Pull-Through Revenue: The Quotes Walking Out of Your Service Book

The Metric Almost Nobody Tracks

Most contractors think about service agreements the same way: recurring revenue, predictable visits, steady margin. Set them up. Bill them annually. Collect.

At the median, follow-on work adds $8.70 per $100 of annual agreement revenue; P75 is $29.60. The ratio measures follow-on revenue relative to agreement revenue, rather than the agreement relationship's total economic value.

Additional repair, replacement and project revenue can come from the agreement relationship, including work discovered outside scheduled maintenance visits. Its contribution depends on delivery cost, not revenue alone. That's pull-through revenue. Within Level's research universe of 2,242 contractors, 386 companies meet the agreement and follow-on revenue eligibility rules. The gap between median and top-quartile pull-through is a distribution difference worth investigating in your own book; revenue spread alone does not prove a profitability lever.

The median contractor generates 8.7% pull-through, meaning for every $100 of SA revenue, they capture roughly $8.70 in follow-on project and repair work. Top quartile runs 29.6%+. The P90 comparison point is 93.4% and can include relationship-driven project work, not only visit-discovered repairs.

These are the eligible 386-company distribution, not a metric computed across every company in the research universe. The improvement targets and recoverable-revenue examples below are Level operating targets and arithmetic scenarios, designed to turn the benchmark into an action plan rather than a promise about any individual contractor.

On a fictional $1M annual agreement book, the median 8.7% ratio corresponds to $87K of follow-on revenue and P75 at 29.6% to $296K. The $400K figure is a separate 40% scenario, not the published top-decile boundary. These comparisons do not prove a training or workflow lift; trade mix, customer demand and attribution also affect the ratio.

This post is about that gap. What it's worth, why most contractors miss it, and the specific workflows that close it.

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The Operator Example: Strong Pull-Through, Unowned Quotes

In one commercial mechanical review, the service division had substantial follow-on work associated with agreement customers. It still lacked reliable ownership of aging quotes and renewals. This is one operator observation, not a measured percentile or a representative case series. Customer-identifying revenue, agreement sizes, quote counts and timing combinations are withheld.

So the punchline of the engagement wasn't pull-through. It was what came next.

The quote-side review examined project quotes generated from maintenance visits and found open quotes without an assigned follow-up owner:

Two findings that only become visible when you stop looking at the FSM dispatch board and start looking at the economics:

1. Larger-value agreements were among those expiring. The expired and active cohorts differed in agreement value. Those larger-value expired agreements warrant a renewal and delivery-cost review; agreement value alone does not establish highest profit or customer quality. The review identified a missing assigned renewal follow-up; it did not establish that no customer contact ever occurred.
2. Substantial quoted pull-through work went cold, without a shared aging-and-owner queue. Techs were finding the work. Project quotes were being generated. And then nothing happened. No systematic follow-up at 7 days. No escalation at 14. No owner-level view of expired pull-through quotes by dollar value.

The lesson is that dispatch completion, open quoted value, recognized follow-on revenue and expired agreement value need separate records and owners. Neither an open quote nor an expired agreement establishes recoverable cash. The proposed queue links each recommendation to its quote, next action, accepted scope and eventual job contribution.

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Why Pull-Through Is Really a Workflow Problem

Most posts about pull-through revenue stop at "train your techs to find work." That's the start, not the finish. Four operating checks can test where your own follow-on work gets lost. The distribution does not show which workflows caused a company to reach P75.

1. Tech-Level Identification (the one everyone talks about)

Techs on a maintenance visit need to be trained to identify more than just the checklist. They're looking at equipment age, code compliance gaps, efficiency issues, and upgrade opportunities. The ones doing this well document everything: photos, notes, rough cost ranges.

An illustrative operating scenario is 0.7-1.4 documented recommendations per eligible visit, compared with a 0.3 scenario. These are planning inputs, not measured technician or contractor quartiles. Count eligible visits, customer-approved opportunities and subsequent contribution separately; more recommendations alone do not establish better service or profit.

