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Guides marketers through ABM program design, pipeline attribution, and sales-marketing alignment decisions; trigger when working on account selection, attribution models, lead qualification frameworks, or pipeline accountability metrics.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Guidance for planning and executing ABM field events—including dinners, lunches, activations, and suite sponsorships—to build pipeline and deepen relationships with target accounts
Guidance for planning, executing, and measuring account-based marketing (ABM) programs in B2B; trigger when a user asks about ABM strategy, target account selection, ABM measurement, account-level personalization, or sales-marketing alignment for named accounts.
Guidance for selecting, structuring, and managing relationships with marketing agencies, freelancers, and external vendors — trigger when a user is evaluating, hiring, briefing, or managing an external marketing partner.
Guidance for marketing leaders on adopting AI tools, enabling teams, structuring workflows, and navigating the human-vs-AI creative tradeoff in B2B marketing contexts
Guidance for using AI tools to create, repurpose, and distribute B2B marketing content—covering workflows, quality guardrails, tool-specific tactics, and where expert opinion diverges on AI's proper role in content creation.
Guides marketers on how to adopt AI tools responsibly, govern their use, and build organizational capability — trigger when a user asks about AI rollout strategy, team enablement, data privacy, ethical use, or building an AI-ready culture.
| name | abm-pipeline-attribution-alignment |
| description | Guides marketers through ABM program design, pipeline attribution, and sales-marketing alignment decisions; trigger when working on account selection, attribution models, lead qualification frameworks, or pipeline accountability metrics. |
| version | 2026-04-20 |
| episode_count | 24 |
This skill covers account-based marketing program design, pipeline attribution methodology, lead qualification frameworks, and the structural and cultural mechanisms for aligning sales and marketing around shared metrics. All practices are sourced exclusively from Exit Five podcast guests across 24 episodes. Where guests disagree, those disagreements are surfaced explicitly rather than resolved artificially.
Establish account selection as a CRO-level activity with data science input and sales veto power. Start with a data science model identifying good-fit accounts based on firmographic and intent signals, then route to sales leadership for refinement. Sales reps know their accounts intimately—existing contracts, relationship status, competitive situations—and should have final say on which accounts stay on the list. Update the list dynamically throughout the year as circumstances change (e.g., remove accounts with multi-year competitor contracts). (Source: Brian Kotlyar, Episode #331)
Start with trailing six months of CRM data to identify win/loss patterns and revenue trends by account type. Look for accounts where you're generating opportunities but losing deals—this signals interest but messaging or positioning gaps. Layer in industry-specific triggers (companies showing growth, publishing content, raising funding). Then add intent data, but validate it by asking vendors: Do they share or resell this data to competitors? What constitutes a signal—website visits only, or deeper engagement like case study reads or review site visits? (Source: Chris Rack, Episode #150)
Picking the right accounts costs time but is borderline free in terms of money. If budget is constrained, begin by identifying and aligning on target accounts with sales. This alone improves sales productivity and focus. Layer in marketing tactics—digital, events, content—only after you've demonstrated the value of account selection and alignment. (Source: Brian Kotlyar, Episode #331)
Map your total addressable market as a specific list of target accounts. Use this as the shared organizing principle for both sales and marketing. Both teams then measure success by the same metric: which accounts entered pipeline that weren't there before? This eliminates funnel debates and credit disputes, and makes account-based measurement straightforward. (Source: Jaleh Rezaei, Episode #248)
⚠️ Note: Account list size is contested — see Where Experts Disagree.
