The Evolution Nobody Predicted
When account-based marketing first entered the mainstream lexicon around 2015, it was positioned as a corrective to the excesses of inbound marketing. The logic was simple: instead of casting wide nets and hoping that valuable accounts would self-identify, marketing teams should deliberately target high-value accounts with personalised campaigns. It was, in essence, a return to the direct marketing principles that predated the digital era, augmented by modern targeting capabilities.
A decade later, ABM has undergone two distinct evolutionary phases and is entering a third that bears little resemblance to its origins. As The CMO's recent analysis of account-based marketing trends documents, the strategy is maturing beyond its demand generation roots into something far more ambitious and far more consequential for how enterprises organise around revenue.
Understanding this evolution — and positioning for the third wave — requires more than tactical adjustment. It requires a fundamental reconsideration of how marketing, sales, customer success, and product functions relate to one another in the context of enterprise revenue generation.
The First Wave: ABM as Targeting
The first wave of ABM, spanning roughly 2014 to 2019, was primarily a targeting innovation. Its defining characteristic was the account list — a curated set of high-value prospects that received differentiated marketing treatment. The technology stack was centred on account identification (reverse IP lookup, firmographic databases), advertising (programmatic display targeting by company), and basic personalisation (company name insertion in emails and landing pages).
First-wave ABM produced genuine results for organisations that executed it well. Response rates improved. Sales accepted a higher proportion of marketing-sourced leads. Average deal sizes increased as marketing concentrated resources on accounts with higher revenue potential. The business case was real.
But the first wave also revealed structural limitations. ABM was typically run as a parallel program alongside traditional demand generation rather than as an integrated strategy. Marketing owned the account list and the personalised campaigns; sales tolerated the approach when it produced pipeline and ignored it when it did not. Measurement was rudimentary — most organisations tracked account engagement scores and pipeline influence without establishing causal relationships.
Perhaps most significantly, first-wave ABM was almost exclusively a new-business acquisition strategy. Existing customers — often representing the organisation's largest revenue opportunity through expansion, cross-sell, and retention — were rarely included in ABM programs. The account-based lens was applied selectively, not systematically.
The Second Wave: ABM as Orchestration
The second wave, which gained momentum from 2019 to 2024, addressed many of the first wave's limitations by reframing ABM as orchestration rather than targeting. The defining shift was from account lists to account journeys — the recognition that each target account moves through a complex, non-linear buying process involving multiple stakeholders, and that effective ABM requires coordinating touchpoints across channels and functions.
Technology evolved accordingly. ABM platforms like Demandbase, 6sense, and Terminus expanded from advertising-centric tools to orchestration platforms integrating intent data, engagement analytics, sales enablement, and multi-channel activation. The concept of buying groups gained traction, acknowledging that B2B purchase decisions involve committees rather than individuals.
Second-wave ABM also brought marketing and sales into closer operational alignment. Shared account plans, joint pipeline reviews, and coordinated outreach sequences became standard practice in mature ABM organisations. As we examine in our analysis of CRM and marketing automation integration, the wall between marketing-qualified and sales-qualified accounts began to dissolve into a more nuanced model of account progression.
The results were compelling. ITSMA's 2024 ABM Benchmark Study reported that organisations with mature orchestration-based ABM programs achieved 40 percent higher win rates and 35 percent larger average deal sizes compared to non-ABM pipelines. Customer lifetime value for accounts engaged through ABM was 25 percent higher than for accounts acquired through traditional demand generation.
Yet the second wave, for all its advances, remained fundamentally a marketing-led initiative with sales participation. The orchestration was sophisticated, but the organisational architecture — marketing targets accounts, sales closes them, customer success retains them — remained unchanged. The account-based lens extended further along the customer journey but did not yet transform the underlying operating model.
The Third Wave: ABM as Revenue Architecture
The third wave of ABM, now emerging in the most advanced B2B enterprises, represents a qualitative shift. It is no longer accurate to call it account-based marketing, because marketing is no longer the primary organising function. A more precise term is account-based revenue architecture — a cross-functional operating model that organises all customer-facing activities around account-level outcomes.
