Measuring ABM ROI: Attribution Models Driven by Intent and Engagement Signals
- Last updated on: August 11, 2025
As B2B marketers adapt to a rapidly changing marketing landscape. Account-based marketing (ABM) has become a requirement for organizations that market to high-value accounts. However, despite the rapid expansion and development of ABM, the most prominent issue remains one that ABM marketers have struggled with since the inception of ABM, which is measuring ABM ROI.
Traditional attribution marketing methods often fall short for ABM, given the multistakeholder account buying journey. This void in measurement opportunity has propelled marketing teams to explore the attribution options. And understand how their ABM program is impacting their pipeline and revenue. In this article, we discuss why we need a new method for measuring ABM ROI. And how the attribution method of using intent and engagement could be used to enable accurate and actionable measurement.
Why Measuring ABM ROI is More Complex Than Traditional Marketing
Unlike traditional marketing models that are aimed at one buyer, ABM works with entire accounts, which usually consist of multiple decision makers and influencers. This inherently complicates attribution. ABM, by its very nature, involves a change in attribution because the traditional last touch or first touch attribution models simplify the buyer’s journey by crediting a single interaction. ABM buying is often a process that extends over months and sometimes quarters, involving multiple touchpoints. On top of content to build awareness, ABM includes outreach and discussions with direct sales.
76% of marketers get a higher ROI with ABM than they do with any other form of marketing. (ABMLA, 2020). The determination that simple attribution does not reflect the rich engagement across buying influences of an account complicates how to assess what activity is actually impacting business. Consequently, some marketing executives do not understand how to properly justify their ABM budgets and how to build effective ABM programs from misleading or incomplete knowledge. To overcome this hurdle, new e-marketer practices need to implement multi-touch, signal-based attribution practices that are a more true representation of the ABM buyer journey.
Understanding Intent and Engagement signals
Intent and engagement signals provide the missing link in ABM measurement, as they help to decode how accounts and contacts engage with marketing and sales in real time. Intent signals are behavioral indicators when a signal represents a behavioral cue suggesting that a prospect is interested or ready to purchase. This involves web browsing, content consumption, keyword queries, 3rd party intent data, and social media. For example, if your target account is consuming competitive research reports or visiting product comparisons, then they are displaying higher levels of intent.
Engagement signals track opens and clicks of emails, webinar attendance, event attendance, demo requests, and conversations with sales reps. Engagement signals track behavior demonstrating engagement and help to validate which contacts in accounts are moving down the buying cycle.
A combination of intent and engagement data gives marketers the complete picture of account health and what marketing tactics are working. This is essential to determine accurate ABM ROI.
Attribution Models Driven by Intent and Engagement
Attribution models need to provide more than a simplistic credit assignment to measure ABM ROI effectively and add the nuances of intent and engagement signals. Several types of attribution models stand out.
Multi-Touch Attribution (MTA):
MTA distributes credit to various touch points in the buyer journey since all activities contribute to the outputs collectively. In a B2B environment, MTA models can also include intent and engagement data and provide weighting to the interactions depending on the signals (strength and time). This can help paint a more detailed picture of what matters in pipeline progression.
Account-Level Scoring Models:
Account-level scoring models use intent and engagement signals at the account level. Rather than just the individual contact level. You can see the collective account interest from the other members of the buying group. As they exhibit intent and engage, you can score the individual accounts. It will be based on both behavioral indicators and your outreach targeting and sequence. From there, when you assess revenues attributed, you can now prioritize which accounts to reach out to. And also, you can have better attribution of revenue to provide influence.
AI-Driven Attribution:
AI and machine learning are emerging to help with ABM measurement and assessment. They are doing this by adapting signals dynamically, and paving the way for predictive so measurement too. AI-driven attribution models even use all the marketing data generated from CRM. Marketing Automation, or Intent Platforms, analyzes large datasets for opportunities to pinpoint similarities. And allocate revenue credits more accurately than just adhering to rule-based systems.
These attribution models solve the challenge of linking marketing investments to pipeline influence and revenue produced. They provide marketing leaders’ insight into what ABM tactics provide the best returns.
Case Example:
In this case study, a top SaaS company used AI-driven multi-touch attribution to combine third-party intent data with CRM and marketing automation. They discovered that early engagement with competitor comparison content and subsequent webinar attendance were strong indicators of pipeline creation. By reallocating expenditure to key touchpoints, they increased ABM ROI by 25% in six months.
Practical Steps to Implement Intent-Driven ABM Attribution
Implementing the ABM ROI measurement process with intent and engagement signals does require proper planning and implementation:
- Data Integration: Integrate your CRM, Marketing Automation, and Intent Data Platform to have one view of your accounts’ behavior and spokes relating to accounts, other than giving them a score. The seamless integration allows for consistency across reporting and attribution.
- Alignment on KPIs: Sales and marketing departments must first understand the definitions of intent and engagement signals, and set outbound attribution based on KPIs that align with business outcomes.
- Analytics and Reporting: Develop dashboards and use reporting functions in other analytics tools to track your accounts’ scores, engagement levels over time, and revenue influence through your pipeline. Periodic review of your reporting will allow you to continue to refine your attribution models and ABM practices.
As a best practice. Implement these steps to ensure, as much as possible. That you convert measurements into “intelligence,” To enable smarter business decisions and targeting of your budgets.
Conclusion
Calculating ABM ROI is no longer a guessing game. Marketers may overcome the constraints of traditional models by adopting attribution models based on intent and engagement signals. Marketers also give them a transparent, accurate perspective of their ABM program’s genuine impact. As ABM evolves, investing in advanced measuring methodologies is critical for improving pipeline contribution and speeding revenue development. Forward-thinking marketing leaders will be best positioned to demonstrate ABM’s benefits and obtain continuous funding for this vital strategy.
FAQs on Measuring ABM ROI
- Why is measuring ABM ROI different from traditional marketing?
ABM involves multiple stakeholders and longer sales cycles. Traditional models miss these complexities, so ABM requires multi-touch attribution to capture the full buyer journey.
- What are intent signals, and why do they matter?
Intent signals show when an account shows interest through behaviors like content views or searches. Using these signals helps marketers spot buying readiness and measure impact accurately.
- How does multi-touch attribution improve ABM measurement?
Multi-touch attribution gives credit to all key interactions across the buyer journey. It offers a clearer view of which activities drive results.
- How does AI enhance ABM ROI measurement?
AI analyzes large data sets to weigh touchpoints dynamically and predict which marketing actions influence the pipeline and revenue.
- How can marketing and sales teams align on ABM attribution?
Teams align by agreeing on key signals, sharing data, and setting clear goals to measure which efforts drive pipeline growth.
