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Data-driven ABM: how to build a strategy on account and buying group intelligence

Learn how to use B2B data and intent signals to prioritize accounts, map B2B buying groups, and execute more effective account-based marketing programs.

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Account-based marketing (ABM) is a cornerstone for B2B companies targeting mid-market and enterprise accounts. It provides a structured approach to managing the realities of B2B buying – extended sales cycles, complex buying committees, and prospects who are empowered to do more research than ever before directly speaking with a sales team.

In contrast to traditional high volume B2B lead generation, ABM focuses on a specific list of high value/high propensity accounts.

Data is central to every account-based marketing strategy. Without a solid data foundation, ABM initiatives are built on assumptions, static lists, and disjointed execution. Conversely, robust data provides teams with the necessary visibility to effectively prioritize accounts, understand and reach buying committees, align messaging, and ensure timely action.

This account-based marketing primer will explore the data essential to execute high performing ABM strategies and how it supports everything from planning and activation to ongoing measurement and optimization.

Understanding account-based marketing (ABM)

What is ABM?

Account-based marketing (ABM) is a growth strategy in which marketing and sales teams collaborate to execute high-touch, personalized campaigns designed to penetrate high-value accounts. Instead of casting a wide net to attract many leads, ABM focuses resources on engaging a specific set of key accounts through highly personalized campaigns and tailored content.

The foundation of ABM rests on three core principles: precise targeting and ICP definition, deep personalization, and coordinated engagement throughout the buyer’s journey across the buying group. This focused resource allocation toward the accounts with the highest revenue potential results in a greater Return on Investment (ROI) and significantly improves internal team alignment.

Developing an effective ABM strategy

For an ABM strategy to be successful, it must be built on foundational components that work together and guide teams through a logical sequence of steps:

  • Ideal customer profile (ICP): the initial step in ABM strategy development involves defining or refining the Ideal Customer Profile (ICP). This profile is created using a combination of firmographic attributes, technographic indicators, historical performance data, market insights, and strategic priorities. Its purpose is to accurately describe the organizations most likely to benefit from your solution and deliver long-term value.
  • Target account list (TAL): once the ICP is defined, teams use it to create the TAL. This list is a prioritized group of accounts that both marketing and sales have agreed to pursue. The TAL should not only align with revenue goals and marketing and sales capacity–it should be dynamic, evolving to shifts in market conditions and account behavior.
  • Buying group definition: Following the selection of target accounts, identifying the buying committee is essential. Given that B2B purchase decisions are seldom made by a single individual, teams must understand the roles and priorities of those who influence purchases, and how their level of engagement changes through the buying journey. This knowledge is crucial for planning effective engagement strategies.
  • Account-level personalization: True ABM relies on account-level personalization to transform generic outreach into a bespoke journey for every member of the buying group. By combining tailored content generation with precise benefit articulation, you move beyond templates to deliver insights that reflect the account’s specific reality. When this is paired with a strategic channel mix, you meet stakeholders exactly where they are most attentive, ultimately validating your value by proving you truly understand their unique challenges.
  • Cross-functional alignment: marketing, sales, and customer success teams must all operate from a unified, account-level view, with common goals and metrics. This shared perspective is crucial for delivering a cohesive and personalized customer experience throughout the entire account lifecycle.
    This convergence of data, goals, and effort ensures that the entire organization is focused on maximizing the lifetime value of high-value accounts, leading to more efficient resource allocation, faster deal closure, and improved customer loyalty.

Using ABM templates and frameworks

Using ABM templates and frameworks allows teams to execute their ABM strategies more efficiently. This standardization also helps maintain consistency as their programs grow and scale. These frameworks often include ICP templates, TAL scoring models, and messaging matrices that map specific value propositions to different buyer personas. These operational assets reduce ambiguity, improve cross-team collaboration, and make it easier to onboard new team members.

Over time, these elements form a repeatable ABM playbook that supports long-term execution by turning strategy into a system rather than a series of one-off campaigns.

What is ABM data and why is it important?

What is ABM data?

A specialized dataset providing comprehensive insights is essential for a successful ABM strategy. This collection of data is essential for accurately identifying and segmenting high-value accounts, determining the best engagement methods, and ultimately, measuring the overall success of the ABM initiative to ensure continuous optimization.

This essential data (ABM data) shifts marketing from a more general, unfocused effort to a precise, collaborative, and measurable strategy focused exclusively on high-value accounts and the buying groups that drive their decisions. Instead of debating which accounts to prioritize, teams can use observable signals to reliably determine an account’s fit, interest, and readiness for engagement and the messages and actions to effectively influence them.

 

Types of data applied in account-based marketing

To thoroughly assess an account’s readiness for engagement, these are the data types most often applied in ABM:

  • Firmographic data provides foundational context, including industry, size, revenue, and location.
  • Technographic data reveals the technologies an account uses, helping teams tailor messaging and competitive positioning.
  • Engagement data includes records of how target accounts have interacted with your brand in the past, capturing their activity with content, campaigns, and outreach across channels.
  • Buying group personas add structure to complex decision-making by identifying stakeholder roles, priorities, and influence, helping teams map and understand B2B buying groups
  • Intent data helps teams identify when accounts are actively researching topics related to a solution.Bombora Company Surge® Intent data highlights increases in topical research activity, helping teams identify accounts engaging across the entire buying journey (early, mid, and late stage research). These insights reveal who is driving research, what they need, and—most importantly—exactly when to engage them.
  • Identity and enrichment data unifies account and buying group profiles across touchpoints allowing for a holistic understanding of account and buying group behavior and consistent activation.