Several converging forces are driving this transition.
The Collapse of Functional Silos
The traditional B2B revenue model — marketing generates leads, sales converts them, customer success retains them — was always a simplification. In practice, the buying journey crosses functional boundaries repeatedly. A prospect might engage with marketing content, interact with a sales development representative, attend a customer success-led webinar, evaluate the product through a self-service trial, and negotiate terms with an account executive — not necessarily in that order, and often simultaneously through different stakeholders within the same account.
Third-wave ABM acknowledges this complexity by organising around the account rather than the function. Account teams — comprising marketing, sales, customer success, and sometimes product specialists — operate as integrated units with shared objectives, shared data, and shared accountability. The question is not "whose lead is this?" but "what does this account need next, and who is best positioned to deliver it?"
This structural shift demands a fundamentally different approach to strategic planning, one that begins with account-level revenue objectives and works backward to functional responsibilities rather than aggregating functional outputs into revenue forecasts.
The Intent Data Revolution
The maturation of intent data — signals derived from third-party content consumption, search behaviour, technographic changes, and peer review activity — has transformed ABM from a targeting exercise into a timing exercise. The question is no longer merely which accounts to pursue but when to engage them and with what message.
Third-wave ABM platforms aggregate intent signals from dozens of sources, apply machine learning to identify patterns predictive of purchase readiness, and surface actionable insights to cross-functional account teams in real time. An account showing surging research activity around a specific capability, combined with technographic signals indicating an expiring contract with a competitor, combined with engagement data showing increased interaction with middle-funnel content, creates a composite picture that no single data source could provide.
The implications for marketing automation are profound. Traditional nurture campaigns — linear sequences triggered by individual actions — are inadequate for account-level orchestration. Third-wave ABM requires multi-touch campaigns that respond dynamically to account-level signals, coordinating marketing touches with sales outreach and customer success engagement in real time.
The Expansion Revenue Imperative
Perhaps the most significant driver of third-wave ABM is the growing recognition that customer expansion — upsell, cross-sell, and platform adoption — represents a larger revenue opportunity than new logo acquisition for most enterprise technology companies. SaaS companies with mature customer bases typically generate 60 to 70 percent of annual recurring revenue growth from existing accounts.
This economic reality makes first- and second-wave ABM, with their acquisition-centric orientation, strategically incomplete. Third-wave ABM applies account-based principles across the entire customer lifecycle, from initial awareness through expansion and advocacy. The account plan is not a pre-sale artifact; it is a living document that evolves through the customer relationship.
Customer success teams, in this model, are not just retention operators but revenue strategists. Product usage data, support ticket patterns, NPS trends, and adoption metrics become inputs to the account-based strategy alongside traditional marketing engagement and sales activity data.
Operationalising Third-Wave ABM
The conceptual case for third-wave ABM is compelling. The operational reality is demanding. Transforming from function-centric to account-centric revenue operations requires changes to technology, process, measurement, and culture that most organisations find deeply challenging.
The Data Foundation
Account-based revenue architecture depends on a unified view of each account's engagement, intent, product usage, and commercial history across every touchpoint and function. This is a data integration challenge of the first order.
Most enterprises operate with fragmented data architectures: marketing data in the automation platform, sales data in the CRM, product usage data in a product analytics tool, customer success data in a CS platform, and intent data in a separate ABM tool. Creating a unified account view requires deliberate data management, ETL, and enrichment work — stitching these sources together, resolving identity across systems, and maintaining data quality at scale.
The customer data platform (CDP) has emerged as the architectural centrepiece of many third-wave ABM implementations, providing the unified data layer that cross-functional account teams require. However, a CDP without governance is merely a more expensive version of data fragmentation. The entities, relationships, and business rules that govern the unified account view must be defined deliberately and maintained rigorously.
