# Playbook M2. Accounts, Ads and Events

*The question: We chose them. How do we reach them as individuals?*

> **Questions this playbook answers**
> - We have a target account list. What should each person in each account see, hear, and receive?
> - How personal can paid ads be, and what do we actually control when the platform's AI runs targeting and creative?
> - What should happen before, during, and after a conference so the follow-up reflects the conversation that actually took place?
> - When is the right move to show nothing, invite no one, and send nothing?
> - How do we prove account-based work caused pipeline when the list is only a few hundred accounts?

## The moment

Larkspur Systems (a fictional company, a composite of many real ones) sells field-service and fleet software. This year it chose 300 target accounts for a mid-market push: large field-service operators that are not yet customers, picked by sales from the CRM and a purchased firmographic file. For eight weeks, LinkedIn ads ran against the list with one creative, "Field service, simplified." Then came the big moment of the quarter: a three-day booth at an industry trade show (fictional, like everything else here).

Marcus runs operations at a utility contractor with about 600 vehicles, one of the 300. He stopped at the booth on day two and spent forty minutes with Larkspur's solutions engineer. He described a dispatch migration from a competitor that had failed overnight, the two weeks his technicians spent on paper, and the reason he now distrusts any vendor that promises a "seamless cutover." The engineer promised to send a reference customer who had migrated at similar scale. She typed three lines into a notes app on her phone.

The show produced 1,400 badge scans. Two days later, all 1,400 received the same email: "Thanks for stopping by booth 412! Here is our e-book on field-service efficiency." Marcus received it. So did a student who scanned for a tote bag. The reference customer never arrived, because the promise lived on one phone. The ads kept running "Field service, simplified" to a man who had just explained why simplicity claims make him leave the room.

Larkspur had chosen Marcus's account by name, paid to reach it, and heard its most important problem in person. Then it treated him like everyone else.

## The decision

Account-based marketing (ABM) is the practice of choosing specific accounts and coordinating marketing and sales around each one. The industry benchmark distinguishes three depths: one-to-one programs, typically covering 5 to 100 accounts; one-to-few, 20 to 200; and one-to-many, 500 to 2,500 [1]. Most programs now run more than one type, and the median program covers about 250 accounts [1].

Here is the sentence this playbook turns on: **in account-based work, the account is the unit of decision and the person is the unit of message.** You decide to pursue a company. You never speak to a company. You speak to Marcus, to his CFO, and to the dispatcher who will use the product, and each of them needs a different reason to care.

![Left, the account as the unit of decision: pursue now, nurture later, or leave alone, plus a tier that is a budget for depth. At Larkspur (fictional), 30 accounts get a named rep, full research and human-approved plays; 120 get clustered, generated briefs reviewed by sampling; 150 get programmatic ads and honestly general content. Right, the person as the unit of message: Marcus in operations, his CFO, and the dispatcher who will use the product each get a different reason to care, and a buying-group member whose colleague is already talking to us hears nothing for now. Every moment (ad exposure, event invitation, meeting request, follow-up) includes silence as an option.](/images/handbook/pb-m2-account-decides-person-hears.svg)

*Figure M2.1. The account is the unit of decision and the person is the unit of message; "nothing" is a real option at every layer.*

So the decisions come in layers, each with "nothing" as a real option (Chapter 14):

- **Account:** pursue now, nurture later, or leave alone. An existing customer in an open escalation, or an account in live negotiation with sales, may need less marketing, not more.
- **Tier:** how much depth each account earns. Tiers are a budget for research and human time, not a status symbol.
- **Person:** who in the buying group hears from us, in which channel, and who hears nothing because a colleague is already in conversation.
- **Moment:** ad exposure, event invitation, a meeting request, a follow-up, or silence.

The decision is not "which accounts get the campaign." It is what changes for this person because we chose their account, compared with doing nothing (Chapter 6 prices that change).

## What you need to know

ABM runs on account memory: one record per company with its people and their relationships, not a list of contacts sharing a domain (Chapter 9). Three shapes matter.

