How Account-Based Marketing Is Powering the Next Era of Programmatic Advertising

Account-based marketing flips traditional advertising logic. Instead of defining an audience by demographic attributes or behavioral signals and then serving ads to anyone who matches, ABM starts with a defined list of target companies—specific organizations that sales and marketing jointly identify as high-priority prospects or expansion opportunities.

Programmatic ABM takes this account-centric strategy and applies the automation, data integration, and optimization capabilities of modern ad tech. You're still using demand-side platforms (DSPs), real-time bidding, and algorithmic optimization. But instead of maximizing reach across a broad audience segment, you're orchestrating personalized messaging to decision-makers at your most valuable accounts across multiple channels.

This article explains what ABM targeting means in a programmatic context, why it's proving more effective than traditional B2B advertising approaches, and how organizations can implement it successfully. 

What is account based marketing targeting?

Most digital advertising targets people. Account-based marketing targets companies.

That distinction matters enormously in B2B contexts. When you're selling enterprise software, manufacturing equipment, or professional services, your customer isn't an individual—it's an organization. Yet traditional programmatic advertising treats users as isolated decision-makers, serving ads based on job titles, browsing behavior, or demographic profiles without considering whether those individuals work at companies you actually want as customers.

Account-based marketing targeting inverts this model. You begin by identifying specific companies that fit your ideal customer profile, then use programmatic infrastructure to deliver tailored advertising to employees and decision-makers at those firms. Rather than casting a wide net and hoping to catch a few qualified prospects, you're fishing with a spear aimed at your most valuable targets.

⚡ ABM is about who first, not where. Decide the account list and buying groups before you worry about channels—everything else flows from that decision. 

In practice, this means uploading a list of target accounts—say, 500 enterprise companies in the financial services vertical—into your advertising platforms. The technology then matches those companies to individual users (through IP addresses, firmographic data, cookie matching, or other identity resolution methods) and serves your ads only to people at those specific organizations. Someone browsing the web from a target account's office network might see your ad. Someone with an identical job title at a non-target company won't.

This precision matters because B2B buying involves complex purchasing committees. 

Research shows that B2B purchase decisions involve an average of 10-11 stakeholders, with buying cycles extending beyond 11 months. 

Your ads need to reach not just one decision-maker but multiple influencers within the same account—the CTO, the VP of Operations, the CFO, various directors and managers—and keep your brand visible throughout an extended evaluation process.

The evolution from audience to account based targeting

Early programmatic advertising wasn't built for B2B precision. The infrastructure optimized for consumer marketing at scale: reach millions of users as efficiently as possible, using cookies and broad data segments to approximate relevance. For consumer brands selling shoes or subscription boxes, this works well enough. For B2B companies with target markets measured in hundreds or thousands of potential customers, it wastes staggering amounts of money.

B2B marketers initially tried to retrofit programmatic tools for their needs, creating audience segments based on job titles, company size, and industry. But this approach still generated massive spillover. The shift toward account targeting emerged as marketers realized they needed to constrain their programmatic buys geographically—not by location, but by company. They started uploading named account lists into data management platforms and working with identity resolution vendors to map those corporate entities to online identifiers.

The breakthrough came when advertising platforms developed the capability to ingest B2B account data natively. LinkedIn introduced account targeting features specifically for B2B advertisers. DSPs began partnering with firmographic data providers. Intent data vendors like Bombora started syndicating B2B audience segments into programmatic marketplaces, enabling advertisers to target decision-makers at in-market accounts across channels including emerging platforms like connected TV.

The programmatic ecosystem now recognizes accounts as audiences. This represents a significant architectural shift—the infrastructure that once optimized for cookie-level reach now optimizes for account-level engagement.

How ABM integrates into the programmatic ecosystem

Now, this is where we become technical.