The Technology Architecture
Third-wave ABM requires a technology stack that is integrated by design rather than integrated by accident. The core components include:
CRM as the system of record: The CRM — whether Salesforce, Microsoft Dynamics, or HubSpot — serves as the canonical source of account and opportunity data. Every other system reads from and writes to the CRM through governed integrations.
Marketing automation as the engagement engine: Platforms like Oracle Eloqua, Adobe Marketo, Salesforce Marketing Cloud, or HubSpot execute the multi-channel campaigns that engage accounts across the buying journey. The critical requirement is that these platforms operate on account-level logic, not just individual contact-level triggers.
ABM platform as the intelligence layer: Dedicated ABM platforms provide intent data aggregation, account scoring, and orchestration recommendations. Their value lies not in replacing the marketing automation platform but in enriching it with account-level intelligence.
Product analytics as the adoption signal: For SaaS and platform businesses, product usage data is the most powerful predictor of expansion opportunity and churn risk. Integrating this data into the account view is non-negotiable for lifecycle-spanning ABM.
The integration architecture connecting these systems must be robust, governed, and bi-directional. This is where platform implementation and CRM integration expertise becomes critical — the connective tissue between systems determines whether the unified account view is a reality or a slide deck.
Account Scoring and Tiering
First-wave ABM used static account lists. Second-wave ABM introduced engagement scoring. Third-wave ABM requires dynamic, multi-dimensional account scoring that incorporates fit, intent, engagement, and relationship signals. As we explore in our analysis of how AI is transforming lead scoring, machine learning models are particularly well-suited to evaluating these multi-dimensional signals at scale.
Fit scoring evaluates how closely an account matches the ideal customer profile based on firmographic, technographic, and business model criteria. This is the foundation — a high-engagement account with poor fit is a distraction, not an opportunity.
Intent scoring measures the intensity and specificity of an account's research activity around relevant topics. Intent signals are inherently time-sensitive; a strong intent score last quarter is irrelevant this quarter.
Engagement scoring tracks the account's interaction with the organisation's owned touchpoints — content consumption, event attendance, website visits, email engagement, and sales conversations. Unlike intent scoring, which measures market-level behaviour, engagement scoring measures relationship-level behaviour.
Relationship scoring — the newest dimension — evaluates the depth and breadth of the organisation's connections within the account. How many buying committee members are engaged? How senior are the contacts? How recent is the last meaningful interaction?
These four dimensions combine to produce a composite account score that informs tiering decisions (how much resource to allocate), timing decisions (when to activate), and play decisions (which cross-functional playbook to execute). Organisations that invest in sophisticated lead scoring and ABM strategy find that their conversion rates and deal velocity improve dramatically.
Cross-Functional Playbooks
Third-wave ABM replaces the campaign concept with the playbook concept. A playbook is a coordinated set of cross-functional actions triggered by specific account signals. Unlike a marketing campaign, which is owned by marketing, a playbook is owned by the account team and involves orchestrated contributions from multiple functions.
Examples of third-wave ABM playbooks include:
The competitive displacement play: Triggered when an account shows intent signals around a competitor's product combined with technographic signals indicating a contract renewal window. Marketing delivers competitive positioning content, sales provides a customised business case, and customer success (for existing customers considering competitive alternatives) proactively addresses value gaps.
The expansion play: Triggered when product usage data shows an existing customer reaching capacity thresholds or adopting adjacent features. Customer success initiates a value review, marketing surfaces relevant case studies and thought leadership, and sales presents an expansion proposal aligned with the customer's observed usage trajectory.
The re-engagement play: Triggered when a previously active account goes dark — engagement scores decline, intent signals fade, product usage plateaus. The cross-functional response is calibrated to the likely cause: if the signals suggest competitive evaluation, the competitive displacement play is activated; if the signals suggest internal reorganisation, a relationship-rebuilding sequence begins.
Each playbook defines the specific actions, owners, timelines, and success metrics for the cross-functional response. Playbooks are documented, reviewed, and refined through regular retrospectives — a practice that benefits enormously from structured campaign maturity assessment.