**The account and its buying group.** Typed properties on the account (tier, fit score, customer status, open opportunities, active holds) and dated relationships to people: who is the economic buyer, who champions, who uses, who blocks. Roles change; a buying-group map with no dates is a guess about the past (Chapter 11). In the self-hosted memory system I built, the buying group is a graph, not a column: "works at" and "reports to" edges are inferred from saved content, so an agent can ask who else sits at this account before writing to anyone. Records resolve by whatever identifier a source carries (email, CRM id, or a key you define), because the badge scanner, the ad export and the CRM each name Marcus differently. A scan with a personal email address is weak identity; it must not inherit the email provider's firmographics.

**Evidence of what each person cares about.** Here the signals differ sharply in authority, and ranking them is most of the work (the Signal Ladder in Chapter 7):

| Signal | What it proves | Authority |
|---|---|---|
| A conversation (booth, call, meeting) | What this person said they are dealing with | Highest, if captured with source and date |
| First-party behavior (site visits, content, product trials) | Someone at the account engaged, sometimes who | Medium |
| Ad engagement | Someone at the account saw or clicked an ad | Low; account-level at best |
| Third-party intent data | Some unnamed people at the domain read related content somewhere | Lowest; treat as a hint to research, never as a fact to quote |

**Commitments we made.** Marcus was promised a reference customer. That promise is not a note; it is a typed obligation with an owner and a due date, readable by every agent and person that touches the account (Chapter 18). Conversations produce promises, and promises decay silently unless the system treats them as state.

The richest fact Larkspur held about Marcus was the forty-minute conversation. It was the only fact that never reached the system. Events generate the highest-authority signal in account marketing and usually store it in the lowest-fidelity place. Capture is a design problem: a short voice note after each conversation, transcribed and extracted against a schema (pains, current vendor, timeline, promises made, who was present), with the original preserved (Chapter 8). In the memory system I built, the transcript is saved first and properties are extracted onto the linked records afterward; if extraction fails, the transcript survives.

## The action

### Tiers: AI moves depth down the list

The benchmark data describes the old economics. One-to-one programs stay small because deep account research takes skilled hours. In the same 2023 study, practitioners rated their own use of generative AI at 1.9 on a five-point scale, the lowest of any tool category [1].

What changes (inference, not yet measured at scale): researching an account and drafting a brief per buying-group member now costs minutes of compute rather than hours of a person, so a one-to-one-style brief becomes affordable for one-to-few and even one-to-many accounts (Chapters 4 and 5). What does not change is the channel's tolerance for personal claims. Depth of understanding can move down the list; depth of what you say out loud should follow the channel.

At Larkspur the tiers became: 30 accounts with a named rep, human-approved plays, and full research; 120 accounts in industry-and-situation clusters with generated briefs reviewed by sampling; 150 accounts in programmatic coverage with ads and honestly general content until they show first-party engagement.

Write each tier's depth down as a contract. In the governed personalization engine I built, every campaign declares its mode before generation (company name only, personalized to the person, or an internal scored brief), and quality review judges output against that contract. When evidence is thin, output steps down: person and account, account, segment, approved static copy, nothing. Specificity degrades before trust does.

### Paid ads: you control the list, the exclusions, the creative set, the destination, and the signal

Paid media is where personalization claims most often outrun reality. Be precise about who controls what.

**The platform controls delivery.** LinkedIn will not serve an ad set to an audience smaller than 300 matched members [2]. That floor is a privacy boundary as much as a technical one: you cannot run an ad to Marcus alone. Meta and Google are pushing further toward automation. Google's AI Max for Search generates headlines and descriptions from your landing pages and routes people to the page it judges most relevant; Google reports advertisers "typically see 14% more conversions or conversion value at a similar CPA/ROAS," based on its internal data [3]. Meta says Advantage+ shopping campaigns average $4.52 return on ad spend, with 22% higher returns than advertisers not using its targeting [4]. Both are vendor-reported figures. In mid-2025 the *Wall Street Journal* reported Meta's aim to fully automate ad creation and targeting by the end of 2026, with Mark Zuckerberg describing a future in which "you don't need any creative, you don't need any targeting demographic" [5].