Programmatic account based marketing sits on four interconnected layers that turn named accounts into media audiences and measurable outcomes:

• Account lists and intent signals: This is the definition layer: the set of companies (and buying groups) that matter plus evidence they’re in market. It typically combines an ICP-based account list with first-party engagement and third-party intent (e.g., topic surges from specialist providers). In many DSPs, these inputs appear as account objects with estimated coverage and reach, and can include partner intent packages (such as iABM-style offerings) that enrich targeting.
• Identity resolution: This is the connective layer that links people and devices to their employer. Privacy-safe identity graphs (for example, enterprise IDs like RampID) translate emails, device IDs, and other signals into persistent, interoperable identifiers so the same account can be recognized across web, apps, CTV, and offline-to-online bridges.

⚡ Identity is the spine of B2B addressability. If you can’t recognise the employer across devices, you can’t manage account-level exposure.

• Activation and controls: This is the buying layer where accounts are matched and media is transacted. Platforms map company domains and known contacts to addressable inventory, then apply account-aware controls—such as frequency caps, pacing, and creative routing—so delivery reaches the right roles within each target company. Professional networks (e.g., company list matching) and enterprise DSPs support this layer.

Measurement: This is the analytics layer that expresses results in account terms. Metrics focus on account reach and penetration, exposure and frequency by account, on-site behavior from the buying group, stage progression, pipeline contribution, and revenue—often aligned to frameworks like Forrester’s B2B Revenue Waterfall rather than lead counts.

💡 For a plain-English primer on the programmatic “pipes” (DSPs, SSPs, auctions) that sit underneath these layers, see AI Digital’s overview of programmatic advertising.

Traditional ABM vs programmatic ABM

Traditional ABM is the classic, hands-on approach. Teams pick a short list of strategic accounts and create tailored experiences for those companies: custom emails, SDR outreach, executive events, direct mail, microsites, and content that speaks to that account’s priorities. It excels at depth and relationship building. The limitation is scale. Most of the work is manual, and coverage often concentrates on a few contacts inside each account. Measurement tends to focus on meeting quality, opportunity creation, and deal progress gathered from sales notes and CRM. 

⚡ In short, traditional ABM is high-touch and high-impact, but it is bandwidth-heavy and usually reserved for a narrow set of top-tier accounts.

Programmatic ABM extends those principles with paid media that can recognize and reach the buying group wherever they spend time online. Identity and account lists are used to direct ads across the open web, online video, native, audio, social, and connected TV. This adds three capabilities traditional ABM struggles to deliver at scale:

• Buying-group coverage. You are not limited to one or two known contacts; identity graphs and company lists allow you to reach multiple roles in the same company across channels.
• Account-level controls. Enterprise DSP features regulate exposure by account (reach, frequency, pacing) and report delivery at the account level so you can shift budget toward under-reached targets.

Omnichannel reach, including CTV. LinkedIn introduced CTV ad buying in 2024, letting B2B marketers reach target companies’ employees while they stream premium content through partners like Roku, Samsung, Paramount, and NBCUniversal.

⚡ Put simply, traditional ABM provides depth; programmatic ABM adds breadth and continuity without losing the account focus.

Programmatic ABM examples

Here are a few practical ways teams put programmatic ABM into market. Each example starts with a named-account list, uses identity to reach the full buying group across channels, and reports results at the account level. Treat them as building blocks you can mix and match based on your stack and budget.

1. Programmatic ABM in a DSP (The Trade Desk iABM)

The Trade Desk’s intelligent account-based marketing (iABM) lets advertisers set the account list as the primary audience and then manage delivery to those accounts. You can cap frequency at the account level, monitor daily account reach and penetration, and shift budget toward accounts you have not yet reached sufficiently. This solves a common B2B problem: over-serving a few individuals while missing the rest of the buying group.

⚡ Account-level pacing prevents burn-out. Without it, a few people see everything while the rest of the buying group sees nothing. 

2. LinkedIn company-list activation.

LinkedIn Campaign Manager supports Matched Audiences built from uploaded company or domain lists. Once the list is matched, you can filter by seniority, function, and title, and run sequenced creative by buying stage (for example, thought-leadership for awareness, customer stories for validation). This keeps targeting anchored to named accounts while taking advantage of LinkedIn’s professional graph.

3. CTV in ABM to boost upper-funnel attention.

Connected TV adds high-impact reach inside the same named accounts you are targeting on web and social. As mentioned, in 2024, LinkedIn introduced CTV buying that extends your company lists into premium streaming environments. 