ABM is a long-term demand-creation play (6–18 months); other marketing tactics are short-term demand-capture plays. Separate these budgets and set different ROI expectations with your executive team. Allocate one budget for demand-capture tactics (paid search, content syndication, paid HQL programs) where you expect quick ROI measurable in weeks. Allocate a separate budget for ABM, where you're building relationships with accounts that may not be in-market yet. Explain to executives that only approximately 5% of your target audience is in-market to buy at any given time in B2B; ABM's job is to be top-of-mind when that 5% becomes interested. This framing prevents pressure to show immediate ABM ROI and allows you to commit to hitting revenue targets 6–18 months out rather than next quarter. (Source: Chris Rack, Episode #150)
ABM ROI is difficult to prove because many tactics (display ads, brand awareness) have weak attribution. Combine tactics with clear, simple attribution pathways alongside brand-building efforts. Use easy-to-attribute tactics like CPL-based lead gen, webinar signups, or event attendance where you can directly track: person downloaded asset → meeting booked → revenue closed. This gives executives confidence in ROI. Then layer in harder-to-measure brand tactics (podcasts, custom creative, thought leadership) that support the overall ABM strategy but don't need perfect attribution. Connect marketing activities in your CRM to closed revenue through your RevOps partner. Set expectations that ABM is a long-term play and measure progress through intermediate signals like response rates and meeting bookings, not just closed deals. (Source: Chris Rack, Episode #150)
Do not involve sellers in high-touch outreach until accounts reach a meaningful engagement stage—visiting product pages, case studies, or pricing pages. Use marketing-only programs to drive awareness and initial engagement first. Once accounts show meaningful engagement signals, then sellers can follow up with re-engagement and qualification efforts. This prevents wasting seller time on cold outreach and improves conversion rates. (Source: Mason Cosby, Episode #186)
View outbound sales sequences, LinkedIn DMs, and direct seller outreach as distribution channels equivalent to email, LinkedIn ads, or search ads. Ensure all outbound sales activity targets only named accounts in your ABM program—no seller should be working accounts outside the defined target list. (Source: Mason Cosby, Episode #186)
The most effective ABM in-person events are those where the salesperson originates the idea, often around hyper-specific pain points or industries that marketing wouldn't have identified. Marketing's role is to support by expanding the attendee list, handling logistics, and providing setup. Sales reps know their accounts best and should drive event strategy. (Source: Drew Pinta, Episode #331)
After any ABM event—dinner, activation, suite—require follow-up to happen the next day, not weeks later. Build this into field marketer and sales rep expectations. Track and report on follow-up rates (e.g., how many accounts had sales reach out after the event). This prevents events from becoming one-off activities and ensures momentum is maintained while the experience is fresh. (Source: Drew Pinta, Episode #331)
True account-based marketing works best with a small, highly focused list. Bring together sales, marketing, customer experience, and executive leadership to brainstorm creative, coordinated outreach specific to each account. ABM by nature is not scalable—attempting to apply the same tactics to hundreds or thousands of accounts defeats the purpose. The goal is creative differentiation that cuts through noise, not volume-based reach. (Source: Chris Rack, Episode #140)
⚠️ Note: Optimal ABM account list size is contested — see Where Experts Disagree.
Explicitly agree at the C-level that fighting over attribution is a waste of time and culturally damaging. Instead, focus on control groups, treatment groups, and progression metrics. This removes friction between marketing and sales and allows teams to focus on what actually drives deals rather than debating credit. Make this a cultural norm from the top down. (Source: Brian Kotlyar, Episode #331)
⚠️ Note: This approach is contested — see Where Experts Disagree.
Assign all named accounts in your ICP to specific sales territories (e.g., 50 accounts per enterprise seller, 200 per mid-market seller). When a meeting is booked with any named account, credit the win to the team regardless of which function—marketing, SDR, or sales—initiated contact. Use cohort analysis (e.g., comparing meeting rates for accounts receiving LinkedIn ads vs. those not receiving them) to measure channel effectiveness without requiring perfect attribution. (Source: Kyle Coleman, Episodes #198 and #123)
⚠️ Note: This approach is contested — see Where Experts Disagree.
Stop tracking "marketing-sourced" vs. "sales-sourced" meetings. Instead, measure total meetings booked with prospects. This eliminates internal credit-claiming and focuses both teams on the shared goal of growing overall pipeline. When marketing does its job well—building awareness, preference, and a warm audience—sales finds it easier to book meetings. (Source: Pranav Piyush, Episode #239)
⚠️ Note: This approach is contested — see Where Experts Disagree.