**You control five things.** The account and contact lists you upload. The exclusions. The set of creative you allow (by tier, industry, and stage, never by person). The page the ad lands on, which can be personalized per account far more safely than the ad itself (the *Marketing I* playbook; Chapter 16 on governed pages). And the conversion signal the platform optimizes toward. The engine I built is deliberately not an ad-buying system; it works on this owned side and leaves delivery to the platform. Choosing the landing page from the campaign context carried in the ad's link (persona, industry, region, theme, offer) is the design for my next pilot, not a result; a tracking parameter identifies a campaign, never a person (the *Marketing I* playbook).

That last one matters most. An ad platform personalizes to its model of the person, not yours. The most important personalization input you give it is not the creative. It is the definition of success. Optimize to form fills and the platform will find people who fill forms. Send back qualified opportunities from your CRM, and it learns something closer to what you want, with a lag and less volume. This is the Proxy Trap from Chapter 21, running inside someone else's model.

**Keep personal facts out of ads.** An ad cannot explain how you know what it implies you know. Aguirre and colleagues found that personalization raised click-through when data collection was overt and lowered it when collection was covert, with recipients reporting more vulnerability [6]. A banner saying "Still recovering from that failed migration?" to Marcus's colleagues is accurate and unacceptable. Relevance in ads comes from situation and industry, not from what someone told you at a booth.

### Events: before, during, after

The same decision logic applies at three moments, with different latency (Chapter 17).

![Three event moments on three clocks. Before (async, days): decide whom to invite starting from target accounts, send a meeting request with one researched reason, and give booth staff a brief the night before covering role, stage and what not to raise. During (real time, seconds): when a badge is scanned, answer three questions before anyone speaks (target, customer, or neither; open escalation or negotiation; a colleague already in conversation), then record a voice note after each real conversation. After (async, hours to days): follow-up is ranked on what was captured; a conversation with a target account gets a rep follow-up that keeps every promise, a conversation with a customer goes to the account manager, and a scan with nothing else gets an honestly general note or nothing.](/images/handbook/pb-m2-event-before-during-after.svg)

*Figure M2.2. Before, during, after: a badge scan is proof of proximity, not of interest.*

**Before (async, days).** Decide whom to invite and whom to leave alone, starting from target accounts and open opportunities rather than the whole database. For registered attendees at target accounts, the rep sends a meeting request with one researched reason that is specific and true, or honestly general. The night before each day, booth staff receive a one-page brief per expected target-account visitor: role, stage, open issues, what not to raise. In the engine I built this is a seller playbook (snapshot, why-now signals, talking points) compiled from the same account context as the landing page and the follow-up, so they do not contradict each other. Rep-internal intelligence is not customer copy: a guess about the incumbent belongs in the brief, never in the email.

**During (real time, seconds).** When a badge is scanned, the system answers three questions before anyone speaks: is this a target account, a customer, or neither; is there an open escalation or active negotiation; is there a colleague already in conversation? A customer with a priority-one ticket should hear "how is the fix holding?" and not a pitch. After each real conversation, the staff member records the voice note.

**After (async, hours to days).** The follow-up is a decision ranked on what was captured, not on the fact of a scan:

- **A real conversation with a target account:** the rep sends a follow-up drafted from the note, honoring every promise made, grounded only in what was said (Chapter 15).
- **A conversation with a customer:** routed to the account manager, not the campaign.
- **A scan and nothing else:** an honestly general note, or nothing. Never "great chatting with you," which asserts a conversation the system cannot prove.

A badge scan is proof of proximity, not of interest.

Speed matters, but the evidence for event follow-up timing is thin. The well-sourced result on response speed concerns inbound web leads [7]; I have not found a rigorous study of trade-show follow-up timing. The widely repeated line that "80% of trade show leads are never followed up" is commonly attributed to the Center for Exhibition Industry Research, but I could not locate the original study.

