, boosting efficiency.
Personalized Messaging: Knowing an account’s specific interests or pain points (e.g. they’ve been reading about cloud compliance) allows you to tailor your content and talks tracks directly to those topics.
Shorter Sales Cycles: Engaging accounts earlier in their buying journey can significantly speed up sales cycles. Companies aligning ABM with intent-driven advertising, for instance, see higher win rates (up to 60% higher) by being first to connect with in-market buyers (7).
Sales & Marketing Alignment: Intent data is a tool both teams can rally around. Marketing can feed sales with “here’s who’s heating up this week,” and sales can give immediate feedback on quality – fostering tighter alignment on target accounts.

The importance of intent targeting is underscored by the 95-5 rule – about 95% of your potential B2B buyers aren’t ready to buy at a given moment, and only 5% are in-market (5). ABM intent data helps you find that crucial 5% and engage them proactively. It’s no wonder that identifying ready-to-buy accounts via intent signals was cited as a top ABM trend for 2025 (3).

Best Practices for Using ABM Intent Data:

Integrate Intent Signals into B2B Lead Scoring: Blend intent data with your account scoring model. For example, assign high scores to accounts visiting competitor comparison pages or searching key industry terms, so these accounts get flagged for fast-track sales outreach.
Act Quickly on Insights: Intent data has a half-life – a window of opportunity when interest is peaking. Establish processes for your sales team to get intent-triggered alerts (e.g. daily or real-time notifications) and reach out within days (or hours) of an intent spike.
Personalize Outreach with Context: When you contact an account showing intent, reference what you know. If you see they’re researching “CRM automation,” your outreach could offer a case study on that topic or simply ask if they’re looking to improve their CRM – demonstrating you understand their interest without being invasive.
Combine Intent with Other Data: Intent signals are powerful, but even more so when combined with other data. Cross-reference intent-qualified accounts against firmographics (do they fit our ICP?) and engagement data (have they interacted with our content?) to refine your target list to the most sales-ready and high-fit accounts.

AI and Predictive Analytics Elevate ABM Data Insights

84% of marketers now use AI and intent data to enhance ABM personalization strategies.
Reference Source: G2 Learning Hub

Artificial intelligence (AI) and machine learning have become integral to making sense of ABM data in 2025. With so much data flowing in – intent signals, technographics, content engagement, CRM data – no human could manually analyze it all in real-time. That’s where AI and predictive analytics step in, spotting patterns and opportunities that would otherwise be missed. The result is smarter ABM programs that can predict which accounts are likely to convert, what they care about, and even the best actions to take next.

The adoption of AI in ABM has surged. By 2025, 84% of marketers report leveraging AI and intent data to enhance personalization in their ABM campaigns (1). Moreover, businesses that integrate AI prospecting tools into ABM are reaping rewards – 79% of companies say using AI in their ABM strategy has increased revenue (2). These numbers reflect an important reality: AI isn’t just a buzzword for ABM, it’s becoming a competitive necessity for processing data at scale and improving outcomes.

How AI is applied in ABM data analytics:

Predictive Account Scoring: AI models analyze historical data to predict which target accounts are most likely to engage or convert. They can weigh thousands of data points (company attributes, web behavior, email engagement, etc.) and output an “account propensity score.” This helps sales prioritize outreach to accounts with the highest probability of closing.
Intent Forecasting: Beyond just detecting intent signals, AI can forecast buying intent by comparing an account’s behavior against patterns of past won deals. For example, machine learning might identify that a sequence of visits to certain webpages + attendance at a webinar + an uptick in a certain intent topic = a high likelihood of being in-market. This allows you to proactively engage accounts before they raise their hand.
Personalization at Scale: AI drives real-time personalization by dynamically selecting which content or message to show each account based on their profile and behavior. Think of website personalization: an ABM visitor from the finance industry might automatically see case studies from your finance clients, whereas a tech industry visitor sees something different – all decided by AI in the background analyzing their firmographic data. This level of personalization was onerous to implement manually, but AI makes it scalable across hundreds of accounts.
Chatbots and Conversational AI: Many ABM teams deploy AI chatbots on their site targeted to key accounts. These bots, informed by account data, can greet a known target account visitor by company name and guide them to relevant info or even alert the sales rep in real time. It’s a data-driven way to ensure a VIP account never browses your site anonymously.
Predictive Content Recommendations: Just as B2C platforms recommend products, B2B marketers are using AI to recommend the next best content or touchpoint for an account. If data shows an account user consumed X, the AI might suggest Y as the next content piece likely to move them along the journey.