Measurement in the Third Wave
Measurement is where third-wave ABM most dramatically departs from its predecessors. First-wave ABM measured account engagement. Second-wave ABM measured pipeline influence. Third-wave ABM measures account-level revenue outcomes across the entire lifecycle.
The key metrics include:
Account penetration rate: The percentage of target accounts that have progressed from awareness to active opportunity within a defined period. This replaces MQL volume as the primary top-of-funnel metric.
Buying group coverage: The percentage of identified buying committee members within each target account that are actively engaged. This replaces individual lead scoring as the measure of account readiness.
Pipeline velocity by account tier: The average time from first meaningful engagement to closed-won, segmented by account tier. This reveals whether resource allocation across tiers is producing proportional returns.
Account lifetime revenue: Total revenue generated from each account across initial sale, expansion, and renewal. This is the ultimate measure of ABM effectiveness and the metric that justifies the investment in lifecycle-spanning strategy.
Revenue per account team hour: The efficiency metric that ensures ABM's high-touch model is economically sustainable. As account teams scale, this metric prevents the strategy from becoming cost-prohibitive.
The Platform Dimension
Each major marketing automation platform brings distinct capabilities and constraints to third-wave ABM execution.
Oracle Eloqua's program canvas and custom data objects provide the architectural flexibility needed for complex account-level orchestration. Its native CRM sync capabilities support bi-directional account data flow, though the configuration requires deliberate governance.
Adobe Marketo's account-based features — particularly its native integration with Adobe's Experience Cloud and its account scoring capabilities — make it a natural fit for organisations committed to ABM. The platform's workspace model supports the multi-tier account architecture that third-wave ABM requires.
Salesforce Marketing Cloud's journey builder, when combined with Salesforce CRM's account hierarchy and Data Cloud's identity resolution capabilities, creates a powerful foundation for account-level lifecycle orchestration. The key challenge is the complexity of the integration architecture.
HubSpot's target accounts feature and its native CRM integration lower the barrier to entry for organisations beginning their ABM journey. For organisations advancing to third-wave practices, HubSpot's operations hub provides the workflow automation and data quality tools needed to maintain the unified account view.
Regardless of platform, the operational execution of third-wave ABM benefits from dedicated platform expertise that can translate strategic intent into platform configuration.
The Organisational Transformation
Perhaps the most challenging aspect of third-wave ABM is the organisational change it demands. Moving from function-centric to account-centric operations challenges entrenched incentive structures, reporting relationships, and professional identities.
Sales leaders accustomed to individual quota models must adapt to account team models where credit is shared. Marketing leaders accustomed to measuring program performance must adapt to measuring contribution to account outcomes. Customer success leaders accustomed to defensive retention metrics must adopt an offensive expansion mindset.
These are not trivial adjustments. They require executive sponsorship, incentive realignment, and patient cultural work. The organisations that succeed typically begin with a pilot — a small number of top-tier accounts managed by a cross-functional team with shared objectives and shared accountability — and expand as the model proves its value.
The Imperative for Enterprise Marketers
The evolution from first-wave ABM (targeting) through second-wave ABM (orchestration) to third-wave ABM (revenue architecture) reflects a broader maturation of B2B marketing from a lead generation function to a revenue function. Each wave has produced meaningful improvements in marketing effectiveness. Each wave has also raised the operational bar.
Third-wave ABM is not for every organisation. It requires a level of data integration, cross-functional alignment, and executive commitment that many enterprises are not yet prepared to invest. But for organisations selling complex solutions to enterprise buyers — where buying committees are large, sales cycles are long, and customer lifetime value is high — the third wave represents the most significant strategic opportunity in a decade.
The enterprises that build the data foundations, technology architectures, cross-functional playbooks, and measurement frameworks described here will not merely improve their marketing. They will fundamentally reshape how they create, capture, and expand revenue. Those that continue to treat ABM as a marketing program — however sophisticated — will find themselves competing against organisations that have made it an enterprise strategy.
The third wave is not coming. It is here. The only question is whether your organisation will ride it or be overtaken by it.
