Dale W. Harrison

It matters only if it helps distinguish wins from losses. They connect this to information theory: a data point is informative when it changes what you can infer about an outcome.

In one post, Harrison imagines that 80% of wins and 80% of losses are SMB accounts.

• That would make SMB a poor signal.
• If 80% of wins but only 20% of losses are SMB, the comparison supports a different conclusion.
• They use the same reasoning to challenge ICPs built from closed-won deals alone.

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Harrison adds the seller’s market position, product readiness, and ability to sell into the account to the usual question of buyer intent.

Harrison contrasts a small CRM vendor competing with Salesforce for a Fortune 1000 account with a non-HIPAA-compliant CRM pursuing healthcare. Even a suitable category need can be a poor fit if the seller cannot meet the buyer’s requirements or navigate its procurement process.

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Reps rewarded for pipeline value may leave stalled opportunities open; incentives around win rates may lead teams to enter deals late. They recommend examining deal-duration distributions and incentives before using sales-cycle averages as a measure of effort.

Harrison suggests comparing closed-won and closed-lost deal durations. In one post, they describe a hypothetical company where 97% of wins close within 81 days: an older open deal deserves review, but 81 days is not a universal cutoff or proof the deal is lost.

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Their proposed test is whether a measure helps explain the probability of revenue reaching the business. They also argue that revenue-efficiency analysis should include lost deals, because the sales effort spent on a loss still has a cost.

In a post about CFO reporting, Harrison contrasts familiar marketing metrics with measures such as lifetime value, win rate, churn, margin, and return per day of sales effort. They ask whether a marketing metric changes the probability of reaching cash.

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Their proposed bridge is account scoring based on demonstrated results that helps prioritize prospects with a better chance of becoming revenue.

Harrison describes a sales rep spending two weeks on a poor-fit lead: the time cannot be recovered, and it could have been spent elsewhere. They cite a Gartner benchmark in the post; ask for the underlying benchmark data before relying on it.

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This post shows how shared traits among customers can mislead—and what to compare instead.

Harrison argues that a trait common among closed-won deals is not automatically predictive.

• In their example, 80% of wins and 80% of losses could both be SMB, making the trait unhelpful.
• If 80% of wins but only 20% of losses are SMB, the comparison tells a different story.
• The practical point: compare winners and losers rather than profile winners alone.

For brands planning a campaign

For lead scoring or target-account selection, compare the same account traits across won and lost deals before weighting them. This can help teams avoid targeting a segment that appears in wins but is just as common in losses.

This post introduces buyer-seller fit and shows how brand position, product readiness and sales capability affect the answer.

This post separates category demand from the chance that a buyer will buy from a particular seller.

• Harrison asks teams to consider both whether buyers want the product and whether the company can serve and sell to them.
• Examples include a small CRM brand competing against Salesforce in a Fortune 1000 account and a sales team unprepared for complex procurement.

For brands planning a campaign

Use fit as a check alongside intent and firmographics. For a campaign, define which accounts the product and sales team can realistically win, then align messaging and qualification to those limits.

See why a familiar CRM measure may not reflect the effort your team actually spends.

Harrison cautions that CRM time-in-pipeline is convenient, but not necessarily a sound measure of sales effort.

• Keeping stalled deals open can inflate sales days; entering deals only when they look likely to close can make the number too low.
• The post points to incentives and data practices as reasons to question the metric before using it.

For brands planning a campaign

Before using sales-cycle days to score leads or compare campaigns, check how deals enter and leave the CRM and whether incentives encourage stale opportunities to remain open. Pair the metric with a review of closed-lost deals and deal activity.

This post shows how to use your own win history to flag deals that deserve a closer review.

This post recommends looking at the distribution of days active for closed-won deals instead of relying on an average.

• Harrison gives a hypothetical example: if 97% of wins close within 81 days, a deal still open beyond that point merits scrutiny.
• They link long-lived deals to optimism and incentives tied to pipeline value, making the threshold a review prompt rather than proof a deal is dead.