Meanwhile, Demandbase’s 2024 State of B2B Advertising reported stronger clicks and conversions for customers that ran CTV and display together, which supports using CTV to spark attention while display and native handle follow-through.

⚡ CTV is your awareness multiplier. Use it to lift familiarity inside named accounts, then let web and social capture the demand.

4. Legacy proof point: identity + account focus increases usable reach.

A well-known AdExchanger case study described how Hewlett Packard Enterprise adopted a common ID strategy for programmatic ABM. Their average match rate jumped to 88% (up from roughly 20–40%), CPMs effectively halved for the same spend, and form fills from targeted accounts increased by 96%. 

Although the example is older, it illustrates a durable principle: when you anchor media to the account and strengthen identity resolution, you reach more of the right people and convert more of them.

5. Why identity quality still matters today.

Across the wider ad market, many providers still report low match rates when tying app and web signals together—often under 45%, according to a 2024 AdExchanger study. That gap is exactly why programmatic ABM efforts lean on enterprise identity graphs and account-level reporting: better matching yields better coverage of the buying group and clearer measurement.

6. Where CTV fits in the mix right now.

LinkedIn’s CTV launch formalized something B2B teams were already testing through DSPs: using television screens to reach named accounts during off-work and work-from-home viewing. Treat CTV as an attention driver that raises familiarity inside the account, then use display, native, and LinkedIn feed formats to deepen engagement and capture demand.

Why ABM is redefining programmatic advertising

Before diving into the tactics, it helps to see why ABM changes the logic of programmatic. When the unit of targeting shifts from anonymous audiences to named accounts and buying groups, every downstream decision—data, creative, channels, and measurement—shifts with it. The result is a move away from volume for its own sake toward accountable engagement that sales can recognize and act on. The next sections break that down: how this shift improves relevance, how data and automation power personalization, and how it closes the gap between marketing activity and revenue.

From mass targeting to meaningful engagement

Traditional programmatic was built to maximise reach against broad segments like “IT decision-makers” or “finance pros.” ABM resets the unit of value from an anonymous audience to a named account and its buying group. 

As mentioned, in B2B the decision is slow and collective; progress comes from consistent, relevant engagement across stakeholders in the same company, not scattershot impressions at look-alikes. It’s also why adoption keeps climbing: 

Momentum ITSMA’s 2024 Global ABM Benchmark found 90% of organisations run ABM and 81% say it delivers higher ROI than other marketing— signal that account-first planning outperforms broad reach for complex B2B purchases.

With ABM, “who” you reach is preselected (named accounts and roles), so every impression is more likely to influence a real opportunity. Media can be evaluated on account coverage (how much of the buying group you’re reaching) and account penetration (how frequently those people are exposed), which are stronger precursors to pipeline than generic CTR or CPM. This reframes programmatic from a volume exercise to a relevance exercise anchored to revenue.

Data, automation, and personalization in action

ABM thrives on two data streams working together:

1. Fit data (your ICP list, firmographics, product usage for customers), and
2. Timing signals (topic surges, research activity, first-party engagement).

Benchmarks through 2023–2025 show growing use of intent data to decide which accounts to prioritise and what content to serve; Demand Gen Report’s 2024 survey highlights the push to formalise intent strategies rather than treat them as ad-hoc add-ons.

Automation turns those signals into delivery. Identity graphs map people to employers so platforms can recognise members of a buying group across the open web, apps, and CTV. That opens the door to true cross-channel ABM. 

⚡ Programmatic ABM is personalization where it counts: the accounts that shape your revenue plan.

If video is central to your mix, pair account targeting with channel-appropriate formats (video, native, display) and map creative to buying stages. 

‍💡 For a primer on channels and formats, see AI Digital’s guide to programmatic video advertising.

Bridging the marketing–sales gap

ABM brings marketing and sales onto the same scorecard because accounts become the shared currency. Lists, buying groups, stages, and goals are defined together, then media and outreach are planned against that shared plan. Measurement follows the same logic: instead of tallying anonymous leads, teams track account reach and engagement, stage progression, pipeline, and revenue—the language sales trusts. 