Create and maintain written documentation that defines what constitutes pipeline readiness, what qualifies as a first touch, and how you measure influence versus attribution. This documentation should be agreed upon by both marketing and sales and used consistently to evaluate whether marketing is delivering qualified pipeline. This is especially important in enterprise sales where opportunities are touched by multiple channels and motions. (Source: Ruth Zive, Episode #175)
⚠️ Note: This approach is contested — see Where Experts Disagree.
For B2B, implement an attribution model that tracks the full sales cycle and identifies the compelling events marketing creates or influences. Use algorithms to weight touchpoints across the funnel journey. Establish a 50/50 attribution agreement with sales to reduce finger-pointing and create accountability. Measure pipeline contribution as a shared metric between marketing and sales, with each team responsible for driving that number. (Source: Kristine Segrist, Episode #277)
⚠️ Note: This approach is contested — see Where Experts Disagree.
Sales and marketing alignment is not about shared vision or credit—it's about explicit contracts. Write down and have both the VP of Sales and VP of Marketing sign agreements on: (1) what defines a good lead; (2) how many leads marketing will deliver and in what timeframe; (3) what each team will do with those leads. For ABM specifically, the contract should define which accounts are targets, what engagement counts as progress, and how sales will work those accounts. Having it in writing creates accountability and prevents scope creep. (Source: Chris Rack, Episode #150)
⚠️ Note: This approach is contested — see Where Experts Disagree.
Be clear about which question you're trying to answer. If you're measuring individual campaign ROI (should we run this campaign again?), that is different from assigning pipeline generation responsibility (which team owns what percentage of pipeline?). You cannot simultaneously optimize for both. Choose the question first, then design your measurement approach accordingly. (Source: Sean Lane, Episode #187)
In enterprise sales cycles, inbound and outbound are inextricably linked. An SDR may outbound to an account with no response for months, but when that same account is targeted with an ad, the contact may click through and be attributed as inbound—yet the outbound motion created the awareness that made the inbound conversion possible. Partner touchpoints, events, and ads all contribute to a single opportunity. Measure influence and attribution holistically rather than crediting a single source. (Source: Ruth Zive, Episode #175)
Store all campaign data and pipeline metrics in Salesforce—where sales lives and works—rather than in HubSpot, even if HubSpot has similar fields. Ensure all campaigns in Salesforce are tagged with campaign value. Pull all metrics from Salesforce into a unified dashboard (e.g., Tableau). This eliminates data disparity between marketing and sales, builds credibility, and respects sales' workflow. Sales should not have to use HubSpot to verify marketing's claims. (Source: Aditya Vempaty, Episode #235)
Rather than relying solely on analytics, directly ask sales reps, customer success teams, and other customer-facing staff to report how prospects and customers first heard about your company during conversations. Record calls (with consent) and track mentions of your content, podcast, events, or brand in those recordings. This qualitative feedback loop provides direct evidence of which brand-building activities are influencing purchase decisions without requiring perfect attribution tracking. (Source: Dave Gerhardt, Episode #307)
Work with sales to establish a shared definition of pipeline (e.g., "identified need and timeline within 12 months"). Once agreed, position marketing as owning pipeline generation across all sources—partner-sourced, sales-sourced, marketing-sourced—rather than fighting over attribution. This eliminates source-based credit disputes and aligns the entire go-to-market function around a single pipeline metric. Marketing's job is to ensure every deal has marketing touchpoints and to report on pipeline progression by account. (Source: Gurdeep Dhillon, Episodes #280 and #203)
⚠️ Note: The right marketing accountability metric is contested — see Where Experts Disagree.