The benefits of predictive analytics in ABM can be dramatic. For instance, companies using predictive analytics to inform ABM have seen significant lifts in conversion – one benchmark found a 22% average increase in conversion rates for sessions influenced by predictive intelligence (3). By anticipating customer needs and behavior, you engage accounts with the right tactic before they lose interest.

Crucially, AI in ABM isn’t about removing the human element – it’s about augmented intelligence. The best ABM teams combine AI-driven insights with human judgment. The data might predict an account is likely to be interested in product A; marketing and sales then use that insight to craft a compelling, creative campaign for that account. In other words, AI crunches the data and serves up recommendations, but people still craft the narrative and build the relationship.

Best Practices for AI-Driven ABM:

Start with Quality Data: AI is only as good as the data fed into it. Ensure your account data is rich and clean. This means updating databases, enriching missing fields, and integrating intent/vendor data into one view. (Remember the adage: garbage in, garbage out.)
Use AI for Specific Use Cases: Identify where AI can have quick impact – e.g. lead/account scoring or personalized web content. Pilot one use case, show results, then expand. For example, implement an AI-based account scoring tool and measure improvement in SDR and BDR efficiency.
Train the Team: Help your marketing and sales team understand AI-driven scores or recommendations. If a predictive model flags an account as “high likelihood to buy,” the team should know how that conclusion was reached (at least generally) and how to appropriately act on it. Build trust in the AI by sharing success stories internally.
Continuously Refine Models: Monitor the outcomes of AI predictions. If certain accounts flagged by AI didn’t convert, analyze why and adjust the model or input data. Many ABM platforms with AI will retrain themselves over time – make sure to periodically review the criteria and performance.
Maintain the Human Touch: Use AI to make your team smarter and faster, but don’t lose the personal, human aspect that ABM is known for. An AI might draft an email template for you – but you should still customize it further for that account and add genuine human warmth.

Data-Driven Personalization in ABM: Hyper-Personalization at Scale

Hyper-personalized messaging in ABM campaigns drives up to a 20% increase in engagement rates and 10–15% boost in conversion rates.
Reference Source: The CMO

Personalization has always been a cornerstone of ABM. In 2025, however, we’re seeing hyper-personalization reach new heights, fueled by the rich data and analytics at marketers’ fingertips. Hyper-personalization means going beyond just using a prospect’s name in an email – it’s about tailoring content, messaging, and even product offerings to the unique context of each account (and key contacts within that account). The payoff is big: personalized ABM, lead generation campaigns break through the noise and deeply engage target buyers, leading to higher conversion and stronger relationships.

ABM practitioners clearly recognize this, as 80% of B2B companies report leveraging hyper-personalization in their ABM strategies (1). By using detailed account insights, these companies are creating content and experiences that feel hand-crafted for each target account. And it works – personalized outreach delivers measurable uplift. Statistics show that personalized messaging can drive a ~20% increase in engagement rates and a 10-15% boost in conversion rates in ABM campaigns (1). When an account feels like you truly understand their business and pain points, they’re far more likely to respond to your outreach and ultimately become a customer.

What does hyper-personalization in ABM look like in practice? Here are a few examples and tactics that data enables:

Account-Specific Content: Marketers are crafting assets (case studies, sales pitch decks, micro-sites) for one specific account. For instance, an ABM team might build a custom landing page that addresses <Client Name>’s 3 Biggest Supply Chain Challenges with content tailored to that client’s industry and even referencing their company strategy. Data from research and past interactions informs exactly what pain points to address. This level of personalization shows the account that you’ve done your homework like no one else has.
Role-Based Personalization: Within a target account, different stakeholders care about different things. ABM data can help segment by persona (e.g. CTO vs. CFO) and customize messages accordingly. The CTO at a target client might receive a technical whitepaper and an invitation to a technical workshop, while the CFO gets a business case infographic highlighting ROI – each touch aligned to their priorities.
Dynamic Ad and Email Content: Using marketing automation and ABM ad platforms, teams deliver dynamic content insertion based on account attributes. For example, an email template might automatically populate industry-specific messaging or swap in an example of a customer success story from the same sector as the recipient. Account-based display ads can show industry-relevant imagery or mention the target account’s name or industry in the ad copy to grab attention.
Customized Product Demos: Some ABM programs go so far as to create personalized demo environments or videos for each target client. Data on the account’s use case and needs guides the creation of a demo that feels like the product was built for them. This might involve showing how the product would integrate into the account’s existing tech stack (leveraging technographic data) or using the account’s own terminology in the demo scenarios.
One-to-Few Personalization: Even in one-to-few ABM (targeting a cluster of similar accounts), hyper-personalization can occur by segment. For instance, an ABM campaign aimed at 5 healthcare companies might use common healthcare industry data and themes – deeply relevant to that segment if not individually tailored to each company. This is more scalable than one-to-one, but still far more personalized than generic marketing.