‍⚡ Forrester’s B2B Revenue Waterfall is the common framework many teams use to codify these definitions and report progress in revenue terms.

Evidence that this alignment is advancing: 6sense’s 2025 attribution and contribution benchmark notes a shift toward revenue-aligned reporting, even as some organisations still rely on legacy lead metrics. 

The direction is clear—ABM programmes are being judged on pipeline and closed-won impact, not just on upstream activity—tightening the link between programmatic spend and sales outcomes. ‍

The strategic value of programmatic ABM

Beyond operational benefits, programmatic ABM delivers strategic advantages that compound over time. Three stand out as particularly significant for B2B organizations: spending efficiency, scalable personalization powered by AI, and full-funnel measurement tied to business outcomes.

1. Smarter targeting and more efficient media spend

The fastest win with programmatic ABM is less waste. By constraining delivery to named, sales-approved accounts, you stop funding impressions from people and companies that will never enter your pipeline. Every dollar is concentrated on winnable demand—the accounts your sales team actually wants.

Effectiveness improves as you focus spend:

In Momentum ITSMA’s global benchmark, ABM programs reported broad business impact: 78% saw pipeline growth and 74% saw revenue growth attributable to ABM activity—evidence that account-first targeting lifts outcomes, not just activity volume. 

Identity beats cookies for B2B. ABM works best when you recognise buying-group members across devices and channels. That requires interoperable identity and account-level controls. Modern DSP features make this practical: For instance, the above-mentioned Trade Desk’s iABM lets you target named accounts, regulate frequency at the account level, and read reach/penetration by account—so you spread impressions across the whole buying group and redirect budget from over-served to under-reached accounts.

Pair channels to combine attention with action. Cross-channel execution compounds efficiency because the same accounts see complementary messages. Demandbase reports that customers integrating CTV with display increased domains visited by 46% and clicks by 54%, a strong signal that CTV + display improves engagement inside named accounts and helps your lower-funnel formats work harder.

Bottom line: Programmatic ABM concentrates budget where revenue is likely, making spend easier to defend with pipeline and revenue metrics—not just CPMs or CTR. And because you’re targeting a finite TAM, this precision also makes programmatic viable for niche B2B categories that would otherwise struggle with broad, audience-only buys.

2. AI-powered personalization at scale

Personalization is no longer limited to a handful of flagship accounts. Advances in AI let you tailor messages, offers, and timing across hundreds of target accounts without ballooning headcount. Two shifts make this possible:

• Data orchestration that understands fit and timing. As mentioned, modern ABM stacks unify fit signals (ICP, firmographics, tech stack) with timing signals (intent surges, repeat visits, content topics) to infer where each account sits in the journey. That context is the trigger for personalization—what to say, to whom, and when.
• Decisioning that adapts creative by role and stage. Rules or models then assemble the right format + message + proof for each buying group: industry explainer for researchers, ROI evidence for finance, integration docs for architects. Dynamic creative handles the permutations, so personalization scales beyond manual one-offs.

Strategic takeaway: AI turns one-to-few personalization into a baseline across your entire target-account universe. You get relevance at scale—and you do it with consistent orchestration rather than dozens of disconnected, manual campaigns.

💡 For more on AI’s role in creative and media—and on dynamic content personalization—see AI Digital’s AI in digital marketing

3. Full-funnel visibility and revenue-level measurement

Programmatic ABM connects advertising to business outcomes in a way traditional, audience-only programmatic rarely does. Because the unit of targeting is a named account, you can follow that account from first exposure → buying-group engagement → opportunity → closed-won (and even into expansion), rather than inferring impact from anonymous clicks. 

This account-first lens is built for B2B complexity. As noted, purchases are made by groups, not individuals, so results need to roll up to the account: who in the buying group saw ads, who visited, what content was consumed, which opportunities were created, and how revenue advanced. Forrester underscores this, advising teams to identify and manage buying groups and then align CRM data so individuals are joined to the right account and opportunity.