2. Per-Tech Pull-Through Attribution

If you don't know which techs generate pull-through and which don't, you can't manage the gap. At tech level, compare opportunities, customer mix and hours before ranking contribution. A 5-8x spread is an author operating scenario, not a published technician-level distribution. Training, documentation and incentives are mechanisms to test, not causes established by that ratio.

Two practices to test are attribution to the originating technician and a contribution-aware recognition or compensation rule. The published pull-through distribution does not measure how often either practice occurs at the median or upper quartile. Keep attribution tied to the original recommendation, accepted scope and realized job costs so incentives do not reward duplicate leads or unnecessary work.

3. Real Quote Follow-Up Sequences (not reminders in the FSM)

A tech recommendation that sits in the FSM queue isn't revenue. The revenue happens when a sales/service manager calls the customer within 48 hours, presents the quote, answers objections, and closes. Across the separate eligible quote dataset, median quote conversion is 73.9%, for decided quotes. Open quotes are excluded from that denominator. Aging and abandoned quotes are a distinct workflow to investigate, alongside price, scope, customer intent and delivery capacity.

A practical operating rule to test is: every open pull-through quote over $5K gets a personal follow-up call within a week. Whether your team has an escalation trigger is a configuration and ownership question, not a measured median fact. The quote just sits and ages out. The 7-day follow-up rule →

4. SA Renewal Prosecution

Renewal is a separate workflow worth checking, even when dispatch and visits run well. SAs don't renew themselves. Or more precisely, the easy ones do, and the complex ones don't. The lower-value residential SA auto-renews. The higher-value commercial multi-site contract with a changing building manager and last year's service complaint? That one needs a human working it 60 days before expiration.

If you lack a 60-90 day pre-expiration renewal workflow, assign an owner to each expiring agreement. The SA runs out. The customer doesn't sign the renewal because nobody showed up with it. Another contractor wins it next year. The renewal rate benchmark data shows the gap: legacy 90%+ and under 60% renewal references have no reproduced eligible annual cohort here; use your renewed / eligible-to-renew count for a stated window.

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What the Numbers Actually Look Like

Here's the pull-through distribution from 386 contractors in Level's dataset with meaningful SA books:

The middle half runs from 1.5% at P25 to 29.6% at P75. This is a comparison among different eligible companies, not the same book before and after a workflow change. Follow-up ownership is within your influence, while customer mix, project attribution and delivery capacity also move the ratio. Test changes within comparable customer cohorts and count realized contribution, not just additional quotes.

On a $5M service contractor with a $1.2M SA book:

The difference between median and top quartile on a $1.2M SA book is approximately $250K in annual revenue in this comparison, with about $113K-$138K of modeled gross profit at an assumed 45-55% margin, before selling and overhead cost; the percentile gap is not a proven attainable improvement. Whether it needs extra selling effort, capacity or equipment must be modeled rather than assumed away.

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Why Your FSM Won't Close This Gap

For BuildOps, ServiceTitan, Housecall Pro, FieldEdge or another FSM, confirm which recommendation, quote and agreement fields your edition exposes. A dispatch board or feature label does not prove end-to-end financial follow-up.

Audit what your configured system actually does. These are checks for the product, workflow and assigned owner, not claims that every vendor lacks the capability:

• Route the recommendation to a quote
• Trigger a 48-hour follow-up assignment
• Escalate quotes aging past 7 days to a manager
• Flag pull-through attribution in tech comp reports
• Surface expired SAs 60/90/120 days before they lapse

Those are financial/operations workflows on top of the FSM. Some products can provide parts of this workflow; confirm the actual edition, integration and configuration. Assign ownership for the exceptions the native workflow leaves unresolved. The gap between what your FSM captures and what actually drives revenue is a category problem, not a product problem.

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The Audit That Usually Uncovers This

When we audit a contractor's pull-through economics at Level, we run the same four queries against the FSM data and the accounting system:

1. Pull-through ratio trailing 12 months, total and by tech.
2. Expired SA cohort: every agreement that lapsed in the last 24 months, with annual value, tenure, and reason for non-renewal (documented or inferred).
3. Aging pull-through quotes: every quote generated from a maintenance visit that's been open more than 7 days, by dollar value and age.
4. Pull-through conversion rate: of quotes generated, what percentage are won? A 30-point swing is a sensitivity scenario, not a measured pull-through-specific quartile spread.