## What can go wrong

### Failure story: The Ad That Could Not Hear

A composite. After a painful outage, Larkspur's account manager promised a customer's operations director no sales or marketing outreach until the post-incident review closed. Larkspur had learned this lesson before (Chapter 18), so the promise became a typed outreach hold that every email and in-app agent read before acting. The agents went quiet.

The ads did not. The customer was also on an expansion audience, uploaded to LinkedIn from a weekly CRM export that did not include holds. For three weeks the operations director and her team saw "Routes that never drop" in their feeds, during an outage about dropped routes.

Nothing malfunctioned. The paid channel was simply never treated as a send path. The fix: exclusion audiences generated from the same suppression state as email, synced on every change rather than weekly, with a check that the platform has processed the update. Every send path is a send path, including the one you pay for.

### Governance risks

- **Invented familiarity.** Generated follow-ups that imply a conversation, a need, or a meeting the record cannot support. Ground every personal claim in captured evidence or go general (Chapter 15).
- **Five teams, one person.** ABM plays, an AI SDR sequence, event follow-up, and customer-success check-ins can all target Marcus in the same week. A per-person contact budget and one arbitration point decide (Chapter 14; the *One Customer, One Conversation* playbook).
- **Audience files are personal data.** Hashing emails before upload does not make them anonymous; the FTC has said so directly [8]. Deletion and objection must propagate to every uploaded audience (Chapter 19).
- **Regulatory lines on targeting.** In the EU, the Digital Services Act bars online platforms from showing ads based on profiling that uses special categories of personal data (Article 26(3)) and requires them to show users the main targeting parameters (Article 26(1)(d)); profiling-based ads to minors are barred under Article 28 [9]. Your audience definitions should never encode a special category, including by inference (Chapter 19). This is not legal advice; check current rules for your markets, as of your launch date.

### Honest limits

AI does not fix account selection. A beautifully researched brief for an account that will never buy is waste at a lower unit cost.

The most cited ABM evidence is self-reported. In the 2023 Momentum ITSMA benchmark, 81% of respondents who answered said ABM's ROI beats other marketing, yet only 52% said their company measures ABM ROI at all, and only 21% reported significant (over 10%) revenue growth they attributed to ABM [1]. I read that as a discipline with strong conviction and weak measurement, not as proof. Figures like "171% more pipeline" circulate without a traceable source; leave them out.

Platform performance claims deserve the same treatment. They are measured by the seller, on the seller's definitions.

## How you will know

Start with how badly ad measurement goes without experiments. In large randomized experiments at Facebook covering 1.6 billion impressions, Gordon and colleagues found that observational methods usually overestimated advertising effects relative to the randomized result, sometimes underestimated them, and did not reliably recover the truth [10]. At eBay, Blake, Nosko and Tadelis found brand-keyword search ads had no measurable short-term benefit, and non-brand ads paid mostly for frequent buyers who would have come anyway [11]. And Lewis and Rao showed why even good experiments struggle: across 25 large field experiments, the median confidence interval on ROI was more than 100 percentage points wide [12].

So the design follows Chapter 21, randomized at the unit of influence, which in ABM is the account:

- **Account-level holdout.** Before launch, randomly hold back a share of the target list from ads and plays (sales still works them normally). Compare opportunities created, win rate, and deal size between treated and held-out accounts. Never compare engaged accounts with unengaged ones; engagement is selection, not effect.
- **Be honest about power.** Detecting an opportunity rate moving from 10% to 15% at conventional thresholds (5% significance, 80% power) needs about 680 accounts per arm. A 300-account program cannot prove a five-point lift in one quarter. Pool across quarters, measure larger effects, or say plainly that the program runs on judgment plus directional evidence.
- **Choose hypotheses before you generate.** Four personas, six industries, five regions, eight message themes and three offers make 2,880 contexts (an illustrative campaign) a generative system can write for. That is expressive capacity, not experiment volume. A 300-account list supports two or three tested hypotheses a quarter.
- **Platform lift studies are useful and not neutral.** Use them, and label them as the platform measuring itself.
- **Events:** among scans with a captured conversation, randomize follow-up treatment (researched rep follow-up versus the standard one) and compare meetings and opportunities within 90 days. Track promise fulfillment: the share of commitments made at the booth that were met on time.