All these efforts are powered by data. Without deep insights into each account’s industry, challenges, and behaviors, you wouldn’t know how to personalize in a meaningful way. In 2025, ABM teams have more data than ever (from social media, intent providers, customer research, etc.) to feed into their personalization engines.

A vast majority of ABM practitioners are convinced of personalization’s value. In one survey, 72% of marketers reported a substantial boost in customer engagement after implementing ABM strategies, attributing much of that success to more tailored, relevant outreach (2). Engagement is a precursor to conversion – if your target contacts actively engage with your content because it speaks to them, you’ve won half the battle.

Best Practices for Hyper-Personalization:

Develop Account Profiles: Build a “dossier” on each target account that synthesizes all key info – firmographics, recent news, pain points, technologies, past engagement, etc. This profile (often stored in your CRM or ABM platform) is the reference for anyone creating personalized content for that account. Update it continuously as new intel comes in.
Modular Content Strategy: Create modular content pieces that can be mixed and matched for personalization. For example, have a library of industry-specific case studies, persona-specific value propositions, and modular product feature highlights. Then, for each account, assemble the most relevant pieces. This way you’re not starting from scratch every time, but the end result still feels highly customized.
Use Data to Personalize Timing: Personalization isn’t only what you say, but when you say it. Use engagement data to time your outreach. If a target account contact has just engaged with a piece of content on your site, that could trigger a personalized follow-up email referencing that content. Or if you know their contract renewal or budget planning season is coming up (data often tracked in CRM), time a bespoke offer or meeting request accordingly.
Don’t Overlook Small Personal Touches: Small details can have big impact – like hand-written notes or bespoke gifts tailored to an account’s interests. Data can inform this too (e.g., noticing a LinkedIn post where the target mentions a favorite sports team – sending a team swag with a note can make a memorable impression). These don’t scale to hundreds of accounts, but for your top-tier ABM accounts, such gestures deepen relationships.
Measure Engagement and Iterate: Track how each account responds to your personalized tactics. Which emails did they click? What content did they spend time on? Use these engagement metrics to refine your personalization. If certain messaging isn’t hitting the mark, adjust your approach for that account or similar accounts. ABM is iterative – data should continuously inform how you tweak the personalization for better resonance.

Omnichannel ABM: Data-Driven, Multi-Channel Engagement

72% of marketers report improved customer engagement after implementing multi-channel ABM strategies.
Reference Source: The CMO

In 2025, successful ABM is inherently omnichannel. Relying on just one or two channels (say, only email or only LinkedIn ads) is not enough to break through to busy decision-makers. Instead, ABM campaigns coordinate touchpoints across email, phone calls, LinkedIn and other social platforms, targeted digital ads, content marketing, events/webinars, and more – all orchestrated in a cohesive way. The glue that holds this together is data: a unified view of account interactions that allows each channel to inform the others.

A data-driven omnichannel marketing approach ensures that no matter where a target account interacts – whether they open an email, see a display ad, or talk to a sales rep – the messaging is consistent and informed by their latest engagement. This strategy has proven highly effective. Marketers report significantly improved results when deploying multi-channel ABM: about 72% of marketers saw improved customer engagement after implementing multi-channel ABM strategies (1). More engaged accounts mean more opportunities for sales.