Hold marketing accountable to two metrics at the same point in the funnel: (1) count of qualified opportunities created, and (2) pipeline value of those opportunities. This dual-metric approach ensures marketing generates both sufficient volume to fill sales capacity and enough pipeline coverage to support revenue targets across multiple quarters. Reject lead-count metrics, which can be gamed by purchasing low-quality lists. (Source: Kyle Coleman, Episodes #198 and #123)
⚠️ Note: The right marketing accountability metric is contested — see Where Experts Disagree.
In long sales cycles (12–18 months), do not hold marketing accountable to revenue or even qualified pipeline. Instead, select a metric that marketing directly controls: hand-raisers (demo requests, trial signups, contact form submissions). Build a separate model to track conversion from hand-raiser to closed deal, but recognize that model includes factors outside marketing's control—product, sales, pricing, customer success. This prevents marketing from being held accountable for factors it cannot influence. (Source: Pranav Piyush, Episode #191)
⚠️ Note: The right marketing accountability metric is contested — see Where Experts Disagree.
Create a visual map of the customer journey from initial site visit through revenue generation (e.g., visit → demo → meeting → qualification → close). Identify which stages marketing wholly owns (site visits, demo requests) and which stages marketing influences but doesn't own (qualification, deal closure). Set metrics for the marketing team that reflect both owned and influenced stages, rather than vanity metrics. This prevents the trap of hitting demo targets while deals don't close. (Source: Brian Kotlyar, Episode #118)
⚠️ Note: The right marketing accountability metric is contested — see Where Experts Disagree.
Co-create a definition of qualified opportunities with sales by identifying the intersection of two vectors: Ideal Customer Profile (ICP—company size, industry, etc.) and Ideal Customer Title (ICT—specific roles with buying power). A qualified opportunity requires both a meeting with the right person AND that person having sufficient buying intent from the right account. This forces marketing to target both account fit and role fit, preventing wasted effort on meetings with unqualified personas or accounts. (Source: Kyle Coleman, Episodes #198 and #123)
Stop using terms like "marketing qualified lead" (MQL) and "sales qualified lead" (SQL). These terms create friction between marketing and sales because they're subjective and often disputed. Instead, focus on explicit intent: a prospect who raises their hand and says "give me a demo," "show me pricing," or "let me talk to sales." These are objective, measurable signals that don't require qualification debates. (Source: Pranav Piyush, Episode #130)
⚠️ Note: Whether to eliminate or standardize MQL/SQL terminology is contested — see Where Experts Disagree.
Eliminate lead scoring models that treat form fills and engagement actions as qualified leads. Stop internal debates about what constitutes a lead versus a contact. Only assign leads to sales when they have demonstrated clear buying intent (e.g., filled out a demo request form, reached a specific engagement threshold). Use AI to revisit your scoring model and set a clear engagement threshold before any contact is assigned to the sales team. (Source: Aditya Vempaty, Episode #304)
⚠️ Note: Whether to eliminate or standardize lead qualification frameworks is contested — see Where Experts Disagree.