**Metrics to watch:** opportunities and pipeline per target account versus holdout; win rate in the treated tier; promise fulfillment rate; share of follow-ups grounded in captured evidence; suppression propagation lag to ad platforms.

## Reader Q&A

**Should we let the platform's AI write and target our ads?** For one-to-many coverage, often yes, inside an approved creative set and with a conversion signal you chose deliberately. Keep your exclusions, your claims policy, and your definition of success in your own hands.

**Can we name the account in the ad?** Naming a company can read as surveillance, and the platform floor means colleagues and others will see it too. Put account-level specifics on the landing page, where the visitor chose to arrive, and never name a person.

**Is third-party intent data worth buying?** As a hint for where to research, sometimes. As a fact to quote to a buyer, never. Measure it like any vendor claim: does it predict opportunities in your holdout better than your own first-party signals?

**Do we have to label AI-generated ad creative?** As of September 2026, the EU AI Act's Article 50 transparency duties apply from 2 August 2026: generative providers must mark synthetic output in machine-readable form, and deployers must disclose deepfakes [13]. Ordinary generated marketing copy is not generally caught, but platforms have their own labeling rules. Check both, per market; not legal advice.

## For your AI

```yaml
playbook: M2
title: "Marketing II: Accounts, Ads and Events"
question: "We chose them. How do we reach them as individuals?"
concepts:
  - name: Account as Decision Unit, Person as Message Unit
    definition: "Pursuit and tier are decided per account; every message is written for one role in the buying group."
  - name: ABM Tier as Depth Budget
    definition: "One-to-one, one-to-few, one-to-many tiers allocate research and human time, not status; AI lowers research cost so depth can move down tiers while channel tolerance limits what is said."
  - name: Conversion Signal as Personalization Input
    definition: "On automated ad platforms, the success event you send back shapes targeting more than the creative does."
  - name: Proximity Is Not Interest
    definition: "A badge scan proves someone was near the booth; only a captured conversation justifies a personal follow-up."
  - name: Promise as State
    definition: "Commitments staff make to a person at an event or meeting are typed obligations with owner and due date, readable by every agent."
  - name: Personalization Mode as Tier Contract
    definition: "Each campaign declares its depth before generation; QA judges against it, and thin evidence steps output down (person, account, segment, static, nothing)."
decision_rules:
  - if: "an event contact's email address is a personal (freemail) domain"
    then: "treat identity as weak; never attach the provider's firmographics; write at lower specificity"
  - if: "an account has an open escalation, legal hold, or active negotiation"
    then: "suppress acquisition and expansion ads and plays; sync the exclusion to every ad platform on change"
  - if: "an event contact has only a badge scan and no captured conversation"
    then: "send an honestly general note or nothing; never imply a conversation"
  - if: "a captured conversation includes a promise"
    then: "create a typed commitment with owner and due date; block generic follow-up until it is honored or reassigned"
  - if: "a personal fact was learned in conversation or inferred"
    then: "never use it in ad creative; ads carry industry and situation relevance only"
  - if: "the ad platform optimizes to a proxy (form fill, click)"
    then: "send qualified-opportunity conversions back instead, accepting lag and lower volume"
  - if: "the target list is too small to detect the expected lift in one quarter"
    then: "pool across quarters or state that results are directional; do not present engaged-vs-unengaged comparisons as lift"
assessment_questions:
  - "How many target accounts are in each ABM tier, and what research does each tier receive?"
  - "Where do booth and meeting conversations get recorded today, and can an agent read them within a day?"
  - "Which conversion event do your ad platforms optimize toward?"
  - "Do suppression holds and opt-outs propagate to uploaded ad audiences, and how fast?"
  - "Is there a random holdout of target accounts, and how many accounts per arm?"
patterns: [Account-Level Holdout, Suppression-Synced Audiences, Evidence-Ranked Event Follow-Up, Tier as Depth Budget]
anti_patterns: [The Ad That Could Not Hear, Badge Scan as Intent, Engaged-vs-Unengaged as Lift, Personal Facts in Ad Creative, Promises in a Notes App, Design Space as Test Plan]
links: {data: [7, 8, 9], memory: [10, 11], decisions: [14, 15, 17], trust: [18, 19], proof: [21], playbooks: [M1, S1, P9]}
maturity_dimension: decisioning
```