The impact shows up in pipeline and revenue. Gartner reports that effective ABM programs increase overall pipeline conversion rates by 14%, while Forrester finds 99% of teams with an ABM program report higher ROI than their traditional marketing efforts—clear evidence that account-level planning translates into outcomes the business values.

Measurement maturity is improving. More teams are shifting beyond MQLs toward buying-group engagement and opportunity-level metrics, reflecting Forrester’s guidance to pivot from individual leads to engaging entire buying groups.

Platforms and processes make the full-funnel view practical. ABM ad platforms and DSPs can report reach and frequency at the account level, while CRM/CDP integrations perform lead-to-account matching (L2A) so ad exposure, site behavior, meetings, and opportunities are tied to the same account record. With that spine in place, marketers monitor coverage (how much of the buying group you’re reaching), progression (stage movement), and contribution (pipeline and revenue) rather than optimizing blindly for CTR.

You can then optimize with intent. If mid-funnel accounts stall, switch creative to proof assets (case studies, comparison guides) or increase account-level frequency for under-reached roles. If an account shows high engagement without a meeting, that signal routes to sales for timely outreach. This is the closed loop ABM enables: media, site behavior, and pipeline all informing each other in near real time.

For finance and leadership, the value is straightforward: spend is concentrated on named accounts and evidenced in opportunity and revenue terms. Beyond benchmarks, broader practitioner data echoes the trend; for example, G2’s 2024 round-up reports companies see a 10% revenue lift on average after one year of ABM, with 19% reporting 30%+ growth, reinforcing that account-centric programs can be tied to tangible financial outcomes when measured properly. ‍

How programmatic ABM works

Before we jump into the steps, a quick refresher on acronyms we’ll use: ICP (ideal customer profile), TAM (total addressable market), DSP (demand-side platform), SSP (supply-side platform), CRM (customer relationship management), CDP (customer data platform), ID graph (identity graph), and L2A (lead-to-account matching).

Step 1: Identify and prioritize key accounts

At this stage, you’re defining who the program should reach and in what order. Start with ICP fit and TAM, then add whitespace (new logos that fit your ICP but aren’t in the pipeline) and expansion (current customers with upsell/cross-sell potential). To separate fit from timing, layer intent signals (topic surges, research patterns, repeat visits) so you can tell who’s in-market now.

How to make it actionable: Tier your list: Tier 1 (high value, high intent), Tier 2 (high value, lower intent), Tier 3 (broader ICP). The tiers drive journey design and budget depth later.

Next up: once you know who to reach, you have to make those accounts addressable in media.

Step 2: Sync data and accounts with programmatic platforms

At this stage, you turn a spreadsheet of company names into addressable audiences across channels—and keep them in sync:

• List ingestion. Upload company/domain or contact lists where allowed. LinkedIn Matched Audiences supports company and contact list uploads for account targeting, with detailed guidance for advertisers and API users.
• Identity resolution. Use a privacy-safe ID graph (e.g., LiveRamp RampID) to connect web, app, CTV, and offline identifiers into an activation-ready, pseudonymous ID so you can recognize buying-group members across devices and channels. Identity is the spine of B2B addressability.
• Activation & controls. In your DSP, enable account-level controls where available. The Trade Desk’s iABM is a good example: you can target named accounts, regulate frequency at the account level, and read reach/penetration by account to manage coverage across the whole buying group.

💡 For a plain-English refresher on DSPs/SSPs and auctions, see AI Digital’s overview Demand-side platform (DSP): how it works, benefits, and examples.‍

Now that accounts are addressable, the next job is what to show them, where, and when.

Step 3: Deliver personalized experiences

This step involves turning account context into sequenced creative that meets each buying group where they are:

• Map creative to stage and role. Early-stage messages introduce the problem and your category; mid-stage assets educate and differentiate; late-stage content proves ROI, security, and peer validation. Keep roles in mind: technical evaluators want architecture and integration details; economic buyers want business outcomes.
• Use the right channels for the moment. CTV is an attention driver for named accounts, especially when paired with video/display on the open web and LinkedIn for lower-funnel interaction.