For brands planning a campaign

Calculate the time window that contains most wins in your own data, then use it to trigger a human review of older open deals. Do not treat the example's 81-day figure as a universal rule or automatically remove opportunities.

Harrison argues that the common B2B process—upload closed-won deals, find common traits, call it the ICP—is a guaranteed failure.

• He shows how, if 80% of wins and 80% of losses are enterprise, “enterprise” tells you nothing about which deal will close.
• Citing Shannon's information theory, they argue that a characteristic is useful only if it reduces uncertainty about future outcomes.
• Their alternative is to compare closed-won and closed-lost outcomes, not just success patterns.

Compare wins and losses; look for factors that appear far more often among winners than losers.

In the network-security versus fintech example, Dale W. Harrison gives illustrative figures but not the full underlying dataset; ask for the data behind them.

For brands planning a campaign

For a lead-scoring or sales-marketing alignment brief, expect an approach that rebuilds ICP and scoring from win/loss contrasts rather than customer-only profiles.

Examples: Source 1 · Source 2 · Source 3 · Source 4 · Source 5

Harrison separates whether a buyer is in-market from whether they will buy from you and whether you are prepared to sell to them.

• They lay out a hierarchy: category fit, in-market fit, consideration-set eligibility, conditional win probability, and high customer lifetime value.
• Examples include a small CRM brand trying to sell into a Fortune 1000 account against Salesforce, or a non-HIPAA-compliant CRM targeting healthcare.

Break fit into category fit, in-market fit, consideration-set eligibility, conditional win probability, and high CLTV.

The posts present the hierarchy as a framework; ask for evidence that it has been tested.

For brands planning a campaign

Lead scoring should account for the seller's ability to win, not just whether the account appears to want the category.

Examples: Source 1 · Source 2

Harrison argues that most lead scoring rests on the false promise that intent data predicts buying intent, and that AI-based lead scoring fails when it optimizes on closed-won deals.

• They say the business wants to maximize revenue flow for a fixed amount of marketing and sales investment.
• Analysis must include lost deals, because sales effort spent on a lost deal costs the same as on a won one.

Use total deal value divided by total sales effort, including all won and lost deals.

The post asserts that 100% of AI-based lead scoring fails; ask for the evidence behind that claim.

For brands planning a campaign

A brief about improving lead scoring should expect a critique of intent data and MQL handoffs, alongside a proposed evidence-based scoring model.

Examples: Source 1 · Source 2 · Source 3

Harrison calls the ratio of days-active for closed-lost versus closed-won deals the most valuable RevOps metric no one calculates.

• They report ratios from 3:1 to 7:1 across hundreds of B2B companies, attributing them to sales optimism and rep incentives based on total pipeline value, which keep zombie deals open.
• Their proposed fix is to examine the days-active distribution: if 97% of closed-won deals close within a company-specific number of days, deals open longer are likely zombies.

Calculate days-active distributions for won and lost deals; set a company-specific threshold for zombie cleanup.

Dale W. Harrison says the 3:1 to 7:1 range comes from calculations across hundreds of companies; ask for the underlying dataset.

For brands planning a campaign

For a brief on pipeline velocity and reducing zombie deals, expect a diagnostic that compares closed-lost and closed-won durations, examines rep incentives, and replaces averages with distributions.

Examples: Source 1 · Source 2

Harrison analyzes the roughly $1.1 trillion US marketing budget by category.

• They put media spend at around 40% of the total, martech and analytics at around 30%, labor overhead at around 9%, market research at around 5%, creative development at around 4%, and media planning at around 1%.
• They argue that AI is more likely to further balloon martech and analytics budgets at the cost of media spend, and that ad production is too small a slice to interest senior finance leaders.
• They also point to $80 billion to $100 billion lost to ad fraud.

Segment the US marketing budget into media, martech/analytics, labor overhead, market research, creative development, and media planning.

Dale W. Harrison says the exact percentages by category are debatable and groups events, influencers, sponsorships, and PR under media costs.