When leads convert, immediately qualify them on two dimensions: (1) firmographic fit (company size, industry, geography, alignment with ICP), and (2) buying intent signals (what content did they download, did they use the chatbot, did they request a demo). Leads that score well on both dimensions have the best velocity through the sales cycle. This qualification happens at the top of the funnel and informs whether the lead is routed to sales or nurtured further. (Source: Ruth Zive, Episode #175)
Work with the sales team to refine form fields. If you're getting too many unqualified leads, add qualifying fields (e.g., project description with character limits) to help sales pre-filter. If you're getting too few leads, reduce friction. Test calendar visibility and form complexity based on sales capacity and feedback. (Source: Gabby Sellam, Episode #218)
Rather than defaulting to "we need more leads," analyze conversion rates at each stage of the sales funnel to identify where pipeline is falling out. If 60% of pipeline is lost at a particular stage, investigate whether a marketing asset, motion, or sales enablement intervention could improve conversion at that stage. This approach often yields better ROI than simply increasing lead volume, and it requires close collaboration between marketing and sales to diagnose the root cause and test solutions. (Source: Ruth Zive, Episode #175)
Don't stop at measuring MQL volume. Trace the full funnel from lead to revenue to identify where actual breakdowns occur. Common issues include: BDRs taking 5+ days to follow up, new sales reps receiving the same leads as top performers without proper support, or declining close rates. Optimizing these downstream factors often yields better results than generating more leads. (Source: Adam Goyette, Episode #164)
Track whether deals that include interactive demos have shorter sales cycles compared to those that don't, rather than only measuring whether a demo was used. This metric better captures the value of demos in accelerating the buying process by reducing the need for multiple discovery calls. (Source: Natalie Marcotullio, Episode #122)
Conduct a zero-dollar audit of closed-lost deals from a recent period by filtering CRM close-lost reason codes for status quo indicators: unresponsive, budget, no champion, value. Sum the pipeline value of deals matching these filters to quantify total pipeline lost to status quo rather than to competition. Use this number to align sales and marketing around a shared problem and justify messaging or strategy changes. Example: one company found $53M in status quo losses; a 10% improvement yields $5M in recoverable pipeline. (Source: Jen Allen-Knuth, Episodes #343 and #308)
Do not optimize for lead volume. Measuring success by form fills, contacts created, or email opens is a vanity metric that can be gamed and does not reflect business outcomes. (Source: Dave Gerhardt, Episode #304; Kyle Coleman, Episodes #198 and #123)
Do not involve sellers in high-touch outreach before accounts show meaningful engagement signals. Cold outreach to accounts that haven't engaged wastes seller time and reduces conversion rates. (Source: Mason Cosby, Episode #186)
Do not let ABM events become one-off activities. Without enforced next-day follow-up, events generate no pipeline momentum. (Source: Drew Pinta, Episode #331)
Do not allow sellers to work accounts outside the defined ABM target list. All outbound sales activity should target only named accounts in the ABM program. (Source: Mason Cosby, Episode #186)
Do not store marketing metrics in a system sales doesn't use. If sales lives in Salesforce, marketing metrics stored only in HubSpot will not be trusted or acted upon. (Source: Aditya Vempaty, Episode #235)
Do not hold marketing accountable to revenue in long sales cycles without acknowledging the factors outside marketing's control. Product quality, sales execution, pricing, and customer success all affect whether a hand-raiser becomes closed revenue. (Source: Pranav Piyush, Episode #191)
Do not attempt to scale true ABM to hundreds of accounts with the same tactics. Volume-based ABM defeats the purpose of account-based differentiation. (Source: Chris Rack, Episode #140)
Do not default to "we need more leads" without first diagnosing stage-by-stage conversion rates. The bottleneck is often downstream of lead generation. (Source: Ruth Zive, Episode #175; Adam Goyette, Episode #164)
Do not fight over attribution without first agreeing on which question you're trying to answer. Campaign ROI measurement and pipeline accountability measurement are different questions requiring different approaches. (Source: Sean Lane, Episode #187)
Do not build a target account list from a sales wishlist alone. Root the list in CRM data, validated intent signals, and industry triggers—not gut feel. (Source: Chris Rack, Episode #150)
This is a genuine disagreement with five distinct positions across the guest pool.
Position A: Abandon attribution entirely — use control groups and progression metrics Brian Kotlyar (Episode #331) argues that fighting over attribution is culturally damaging and a waste of time. His prescription is to agree at the C-level to stop doing it, and replace attribution with control groups, treatment groups, and progression metrics as the measurement framework.
Position B: Implement a formal 50/50 attribution agreement Kristine Segrist (Episode #277) recommends a formal 50/50 attribution split between marketing and sales, supported by algorithmic touchpoint weighting across the funnel. This creates shared accountability without abandoning attribution measurement.
Position C: Use account-based territories and credit the team, not the source (3 supporters) Kyle Coleman (Episodes #198 and #123) and Pranav Piyush (Episode #239) argue for eliminating source-based credit by assigning named accounts to territories and crediting the team when any meeting is booked, regardless of which function initiated contact. Measure total meetings and pipeline by account, not by source. Use cohort analysis to evaluate channel effectiveness separately.