Collecting and analyzing ABM data

Organizations typically collect ABM-relevant data from existing systems (like CRM, marketing automation, and advertising platforms), and external providers (such as third-party Intent data sources like Bombora). Identity resolution solutions like Bombora’s Visitor Insights (VI) are then used to connect anonymous website activity with specific known accounts and target account lists. This connection enables more personalized web experiences and retargeting campaigns. The challenge lies in unifying and interpreting that data.

By leveraging analytics and predictive scoring, teams can prioritize accounts based on shifts in behavior rather than relying on static characteristics. Understanding patterns in intent allows teams to pinpoint when accounts are nearing a purchasing decision, enabling them to engage earlier and more relevantly. For more details on this subject, see our resource on harnessing intent data for ABM.

ABM data in action: turning insights into execution

Using ABM data to personalize marketing efforts

ABM-relevant data enables personalization that reflects actual buyer behavior. Marketing and sales teams use this data to segment target accounts based on intent signals, engagement level, and buying stage, then tailor value proposition messaging, content, and timing accordingly. For example, teams can trigger specific campaigns when accounts show increased interest in relevant topics, customize website experiences for known accounts, and align outreach based on recent engagement across channels. This level of insight guides your creative decisions, ensuring your messaging reflects that specific interest.

 

Enabling ABM through data unification and orchestration

Account-based marketing isn’t dependent on a technology or software platform. It’s a strategic discipline built on unifying data, aligning teams, and activating insights consistently across channels.

Successful ABM programs connect data sources and tools to support four core capabilities:

  • Unification of account and buying group data: bringing together firmographic, engagement, and intent signals to create a shared account and buying group view, including resolving identities across channels to connect anonymous and known activity
  • Segmentation and prioritization: using ABM data and intent signals, teams can score and prioritize accounts and buying groups based on engagement, in-market signals, and potential value, ensuring resources are focused where they will have the greatest impact.
  • Orchestrated, omnichannel activation: delivering coordinated engagement across email, advertising, content, and sales outreach by activating targeted ABM audiences based on account readiness
  • Measurement and next best actions: measuring performance at the account and buying group level to understand engagement across the buyer’s journey and guide optimization. Leveraging B2B signals, such as B2beacon™, enables teams to quantify impact with greater precision and identify the next best actions for each account

Case studies and success stories

Organizations that apply intent-driven ABM-relevant data often see improvements in pipeline quality, sales efficiency, and deal velocity.

Examples of effective ABM implementation

Our client, a premier cybersecurity organization, invested 90% of its marketing budget into Search Engine Marketing (SEM), gated assets, and content syndication only to realize a 1% return in digital leads. By shifting gears and using Company Surge® Intent data to identify and generate a list of high propensity “in market” accounts that are actively researching solutions, it was able to create, nurture, and measure an entirely new sales funnel.

The client further maximized the impact of their intent data by developing targeted ABM programs across paid media and account-based marketing (ABM) initiatives. These programs included building dynamic target account segments based on in-market intent signals, activating personalized advertising campaigns to engage those accounts, and aligning messaging to specific buying group interests.

As a result, the client saw a 33% improvement in target-to-booked-meeting conversion along with a 122% increase in email open rates, demonstrating the impact of intent-driven ABM execution on engagement and pipeline performance.

Another client, a category-leading B2B retailer, faced challenges integrating programmatic advertising and Intent data into their account-based marketing (ABM) strategy due to the complexity of their existing systems, hindering effective TAL engagement.

The retailer leveraged B2beacon™ with frequency capping, a solution in partnership with The Trade Desk, Bombora, and Chalice AI, to ensure delivery to every account on their target list and report on performance. The addition of B2beacon™ led to 243 new sign-ups and a 59% decrease in cost per sign-up, significantly improving the retailer’s marketing efficiency and providing visibility into campaign performance relative to key business metrics.

Resources and further reading

If you’re looking to deepen your knowledge of account-based marketing and the practical frameworks for planning and execution, there are several resources available:

  • ABM handbooks: comprehensive guides that cover the cultural shift required for ABM.
  • ABM playbooks: tactical instructions for running specific campaign types.
  • Guide to account-based marketing resources: curated lists of tools and frameworks to help you get started

For the latest industry trends and deeper insights into ABM execution and intent-driven strategies, visit the Bombora blog. Also, explore B2beacon™ and how it utilizes AI technology to give marketers superior control over cross-channel campaigns, achieving 99% accuracy. With B2beacon™, you benefit from total control over frequency, the transparency needed to validate campaign performance, and confirmation of your ad spend.

Conclusion

A successful account-based marketing (ABM) strategy relies heavily on a strong data foundation. The correct data is essential for identifying and reaching the companies that are actively in the market for your product or service.

Bombora supports this by identifying these high-potential accounts and providing genuine buying signals, which allows teams to engage with them in a relevant way at every stage of the buyer journey. This strategic shift moves marketing from broad, general tactics to an intent-driven, focused plan. As a result, teams can concentrate their efforts on the accounts most likely to convert.

Find out what Bombora data can do for your ABM strategy: Speak to an expert today.

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