For brands planning a campaign

A brand weighing AI investment in marketing should expect an analysis arguing that AI is more likely to expand martech and analytics budgets than replace media spend, and that ad production is a small slice of total spend.

Examples: Source 1

They say every lead handed to a sales rep is a potential waste of the rep’s time, and marketing hands off many leads that never close.

• They cite Gartner benchmark data showing that 1% or less of marketing-qualified leads ever close.
• They argue that evidence-based account scoring reduces marketing’s need to avoid blame and sales’ regret.

Contrasts marketing’s effort to avoid blame with sales’ effort to avoid regret to explain friction in MQL handoffs.

Harrison cites Gartner benchmark data showing that 1% or less of MQLs close.

For brands planning a campaign

Sales-marketing alignment should address incentives and wasted sales time, not just data sharing.

Examples: Source 1

They ran an anonymous survey asking who would likely be on the buying committee for a $5 million annually recurring purchase.

• The survey received 151 responses from 4,300 impressions.
• 77% of respondents thought the CEO and CFO would definitely be on the buying committee, while 17% thought no one from the C-suite would be.
• They argue that larger purchases are more likely to be decided by operational staff, not senior leadership.

Anonymous survey with 151 responses; uses sourcing toilet paper for Google restrooms as an example of a large annual purchase.

The survey response rate was 3.5%; the Google toilet paper example illustrates the purchase in question.

For brands planning a campaign

ABM targeting and personalized messaging shouldn’t assume the CEO or CFO is involved in operational purchases; larger purchases tend to be delegated to operational staff.

Examples: Source 1

They argue that information changes the odds of an outcome, while data is just the container. They use Shannon’s information theory to make the point.

Uses Shannon’s information theory to distinguish data volume from information that changes the probability of an outcome.

For brands planning a campaign

Judge scoring models by whether a variable reduces uncertainty about a future outcome, not by whether it is commonly observed among customers.

Examples: Source 1 · Source 2

Episodes cover marketing effectiveness, buyer-seller fit, data versus information, CFO metrics, and pipeline measurement.

• The series announcement identifies them as Head of Strategy at The Insight Collective.
• They co-host or appear with Jamie Hendrie, Liam Moroney, and Tom Grunwald.
• Posts typically preview an episode, develop a worked argument, and link to the live session.
• The series announcement states that The Insight Collective sponsors it.

Uses LinkedIn posts as episode briefs, with examples, some graphs, and links to live sessions.

For brands planning a campaign

Brands can evaluate not only Harrison’s posts but also a recurring live series featuring co-hosted discussions and worked examples.

Examples: Source 1 · Source 2 · Source 3 · Source 4 · Source 5 · Source 6

They argue that the proof finance trusts already sits in the CRM: dollar return per day of sales effort, customer lifetime value, win rate, churn, and margin. Their test for any marketing metric: How does this change the probability of reaching cash?

Frames marketing metrics around whether they change the probability of reaching cash.

For brands planning a campaign

Measurement work should connect marketing metrics to dollar return per day of sales effort, CLTV, win rate, churn, and margin—not rely on impressions, clicks, MQLs, and cost per lead alone.

Examples: Source 1

Harrison says they are Head of Strategy at The Insight Collective, which provides lead generation for B2B technology brands. The firm also sponsored the LinkedIn Live series they co-host with Jamie Hendrie.

Original disclosure

Dale is Head of Strategy at The Insight Collective, which sponsored the LinkedIn Live series.

View example

Ask for confirmation of Dale W. Harrison’s formal relationship with the business behind it.

Original disclosure

That's exactly the kind of evidence-based scoring model we built — the Next-Gen MQL.

View example

Karuppanan writes about reading sales calls, transcripts, and CRM data to predict stalls and find buying-committee gaps.

• Harrison argues that sales effort should be measured against closed-won and closed-lost outcomes, and that intent data alone does not predict buying.
• Both doubt that top-of-funnel personalization is a meaningful differentiator.