How ABM data enables omnichannel coordination:

Unified Account Tracking: Modern ABM platforms and CRM systems compile interactions from all channels into one timeline per account. For example, it will log that Company X: visited your website, clicked your LinkedIn ad, and replied to an email – all in one place. This unified data lets the team plan next touches intelligently. If an account is ignoring emails but engaging on LinkedIn, you might shift focus to LinkedIn messaging for that account.
Sequenced Outreach Cadences: Sales development representatives (SDRs) often execute orchestrated cadences that include a mix of emails, calls, LinkedIn InMails, etc. Data dictates the sequence – e.g., an SDR might call two days after an email open, or send a LinkedIn message referencing a webinar the prospect just attended. The cadence can automatically adjust based on what the account does.
Consistent Messaging Across Channels: Data insights (like pain points or intent topics) are used to craft a core message that then gets adapted to each channel. Say your data indicates a target account is very interested in “cloud security compliance”. Your LinkedIn ads, email subject lines, and voicemail and call scripts can all revolve around that topic. The channel format changes, but the value proposition stays coherent. Consistency reinforces your story each time the account sees it.
Channel Preference Insights: Data might reveal that certain accounts or personas respond better on certain channels. Maybe engineers from Account A tend to engage with technical blog content (content marketing), whereas executives from Account B prefer webinars or white-glove events. By analyzing engagement data, you can double down on the channels that work for each account. This personalized channel mix is a form of data-driven optimization.
Retargeting and Sequence Suppression: On the backend, data ensures you’re not overloading or contradicting across channels. For example, once an account schedules a meeting (success from an email), your system can automatically pause other outreach like ads or sales calls to avoid redundancy. Similarly, if a key contact goes dark, data might trigger adding them to a retargeting ad audience to keep nurturing them passively. All of this prevents “one hand not knowing what the other is doing” in your outreach.

A prime example of omnichannel ABM success is leveraging LinkedIn alongside other channels. LinkedIn has emerged as a powerhouse for ABM due to its B2B targeting capabilities. Roughly 79% of B2B marketers consider LinkedIn the most effective platform for generating high-quality business leads in ABM (1). Using LinkedIn data (like job role, company, industry) combined with your own intent data, you can serve very tailored ads or direct messages to the exact people you need to reach at an account. When those LinkedIn touches are coordinated with, say, an email or a direct mail piece, the account experiences a cohesive narrative.

Aligning sales and marketing around an omnichannel ABM approach can dramatically improve results. Companies with strong ABM alignment across teams attribute 73% of their total revenue to ABM efforts, indicating that when everyone works together across channels, ABM becomes a primary revenue driver (4). It’s truly a team sport: marketing, SDRs, and sales all executing data-informed touches in concert.

Best Practices for Omnichannel ABM:

Build a Multichannel Playbook: For each tier of ABM accounts, define a playbook that outlines which channels to use and in what sequence. For example, for high-value Tier 1 accounts: Week 1 send personalized direct mail package, Week 2 email follow up and LinkedIn connect, Week 3 target with ads + phone call from sales, etc. Use data to customize this over time per account, but have a baseline plan.
Ensure Messaging Alignment: Create a core messaging document per campaign or account that all teams and channels draw from. This prevents a scenario where, say, the ads say one thing but the sales rep is pitching something different. Consistency builds credibility. A shared content repository accessible to both marketing and sales is useful here.
Use Technology for Orchestration: Take advantage of ABM tools that help with orchestration. There are platforms that will manage account-based ad campaigns, send alerts to reps when engagement hits a threshold, or even automate social touches. For example, solutions that integrate with CRM can automatically enroll engaged contacts into LinkedIn Matched Audiences for ad retargeting. Leverage these to reduce manual coordination.
Monitor Channel Engagement and Pivot: Track which channels are yielding responses for each account. If after a few touches you see zero engagement on one channel, try a different one or a different message. Data might show, for instance, that Account Y isn’t responding to emails, but someone from the account clicked an ad. That’s a signal to perhaps use that ad’s content in an email follow-up or have sales reference it in a call.
Frequency and Cadence Control: Respect your targets by managing frequency. Omnichannel doesn’t mean bombard-from-all-sides. Use data to gauge saturation – e.g., if a contact just engaged with one channel today, maybe pause another outreach to avoid annoyance. Many ABM teams use rules like “no more than X touches per week across all channels” for a given contact. Quality and timing trump sheer volume of touches.