Position D: Document attribution rules explicitly and maintain them Ruth Zive (Episode #175) recommends creating and maintaining written documentation defining pipeline readiness, first touch, and attribution rules, agreed upon by both teams. This removes ambiguity without abandoning attribution measurement—particularly important in enterprise sales with complex multi-touch journeys.
Position E: Create a formal written contract signed by both VPs Chris Rack (Episode #150) argues that alignment requires explicit written contracts—not cultural norms or measurement frameworks—signed by both the VP of Sales and VP of Marketing, defining lead definitions, delivery commitments, and responsibilities.
Support summary: 3 vs 1 vs 1 vs 1 vs 1 (Position C has the most supporters)
Context dependency: The account-based territory approach (Coleman, Piyush) presupposes a named-account ABM motion. The documented rules approach (Zive) is more applicable to enterprise sales with complex multi-touch journeys. The 50/50 model (Segrist) and the written contract (Rack) could apply broadly. The core disagreement—whether to abandon attribution measurement, formalize it, or restructure credit—is a genuine disagreement across similar B2B contexts.
Why it matters: The mechanism chosen to resolve attribution disputes directly shapes team culture, incentive structures, and whether marketing investment in brand and awareness gets fairly credited or systematically undercounted.
Position A: Pipeline value and qualified opportunity count (5 supporters) Kyle Coleman (Episodes #198 and #123), Gurdeep Dhillon (Episodes #280 and #203), and Dave Gerhardt (Episode #304) all argue that marketing should own pipeline generation across all sources and be held accountable to two metrics: count of qualified opportunities created and pipeline value of those opportunities. Lead count is explicitly rejected as gameable.
Position B: Hand-raisers only, in long sales cycles Pranav Piyush (Episode #191) argues that in 12–18 month sales cycles, holding marketing accountable to pipeline or revenue includes factors outside marketing's control—product, sales, pricing, customer success. Only hand-raisers (demo requests, trial signups, contact form submissions) are truly within marketing's control. A separate model tracks downstream conversion.
Position C: Owned stages plus influenced stages across the revenue supply chain Brian Kotlyar (Episode #118) argues that marketing should own some funnel stages entirely (site visits, demo requests) and influence but not own others (qualification, deal closure). Metrics should reflect both owned and influenced stages, not just one point in the funnel.
Support summary: 5 vs 1 vs 1 (Position A has the most supporters)
Context dependency: Pranav Piyush explicitly scopes his position to 12–18 month sales cycles, which could dissolve some tension with Coleman and Dhillon if they are speaking about shorter cycles. However, Coleman and Dhillon do not restrict their advice to short cycles, and Kotlyar's model applies broadly. The core disagreement about whether marketing should own pipeline as a metric vs. only own hand-raisers is a genuine disagreement even within enterprise B2B contexts.
Why it matters: Choosing the wrong accountability metric either lets marketing off the hook for business outcomes or holds them responsible for factors they cannot control—both of which damage credibility and alignment with sales.
Position A: Eliminate MQL/SQL — replace with explicit intent signals (3 supporters) Pranav Piyush (Episode #130) argues MQL and SQL terms are subjective and create friction; replace them with objective intent signals like demo requests or pricing inquiries. Aditya Vempaty (Episode #304) argues for eliminating lead scoring models based on form fills and engagement entirely. Kyle Coleman (Episodes #198 and #123) proposes the ICP + ICT intersection as a replacement qualification standard—a structured two-dimensional framework that sidesteps MQL/SQL labels.
Position B: Standardize MQL/SQL terminology through written agreement (3 supporters) Chris Rack (Episode #150) argues that every organization uses different terminology and the solution is to write down and have both VPs sign agreements on what defines a good lead—standardize the terms, don't abandon them. Ruth Zive (Episode #175) recommends documenting definitions of pipeline readiness and qualification criteria to remove ambiguity, and separately recommends qualifying leads at top of funnel on firmographics and buying intent signals—implying a structured qualification framework rather than abandoning qualification entirely.