With delivery humming, you’ll get signal fast. The final step is using that signal to learn and reallocate continuously.

Step 4: Optimize through AI feedback loops

Finally, you’ll need to close the loop between media, on-site behavior, and pipeline so your program gets sharper each week:

• Instrument for account-level learning. Ensure L2A so site leads and visitors resolve to the right account in your CRM/CDP. Track reach and frequency by account, time-on-site and content depth from target accounts, and stage movement into opportunities.
• Let identity guide budget shifts. Where your DSP exposes account-level metrics (e.g., iABM), reallocate spend from saturated accounts to under-reached ones, and increase frequency for roles you haven’t reached yet. Account-level pacing prevents over-serving a few users while the rest of the buying group sees nothing.
• Use diagnostics to fix real gaps. If a segment stalls mid-funnel, swap to proof assets (case studies, comparison guides). If engagement is high but no meeting, route a sales task. If match rate dips on a channel, revisit identity inputs. Industry guidance highlights that traditional DSP reporting was built for people-level metrics; ABM add-ons and partner solutions now link performance to account-level outcomes to address that gap.

💡 For a perspective on how AI is being embedded into DSP decisioning, see AI Digital’s take on AI in DSPs.‍

⚡ Programmatic ABM is a closed system—named accounts in, identity-driven activation and sequencing through, and account-level measurement out—so every iteration tightens your coverage of the buying group and your line of sight to revenue.‍

How to get started with programmatic ABM

A practical rollout plan keeps teams aligned and avoids wasted effort. Think of the path in five moves—agree the revenue plan, check your data/tech spine, run a small but telling pilot, scale with automation, then measure and refine against account outcomes.

1. Align on a shared revenue strategy with sales

Start by agreeing who you’re going after and how success will be judged. 

Co-define target accounts, buying groups (primary roles and influencers), stage definitions, and exit criteria. 

Map KPIs to sales-recognised outcomes—pipeline created, win rate, deal velocity—and use a shared framework such as Forrester’s B2B Revenue Waterfall so marketing’s contribution shows up in the same units sales cares about (buying groups, opportunities, revenue). 

Establish a weekly cadence to review account progress and decide next best actions jointly with sales.

2. Audit data and technology stack

Before launching, confirm your identity and data foundations are ready:

• First-party data and consent: CRM integrity, domain and contact hygiene, and lead-to-account matching (L2A).
• Identity resolution: you’ll need to translate emails, MAIDs, and cookies into a pseudonymous activation ID that works across channels (e.g., an enterprise ID such as RampID) and supports account-level reporting. LiveRamp’s resources explain how to connect known (PII) and unknown (device) identifiers safely for activation.
• Platform readiness: confirm list-upload workflows (e.g., LinkedIn Matched Audiences), event taxonomies, and basic QA dashboards (match rate, coverage, frequency by account).

3. Launch pilot campaigns

Pick a tiered list (for example, Tier 1: 50 strategic accounts; Tier 2: ~200) and run a 4–8 week pilot across LinkedIn + open web (display/native/online video). 

⚡ Hold something out. A small control set of look-alike accounts turns opinions about lift into facts you can show finance.

Where budget allows, add CTV to test lift from cross-channel exposure; recent analyses show that pairing CTV with display improves on-site engagement from named accounts. 

Define a tight measurement plan—coverage and frequency by account, account-level site engagement, meeting/demo rates, and opportunities sourced—so you can learn and decide what to scale.

4. Scale with automation

Once you see signal, expand beyond the pilot:

• Controls and orchestration: introduce account-level frequency/pacing in your DSP, enable dynamic creative for role/stage variants, and use automated audience rules (e.g., intent surge → add to active list).
• Supply quality, without stack lock-in: if you want DSP-agnostic, outcome-based selection, AI Digital’s Smart Supply can sit alongside your existing stack. It focuses on your KPIs (not generic trends), neutralises inventory bias, reduces bid-stream recycling, applies IVT protection and brand-safety screens, and builds custom Deal IDs that are optimised continuously.

⚡ Our POV—opt for outcome-based supply. Curate deals around your KPIs and keep your stack DSP-agnostic so you can improve quality without lock-in. 