ABM Analytics: Measuring What Matters and Optimizing Strategy

84% of companies report seeing pipeline growth as a direct result of their ABM programs.
Reference Source: G2 Learning Hub

As ABM programs mature, a crucial question arises: How do we measure success? Traditional marketing metrics (like lead volume or generic web traffic) don’t fully capture ABM’s impact, because ABM is account-centric and long-term. This is where ABM analytics comes into play – the practice of lead tracking and analyzing account-level metrics and lead generation KPIs that align with ABM goals. In 2025, organizations are getting far more sophisticated in ABM analytics, though it remains a work in progress for many.

The importance of ABM analytics is clear from a leadership perspective. ABM often requires significant budget and effort, so CMOs and CROs want to see tangible results: Are these targeted campaigns actually driving bigger deals and more revenue? The good news: various studies confirm ABM’s positive impact on pipeline management and revenue. For example, 84% of companies report seeing pipeline growth as a result of their ABM strategies, and companies with aligned, data-driven ABM have attributed a large portion of new opportunities to those efforts. When measured properly, ABM often shines. One survey even found 79% of all opportunities were attributed to ABM efforts in companies with strong ABM programs (4).

However, measuring ABM is not without challenges. Many organizations are still building this muscle. Only about 52% of companies currently measure the ROI of their ABM programs (3) – meaning nearly half of ABM practitioners lack full visibility into performance. Part of the problem is choosing the right metrics and having the tools to track them. Unlike simple lead-gen, ABM might require custom dashboards and multi-touch attribution models to connect the dots from first engagement to closed deal across a buying committee.

Key metrics and analytics focus areas in ABM:

Account Engagement Score: Rather than individual lead scores, ABM looks at aggregate engagement per account. This can be a composite metric weighing things like number of contacts engaging, frequency of interactions, content consumed, meetings set, etc. A high engagement score signals that the account is warming up.
Pipeline Velocity & Stage Progression: ABM analytics track how target accounts move through the sales pipeline compared to non-ABM accounts. Are ABM accounts progressing faster from initial meeting to proposal? Data often shows they do – targeted accounts can move 234% faster through the pipeline in some cases (1). Monitoring stage-to-stage conversion rates by account allows you to identify bottlenecks or successes in your approach.
Average Deal Size and Revenue: A primary goal of ABM is to win larger deals. Metrics like average deal size for ABM-targeted deals vs. others are telling. Many ABM practitioners report significantly higher deal sizes – e.g., 58% of marketers observed deal size growth with ABM, with some seeing deal values 50%+ larger (1). Total revenue influenced by ABM campaigns is the ultimate bottom-line metric (some use uplift tests or attribution models to quantify this).
Account Retention and Expansion: ABM isn’t just for acquisition; it’s increasingly used for customer marketing too. Analytics should cover retention rates and expansion revenue (upsell/cross-sell) among accounts touched by ABM programs. Companies using ABM have noted improvements in retention by focusing on existing accounts’ needs – one stat showed a 20% improvement in customer retention with ABM engagement (1). Tracking churn vs. growth in ABM accounts vs. baseline can prove that value.
Sales and Marketing Alignment Indicators: This can include survey-style metrics (e.g., internal alignment score), but also data such as the frequency of sales-marketing interactions on ABM accounts or the usage of shared ABM dashboards. While “alignment” itself isn’t a numeric business metric, it’s strongly correlated with success. Notably, only 36% of companies running ABM consider their sales and marketing teams to be tightly aligned today (1), indicating a lot of room to improve – and analytics can highlight where disconnects might be (for instance, if marketing reports engagement that sales isn’t following up on, that’s an alignment gap).

Key Insight: ABM analytics often requires new lead generation tools and mindsets. A fully aligned sales and marketing team is vital – 93% of marketers say alignment is critical to ABM success (2) – because metrics like account engagement or multi-touch attribution cross departmental lines. The organizations excelling at ABM measurement typically have joint dashboards and regular interlock meetings to review ABM performance, ensuring everyone agrees on what “success” looks like and how it’s measured.