Support summary: 3 vs 3 (evenly split)
Context dependency: This is a genuine disagreement regardless of context. All guests are addressing the same problem—marketing-sales misalignment over lead definitions—and prescribing directly opposing solutions: elimination vs. standardization.
Why it matters: Whether you eliminate or standardize lead qualification terminology determines how marketing and sales communicate about pipeline quality. Getting it wrong perpetuates the exact misalignment both approaches are trying to solve.
Position A: Small focused list — 2–5 accounts per quarter (3 supporters) Chris Rack (Episodes #140 and #150) argues that true ABM works best with 2–5 accounts per quarter, that ABM is inherently not scalable, and that the goal is creative differentiation, not volume. His Episode #150 "Goldilocks" framing adds nuance: the list should be large enough to experiment with but rooted in data rather than a sales wishlist. Brian Kotlyar (Episode #331) emphasizes quality and process over volume in account selection, though does not specify a target list size.
Position B: Large mapped universe — 50,000–100,000 accounts (1 supporter) Jaleh Rezaei (Episode #248) argues for mapping your total addressable market as a specific list of 50,000 to 100,000 target accounts, using this large universe as the organizing principle for both sales and marketing strategy.
Position C: Named account territories — 50–200 accounts per rep (2 supporters) Kyle Coleman (Episodes #198 and #123) recommends assigning 50 accounts per enterprise seller and 200 per mid-market seller as named account territories—a structured middle ground that enables both focus and coverage.
Support summary: 3 vs 2 vs 1 (Position A has the most supporters)
Context dependency: Chris Rack's 2–5 account recommendation may apply to a high-touch enterprise ABM motion. Jaleh Rezaei's 50,000–100,000 account universe may reflect a tech-enabled programmatic ABM approach. Kyle Coleman's territory model sits between these. However, all three frame their advice as the right way to do ABM rather than as context-specific variants, making this a genuine disagreement about ABM philosophy and execution.
Why it matters: Account list size determines the entire resource allocation, personalization depth, and measurement approach for an ABM program. Getting this wrong either spreads resources too thin or limits market coverage unnecessarily.
| Episode | Guest | Date |
|---|---|---|
| #343 | Jen Allen-Knuth | 2026-04-03 |
| #331 | Brian Kotlyar | 2026-02-19 |
| #331 | Drew Pinta | 2026-02-19 |
| #308 | Jen Allen-Knuth | 2025-12-01 |
| #307 | Dave Gerhardt | 2025-11-27 |
| #304 | Dave Gerhardt | 2025-11-17 |
| #304 | Aditya Vempaty | 2025-11-17 |
| #280 | Gurdeep Dhillon | 2025-09-08 |
| #277 | Kristine Segrist | 2025-08-28 |
| #248 | Jaleh Rezaei | 2025-05-22 |
| #239 | Pranav Piyush | 2025-04-21 |
| #235 | Aditya Vempaty | 2025-04-07 |
| #218 | Gabby Sellam | 2025-02-10 |
| #203 | Gurdeep Dhillon | 2024-12-19 |
| #198 | Kyle Coleman | 2024-12-02 |
| #191 | Pranav Piyush | 2024-11-07 |
| #187 | Sean Lane | 2024-10-24 |
| #186 | Mason Cosby | 2024-10-21 |
| #175 | Ruth Zive | 2024-09-12 |
| #164 | Adam Goyette | 2024-08-05 |
| #150 | Chris Rack | 2024-06-17 |
| #140 | Chris Rack | 2024-05-13 |
| #130 | Pranav Piyush | 2024-04-08 |
| #123 | Kyle Coleman | 2024-03-11 |
| #122 | Natalie Marcotullio | 2024-03-04 |
| #118 | Brian Kotlyar | 2024-02-19 |