5. Track and refine performance

Report in account terms: reach and penetration by account, stage transitions, pipeline contribution, win rate, and deal velocity. 

Add diagnostics—match rate, coverage by role/seniority, path-to-site behaviour from targeted accounts, and cost per opportunity / cost per revenue dollar. Use these insights to:

• Reallocate budget from saturated accounts to under-reached ones,
• Swap creative to proof assets when accounts stall mid-funnel, and
• Trigger sales actions when engagement spikes without a meeting.

You’re running an always-on loop—targeted in, identity-driven delivery through, revenue-aligned measurement out—so each cycle sharpens coverage of the buying group and your line of sight to revenue.

Common pitfalls to avoid in programmatic ABM

Even well-planned programs can wobble if a few fundamentals are ignored. Use these patterns as guardrails rather than a checklist.

• Weak sales–marketing alignment: If marketing runs ABM without sales at the table, you get engagement with no follow-through—or sales outreach to accounts the media plan isn’t supporting. Fix it by sharing ownership of the target list, agreeing stages and exit criteria, and reviewing progress together on a set cadence with shared, revenue-aligned KPIs.
• Too many—or the wrong—accounts: Spreading budget across thousands of logos dilutes impact; picking accounts on hunches wastes it. Start with a narrow, defensible list built from ICP fit, objective data (intent, technographics, look-alike models), and clear tiering. Expand only when you see signal.
• Generic messaging masquerading as personalization: If your ABM creative could run to anyone, it isn’t ABM. Ground messages in segment-specific pains and outcomes, reflect industry language, and vary proof by role (finance vs. technical). Even light-touch relevance beats recycled brand banners.
• Disconnected data spine: Programmatic ABM runs on clean, connected data. If CRM, marketing automation, identity, and ad platforms aren’t integrated, you can’t see which accounts progressed—or why. Prioritise lead-to-account matching, consistent taxonomies, and an attribution approach you’ll actually use.
• Optimising to the wrong scoreboard: Clicks and CPMs are directional at best. ABM success is account coverage, buying-group engagement, stage movement, opportunities, win rate, and velocity. Build an ABM scorecard and give programs enough runway to influence long B2B cycles.
• Underestimating content and creative refresh: You’ll need assets by stage and role. One banner on repeat leads to fatigue fast. Plan a simple content matrix up front, repurpose what works, and set review points to update headlines, formats, and proof.

⚡ Mind the creative shelf life. Even perfect targeting fatigues fast if creatives don’t rotate—refresh cadence should be on the plan.

• Team not enabled for an account-first model: If the plan isn’t understood, execution suffers. Sales may worry about fewer leads; marketing may ignore account-level reporting features. Share the strategy, show early wins, and train the team on tools, workflows, and how decisions get made in ABM.

Build these safeguards into your operating rhythm and you’ll keep the program focused on what matters: reaching the right accounts, moving buying groups forward, and proving impact in pipeline and revenue.

The future of programmatic ABM

Before wrapping up, it helps to name the big currents that will shape how account-based media gets planned, bought, and measured. In our view, three forces matter most—and they’re already visible in market data and platform roadmaps.

Omnichannel account reach becomes table stakes

Account lists won’t stay confined to web feeds and banners. They’re now portable to the biggest screen in the house. ABM-specific CTV solutions let you deliver ads to smart-TV environments using the same named-account data and attribute results back to those accounts.

For example, Demandbase Connected TV supports account-targeted CTV and ties performance to pipeline. You’ll also find CTV options inside other ABM platforms—for instance, AdRoll’s ABM packages include CTV delivery through streaming apps and devices (e.g., Roku, Apple TV), so account-based campaigns can span both web and television with consistent targeting. The above-mentioned LinkedIn’s CTV launch in April 2024 is another route, but it’s now one of several ways to get account-level reach on TV and feed those exposures into your ABM measurement. 

What this changes: upper-funnel attention for named accounts becomes planned and measurable, not a hopeful by-product. Teams can schedule CTV to raise familiarity inside target firms, then let open-web video and display carry the mid-funnel follow-through, all reported in account terms.