Challenges in ABM Measurement (and how to overcome them):

Attribution Complexity: ABM deals involve multiple touches and stakeholders, making attribution tricky. To tackle this, many teams use multi-touch attribution models or even build custom ABM attribution logic (like giving credit to the first meaningful touch that got the account engaged, and to the last touch before close). Marketing automation and BI tools can be set up to capture account-level attribution. It may not be perfect, but even directional attribution is better than none.
Data Silos: If sales logs meetings in one system and marketing logs engagement in another, measuring overall account progress is painful. Solve this by integrating systems or adopting an ABM platform that unifies data. Data cleanliness is part of this – note that 40% of marketers cite data quality (cleanliness) as a key challenge in ABM (2). Investing in data cleanup and integration yields more trustworthy analytics.
Defining the Right Sales KPIs: It’s important to distinguish activity metrics from impact metrics. For example, counting the number of account touchpoints is fine, but the real question is outcomes – like pipeline generated or conversion rate. Define a concise set of ABM KPIs that tie to business outcomes (e.g., pipeline $, win rate for ABM accounts, ACV, sales cycle length, retention %). Use activity metrics diagnostically, but report the outcome metrics to executives.
Measurement Frequency: ABM deals can have long sales cycles, so you might not see final outcomes for months. In the interim, use leading indicators (like meeting count, opportunity creation) to gauge if you’re on track. Many ABM teams have quarterly business reviews (QBRs) where they evaluate progress on target accounts – even if an account hasn’t closed yet, seeing pipeline movement or increased engagement quarter over quarter is a positive sign to report.
Benchmarking and Learning: If you’re new to ABM measurement, it helps to benchmark against yourself over time or against a control group. For instance, compare the win rate of ABM accounts to similar non-ABM accounts to quantify improvement. If win rate jumps from say 20% to 32% for ABM accounts, that’s a clear validation of your ABM approach (6). Document these learnings and iterate on both strategy and how you measure it.

In summary, robust ABM analytics closes the loop on your efforts – turning data into insight about what’s working and what’s not. It enables a cycle of continuous improvement: use analytics to spot which accounts or segments are lagging, investigate why (perhaps those accounts need different content or have data gaps), adjust your tactics, and then measure again. Over time, this data-driven optimization can dramatically increase the effectiveness of your ABM program, ensuring that resources are spent where they yield the highest return.

Overcoming ABM Data Challenges (Quality, Compliance, and Skills)

40% of marketers cite maintaining accurate, clean data as a major challenge in ABM execution.
Reference Source: G2 Learning Hub

While ABM data is incredibly powerful, it comes with its own set of challenges. B2B organizations often struggle with data quality issues, fragmented data sources, and ensuring compliance with privacy regulations – all of which can hinder ABM success if not addressed. Additionally, running a data-heavy ABM program requires certain skills and a strategic, long-term mindset that not every team may yet have. Let’s discuss these challenges and how to overcome them in 2025.

1. Data Quality and Accuracy: “Garbage data” can derail an ABM campaign. If your account information is outdated or incorrect (e.g., wrong contact roles, old technographic info, or duplicate entries), your personalization efforts might misfire or you might chase accounts that aren’t truly a fit. This is a widespread issue: about 40% of marketers say maintaining clean, accurate data is a major challenge in ABMl (2). The situation is exacerbated as data decays quickly – people change jobs, companies get acquired, etc.

Solution: Make data hygiene a continuous process. Implement regular data audits and enrichment cycles. For example, schedule quarterly CRM clean-ups where invalid contacts are purged or updated. Leverage third-party data services to enrich records with missing fields (like firmographics or new contacts at target accounts). Many companies now use automated tools that verify and update data in real-time (for instance, when a contact’s email hard-bounces, that triggers a check against LinkedIn to see if they left the company). A clean dataset instills confidence in your targeting and metrics.

2. Technology Integration: An ABM program might involve multiple tools – CRM, marketing automation, intent data dashboards, sales engagement platforms, analytics suites, etc. If these don’t talk to each other, you end up with siloed data and a fragmented view of the account. For ABM data to be actionable, systems need to be integrated. Yet, integrating tools (and getting teams to use them properly) can be complex.

Solution: Map out your ABM tech stack and identify integration gaps. Prioritize connecting core systems like CRM and marketing automation (so marketing activities reflect in CRM) and connecting sales outreach tools so that both sales and marketing see all touches. Many organizations are moving toward an “ABM command center” approach – whether that’s their CRM as the central hub or using an ABM platform that overlays all data. If integration in-house is tough, consider platforms that offer built-in integrations with common tools. Also, invest in training so that team members fully utilize the integrated features (e.g., sales knowing how to log activities or check intent data within CRM).