## References

1. Momentum ITSMA and ABM Leadership Alliance (2023-11). *Rethinking ABM: Outperforming the Market in the World of AI*, 2023 ABM Benchmark Study (7th annual). https://5356237.fs1.hubspotusercontent-na1.net/hubfs/5356237/MomentumITSMA_ABMLA2023_GlobalBenchmarkReport.pdf
2. LinkedIn Marketing Solutions Help, "Upload company and contact targeting lists for LinkedIn Ads" (accessed 2026-09-26). https://www.linkedin.com/help/lms/answer/a421822
3. Google (2025-05-06). "AI Max for Search campaigns." https://blog.google/products/ads-commerce/google-ai-max-for-search-campaigns/ (vendor-reported, internal data).
4. Meta, "Advantage+ shopping campaigns" (accessed 2026-09-26). https://www.facebook.com/business/ads/meta-advantage/advantage-plus-shopping-ads (vendor-reported).
5. Marketing Dive (2025-06), reporting the *Wall Street Journal* of 2025-06-02: "Meta plans to enable fully AI-automated ads by 2026." https://www.marketingdive.com/news/meta-plans-to-enable-fully-ai-automated-ads-by-2026/749613/
6. Aguirre, E., Mahr, D., Grewal, D., de Ruyter, K., Wetzels, M. (2015). "Unraveling the personalization paradox." *Journal of Retailing* 91(1). https://www.sciencedirect.com/science/article/abs/pii/S0022435914000669
7. Oldroyd, J., McElheran, K., Elkington, D. (2011-03). "The Short Life of Online Sales Leads." *Harvard Business Review*. https://hbr.org/2011/03/the-short-life-of-online-sales-leads
8. FTC Office of Technology (2024-07-24). "No, hashing still doesn't make your data anonymous." https://www.ftc.gov/policy/advocacy-research/tech-at-ftc/2024/07/no-hashing-still-doesnt-make-your-data-anonymous
9. Regulation (EU) 2022/2065 (Digital Services Act), Articles 26 and 28. https://www.cms-digitallaws.com/en/dsa/article-26/ ; https://www.cms-digitallaws.com/en/dsa/article-28/
10. Gordon, B. R., Zettelmeyer, F., Bhargava, N., Chapsky, D. (2019). "A Comparison of Approaches to Advertising Measurement: Evidence from Big Field Experiments at Facebook." *Marketing Science* 38(2):193-225. https://pubsonline.informs.org/doi/10.1287/mksc.2018.1135
11. Blake, T., Nosko, C., Tadelis, S. (2015). "Consumer Heterogeneity and Paid Search Effectiveness: A Large-Scale Field Experiment." *Econometrica* 83(1):155-174. https://onlinelibrary.wiley.com/doi/abs/10.3982/ECTA12423
12. Lewis, R. A., Rao, J. M. (2015). "The Unfavorable Economics of Measuring the Returns to Advertising." *Quarterly Journal of Economics* 130(4):1941-1973. https://academic.oup.com/qje/article-abstract/130/4/1941/1914592
13. EU AI Act (Regulation (EU) 2024/1689), Article 50, and Commission FAQ. https://artificialintelligenceact.eu/article/50/ ; https://digital-strategy.ec.europa.eu/en/faqs/transparency-obligations-under-article-50-ai-act