Our view at AI Digital: CTV will become a routine line in ABM media plans. Expect account-level reach and frequency to appear more natively in buying tools so planners can balance exposure across the buying group—on TV and the web — with one scoreboard.‍

Identity and measurement mature beyond cookies

Even as Chrome continues to allow third-party cookies for now, the center of gravity has moved to privacy-safe, interoperable identity and a mix of privacy-enhancing technologies (PETs). The IAB Tech Lab’s addressability program and Privacy Sandbox workstreams codify that shift, while identity graphs like LiveRamp RampID document how pseudonymous, people-based IDs link devices and de-identified PII for activation and measurement across channels (including CTV).

What this changes: account recognition becomes more durable and auditable across devices and channels, and revenue-grade measurement (tying impressions to buying groups and opportunities) becomes a realistic default rather than a special project.

Our view at AI Digital: Plan for a portfolio approach to addressability: first-party data with consent, enterprise IDs (e.g., RampID), clean PETs patterns, and account-level reporting in DSPs. Even if cookies linger, the winners will be the teams that measure in account terms and can prove coverage, progression, and pipeline impact without fragile user-level syncs.

Revenue platforms consolidate ABM and MAP

The stack is converging. Forrester’s 2024 Wave for B2B revenue marketing platforms evaluates vendors on data management, buying-group support, omnichannel orchestration, and lifecycle measurement—a clear signal that ABM, marketing automation, and analytics are fusing into one operating layer for revenue teams. Forrester’s own commentary describes the rise of these platforms and why they matter.

What this changes: ABM won’t feel like a bolt-on campaign. It becomes the default operating model, with buying-group entities, opportunity-level metrics, and activation hooks living in the same system that powers email, ads, and sales alerts.

Our view at AI Digital: Over the next 24 months, we anticipate:

• Account-level metrics (reach, frequency, penetration) surfacing as first-class objects in major DSPs and analytics suites.
• CTV + web + social planned as one account-sequenced journey, not separate buys, with unified pacing against buying groups.
• Identity portfolios (first-party + enterprise IDs + PETs) becoming a procurement topic, not just a marketing ops concern.
• Creative intelligence layers—AI systems that assemble approved copy/visual components into role- and stage-specific variants—sitting between the DAM and the ad server so personalization scales without chaos.
• Outcome-based supply curation (like our Smart Supply approach) used to stabilize quality and cost while keeping stacks DSP-agnostic.

In short, the next era of programmatic ABM will be account-first, identity-aware, and revenue-literate by design. As channels broaden (CTV included), the stack consolidates, and identity gets sturdier, the practical job for B2B teams is clear: plan around buying groups, measure in revenue terms, and let automation handle the cross-channel orchestration.

💡 For a deeper dive into GenAI’s impact on media, see the transformative power of GenAI in creative & media strategy.‍

Conclusion: How ABM powers the future of programmatic advertising

Programmatic has always been efficient at buying impressions. Programmatic ABM makes those impressions accountable to revenue by focusing on named accounts, aligning delivery to buying groups, and measuring progression through pipeline. With modern identity resolution, account-level controls, and cross-channel activation (including CTV), B2B teams finally have the precision engine sales has wanted for years—coverage where it matters, cadence that fits long buying cycles, and metrics the board recognises.

Four practical takeaways to apply this quarter

1. Pick your “Tier 1 fifty.” Sit down with sales, lock a 50-account Tier 1 and a 200-account Tier 2, and agree objectives for each group.
2. Light up web + social + CTV for those accounts. Upload company lists, resolve identities, and run sequenced creative by stage so every role in the buying group sees the right message at the right moment.
3. Report like a revenue team. Track account reach, stage movement, pipeline, and cost-per-opportunity using a buying-group model (e.g., the Revenue Waterfall) so marketing and sales read the same scoreboard.
4. Redirect waste. Use account-level reach and frequency to pull budget from saturated accounts and increase coverage on under-reached ones.

If you want help putting this into practice, reach out to AI Digital. We can pressure-test your target list with sales and stand up identity and measurement the right way.