3. Privacy and Compliance: With the increasing focus on data privacy (GDPR in Europe, CCPA/CPRA in California, etc.), using data in marketing requires caution. ABM, especially when using third-party intent data or personal data like emails and LinkedIn profiles for outreach, must comply with relevant laws and regulations. The last thing you want is a GDPR violation while pursuing an EU account, for instance. Additionally, the move towards a cookieless world means some traditional tracking data might diminish, making first-party data even more critical.

Solution: Work closely with your legal/compliance team to ensure your ABM data usage is compliant. Obtain proper consents where required (for example, if you’re adding EU contacts to an email campaign, ensure they’ve opted in or meet legitimate interest criteria). Lean more on first-party data that prospects willingly share by engaging with your content, and use intent data providers that aggregate information in privacy-compliant ways. Also, consider account-based advertising techniques that use corporate IP targeting or geo-targeting instead of personal data, where appropriate. Essentially, be transparent and respectful in how you gather and use data – build trust, not just pipelines.

4. Skill Gaps and Internal Expertise: Running data-driven ABM requires a mix of skills – data analysis, tools expertise, content creation, sales coordination, etc. Not all teams have this full range in-house. In fact, 40% of marketers cited lack of internal expertise as a primary challenge in executing ABM (2). ABM is both an art and a science, and it can be demanding for lean teams to cover all bases (strategy, creative, tech, analytics) effectively.

Solution: Invest in your people and/or partners. This could mean upskilling your current team – sending marketers for ABM certification courses, training sales on using intent data, or hiring a data analyst to support ABM reporting. Many organizations also turn to external agencies or consultants specializing in ABM to fill gaps. Bringing in outside expertise can accelerate your success and transfer knowledge to your team. In fact, partnering with ABM experts often yields higher returns – companies that worked with a specialized ABM agency reported a 72% higher ROI from their ABM programs, compared to those managing it entirely in-house (1). Even if you don’t fully outsource ABM, external experts can help in specific areas like content personalization or campaign execution, relieving pressure on your team.

5. Patience and Long-Term Mindset: ABM is not a quick fix; it’s a strategic approach that plays out over months and years. Yet, marketers often face pressure for immediate results. About 23% of practitioners said short-term mindset (“pressure to prioritize quick wins over long-term ABM investment”) is a challenge in their organizations (2). If leadership expects huge numbers in quarter one of ABM, they might be disappointed and prematurely pull back support.

Solution: Set realistic expectations and communicate wins along the way. Educate stakeholders that ABM is about quality and lifetime value, not just quick volume. Use early metrics (like engagement or meetings) to demonstrate progress in lieu of revenue if sales cycles are long. Perhaps run a pilot ABM campaign and document its pipeline influence story to bring executives on board. Over time, showcase the strategic insights gained (for example, “ABM helped us identify a new market opportunity based on intent data trends”) in addition to deals closed. Building that internal case helps ensure ABM gets the runway it needs.

In navigating these challenges, the organizations that succeed treat ABM data as a strategic asset – protecting its quality, investing in the right tools and skills, and aligning the whole team around its proper use. It’s a journey, but overcoming these hurdles leads to a formidable competitive advantage: a marketing and sales engine that runs on high-octane data, delivering highly qualified pipeline and customers with maximized lifetime value.

Conclusion: Data-Driven ABM as the New Normal – Are You Ready?

The state of account-based marketing data in 2025 can be summed up in one word: empowering. Never before have B2B companies had such capabilities to pinpoint their ideal prospects, understand their needs through data, and engage them with tailored outreach strategies across multiple channels. The trends we’ve explored – from intent data and AI analytics to hyper-personalization and omnichannel orchestration – all point to a future where marketing and sales are more targeted, more timely, and more efficient. The numbers speak for themselves: organizations that fully embrace data-driven ABM are seeing tremendous payoffs, including faster sales cycles, larger deal sizes, higher engagement, and massive ROI improvements. In short, ABM data isn’t just a component of marketing – it’s become the engine of B2B growth.

But harnessing this power requires the right approach and sales agency. It’s not easy to build an ABM program that checks all these boxes (intent signals, AI models, personalized content, etc.) overnight. That’s where we can help. At Martal Group, we specialize in scaling outbound programs with an ABM mindset. Our team lives and breathes B2B data – we use real-time intent targeting to focus outreach on in-market prospects, ensuring your message lands when interest is highest. Through our tiered packages, you can ramp up from a pilot to a full-scale program at a pace that suits your resources and goals, all while maintaining cost efficiency.