Karuppanan's posts are operational: named AI skills for HubSpot, specific playbooks, and an offer to share outputs. Harrison's are diagnostic, covering win/loss contrast, information theory, and metric definitions, with the LinkedIn Live series as the main format.

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Harrison also works on scoring and pipeline decisions, and both press the need to compare outcomes rather than copy a formula. Ruha's rule lists and Harrison's win/loss framework give teams ways to check whether their process matches their stage.

Ruha's frame is prescriptive and stage-based, with tool lists for each ARR band.

• Harrison's is analytic, asking what a metric or customer trait actually tells you.
• They lean on co-hosted live discussions rather than numbered rule sets.

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Harrison makes a related case for B2B revenue: win rate, margin, churn, and return per day of sales effort matter more than impressions or pipeline totals. Both urge readers to judge metrics by what they reveal, not by how easy they are to report.

Adolph writes about ecommerce and Shopify growth, with sale-and-drop timing and new-customer acquisition cost examples. Harrison writes about B2B sales and marketing, covering closed-won versus closed-lost comparisons, buyer-seller fit, and CFO reporting.

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The published posts describe Claude skills for deal reporting, audience targeting, account research, field marketing, and stall diagnosis.

• The stall-diagnosis post names buying-committee gaps, champion silence, unacted triggers, and sequence fatigue as ranking factors.
• That fits a company that accepts the lead-scoring concepts and needs a working pilot to read call transcripts, CRM outcomes, and engagement data to surface stalled accounts.
• They also state that they run ABM execution programs for enterprise SaaS and IT services companies, which is closer to implementation than commentary.

The tradeoff is depth of decision theory and measurement proof.

• Their posts show practical AI workflows and a practitioner’s close on pipeline review, but they do not make the full case for testing ICP traits against closed-won and closed-lost populations, pricing sales effort across all deals, or taking a CFO-facing metric to the board.
• They mention closed-won and closed-lost data in a deal-reporting skill, so they can support implementation; the buyer would still need the won-loss contrast, the sales-effort denominator, and the explicit sales-marketing alignment frame in Dale W.
• Harrison’s posts.

Examples: Source 1 · Source 2

One post lays out rules by ARR band from $0-1M to $25M+, including when to score ICP with signals rather than firmographics, when to add SDRs and ABM, and what to watch in CAC payback and NRR.

• The second post describes a lead-gen system that compares closed-won and closed-lost deals in Claude, requires at least two buying signals, and turns research into personalized outreach.
• For a company that needs the workflow and stage-specific rules to make lead scoring repeatable, that is more directly actionable than a conceptual framework.

The tradeoff is focus.

• The posts are stronger on building an outbound and ABM system than on sales-marketing blame versus regret, zombie-deal detection, or the math of total deal value over total sales effort.
• They use closed-won versus closed-lost comparison as part of an AI skill, so they overlap with the need for lead scoring, but they do not develop the statistical point that a trait must separate winners from losers or the CFO-facing cash-probability test.
• For a campaign aimed at defending a scoring model to finance and cleaning up pipeline measurement, Dale W.
• Harrison’s argument is more central; for scaling the motion with AI-supported research and outreach, Ruha’s posts are more specific.

Examples: Source 1 · Source 2

He becomes relevant only if the campaign decision shifts to e-commerce growth measurement: which KPIs to track and how to structure demand capture.

• The posts argue that ratios such as conversion rate and revenue per visitor beat absolute numbers like revenue and clicks.
• They also describe a monthly push-and-pull cycle for a seven-figure e-commerce brand, with a demand-capture sale followed by a design drop to returning customers.
• For a DTC or Shopify operator wanting to test spend and measure performance with ratios, that is a clearer fit.

The posts do not address B2B lead scoring, sales-marketing alignment, sales effort per deal, or zombie-deal review.

• Their examples are consumer e-commerce, Shopify, and email marketing, not SaaS pipeline or CRM hygiene.
• This alternative fits only if the need shifts away from B2B SaaS lead scoring.

Examples: Source 1 · Source 2