Martal Group’s ABM-Driven Outbound Solutions: We offer a comprehensive suite of services to help you execute on the trends discussed in this post:

Outbound Lead Generation & Sales Outsourcing: Need more high-quality opportunities? Our seasoned team can act as an extension of your sales force, handling outbound prospecting and follow-ups with a data-driven approach. We identify and engage key accounts on your behalf, filling your pipeline with qualified leads.
Appointment Setting: We’ll not only find interested accounts – we’ll book meetings with the right decision-makers for your team. By leveraging intent data and personalized outreach, we secure appointments with prospects that truly fit your ideal customer profile, boosting your win rates.
B2B Cold Email & Cold Calling (with a Personal Touch): Our multi-channel outreach campaigns combine personalized cold emails with strategic cold calls. We craft messaging that resonates by using insights about each account, far from generic spam. The result? Prospects who actually want to talk to you.
LinkedIn Outreach & Social Selling: Given LinkedIn’s power in ABM, we handle targeted connection requests, messaging, and content engagement with your top accounts. This social touch often warms up accounts that might ignore other channels. We ensure your company stays on their radar in a professional, value-adding way.
Omnichannel Campaign Strategy: Martal’s approach is truly omnichannel – we integrate email, phone, LinkedIn, and even direct mail or gifting into a cohesive strategy. Every touch is informed by data (for example, we’ll see if a prospect clicked an email before calling them) to maximize relevance.
B2B Sales Training & Consultation: We don’t just generate meetings and leave you hanging. We provide B2B sales training to your team on how to capitalize on ABM insights, handle consultative sales conversations, and ultimately close deals with account-focused, lead generation strategies. We also continuously share campaign performance data and strategic insights with you – it’s a partnership for growth.

All of these efforts are underpinned by Martal’s use of real-time data and intent signals, much like the trends outlined earlier. We constantly refine targeting based on what the data tells us – which industries are surging in interest, which job titles are engaging, what messaging is clicking. Our goal is to help you scale your outbound efforts efficiently by focusing on the activities that drive the highest ROI. In fact, our clients have seen their outbound pipelines grow predictably while keeping acquisition costs low, because we prioritize quality over quantity every step of the way.

Ready to take your ABM and outbound strategy to the next level? We invite you to book a free consultation with Martal Group. In this no-obligation session, we’ll discuss your growth goals, evaluate your current ABM data usage, and show you how our data-driven outbound framework can rapidly accelerate your results. Whether you’re just starting with ABM or looking to refine an advanced program, our experts will offer actionable recommendations tailored to your situation. Let us do the heavy lifting to identify and engage your best accounts, so your internal team can focus on doing what they do best – closing deals and serving customers.

The era of account based marketing data is here. Companies that leverage these key trends and insights stand to dominate their markets, while those that stick to spray-and-pray marketing will fall behind in efficiency and effectiveness. By embracing intent data, AI analytics, personalization, and robust ABM measurement – and perhaps partnering with specialists like Martal Group – you’ll position your organization to capture more value from each high-value account. The playbook is in your hands; now it’s time to act on it. Here’s to scaling your B2B success with the power of ABM data!

References

FAQs: Account Based Marketing Data

What is ABM intent data?

ABM intent data reveals which target accounts are actively researching products or services like yours. It includes behavioral signals such as web searches, content engagement, and topic surges, helping marketers prioritize outreach to in-market buyers. Sources include first-party activity (your website) and third-party providers. This data is vital in identifying the 5% of accounts to buy or sales ready leads.

What is ABM Analytics?

ABM analytics refers to tracking account-level metrics to measure engagement, pipeline influence, and revenue impact. It includes insights like engagement scores, deal progression, and account win rates. These analytics help align outbound sales and marketing, optimize ABM performance, and prove ROI with metrics tailored to the buying journey.

What are the three types of ABM?

The three types of ABM are One-to-One (custom campaigns for single high-value accounts), One-to-Few (targeted outreach to small account clusters), and One-to-Many (scalable ABM using automation for broader segments). Each approach varies in personalization and scale, with strategies often combined in a tiered ABM program.

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