A $150K-$500K annual opportunity is a planning scenario, not a nine-in-ten audit outcome. Size it from your eligible book, quoted value, conversion assumptions and contribution margin, and hold disputed, expired or already-covered work separately. A large book may support a $1M scenario, but company revenue alone does not establish it.

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What to Do This Month

If you haven't audited your pull-through in the last 12 months:

1. Pull the ratio. Total non-SA service/project revenue on SA customers ÷ total SA revenue. If you're under 8.7%, you're below the reported median for the eligible sample.
2. Segment by tech. Flag the bottom quartile of techs by pull-through dollars per visit. Review customer mix, opportunity quality, recorded hours and documentation before choosing a training intervention; that ranking does not prove training ROI.
3. List every quote open more than 7 days. Sort by dollar value. Assign a human to follow up on the top 10 this week.
4. Pull the expired SA list for the trailing 24 months. Sort by ACV. Start with the top 5.
5. Build a 90-day pre-expiration workflow on every commercial SA above a threshold ($25K ACV is a fine starting line).

If you have a meaningful SA book (50+ agreements) and you don't have pull-through dialed in, compare the contribution opportunity against collections, pricing and capacity constraints. The benchmark cannot rank it as your highest-return project without your own data.

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The Bottom Line

Pull-through compares follow-on revenue with agreement revenue; evaluate delivery costs and attribution before treating it as a service-division performance score. The median contractor captures $8.70 of pull-through for every $100 of SA revenue. P75 captures $29.60 per $100 of agreement revenue. This compares different companies, customer mixes and attribution rules. The four-part workflow above is an operating playbook to test, not the identified cause of that spread.

And quietly underneath all of that: the published anonymous example had a higher average annual value among expired agreements. Test your own expired cohort; it does not mean your best customers are necessarily the most likely to leave.

Source and claim note

Pull-through, agreement cohorts, and per-technician workflow figures come from Level's proprietary contractor research and operating work. Current metric definitions and samples are available in the downloadable contractor benchmark data. Dollar examples are transparent scenario math, not forecasts. For external context on service-contract controls, see the U.S. Small Business Administration's financial-management guidance and BLS installation and repair occupation data. Use the service-agreement profitability calculator or work with Level to measure your own book.

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Q: What's a realistic pull-through ratio to target? A: Median is 8.7%. Top quartile is 29.6%. For a contractor starting near the median, 15-20% in year one and 25%+ in year two are author operating targets to test against opportunity mix, attribution and delivery capacity. The published distribution does not establish those time-to-improvement results. P90 is 93.4% and includes relationship-driven projects. A 40% operating scenario is not the top-decile boundary or an assured cultural outcome. See the service-agreement power-law analysis for concentration context; it does not guarantee that every program reaches 40%.

Q: How do I train techs to identify pull-through without making them feel like salespeople? A: Frame it as "inspect, recommend, document," not "upsell." A good tech on a maintenance visit is already looking at equipment age, code gaps, and efficiency issues. The training is about documenting what they see, not selling it. The selling happens later, by a service manager or rep, on a follow-up call. Separating identification from closing is the key move most contractors miss.

Q: What's a reasonable pull-through spiff/commission? A: A 2-5% incentive on attributed pull-through revenue is an illustrative compensation design, not a measured top-quartile practice. Test it against contribution margin, customer consent and employment rules before implementation. The number isn't the point, the visibility is. A weekly attribution report is a mechanism to test. The article does not establish a causal 2-3x revenue lift from leaderboards. See the full add-on sales benchmarks analysis for per-trade comp norms.

Q: How often should we review the expired SA list? A: Monthly at minimum for commercial accounts. Quarterly for residential. The metric that matters most is renewal with price increase, not just renewal rate. In a fictional same-value cohort, 95% renewal at flat price shrinks the book 5% nominally before new sales. With 3-5% inflation, real retained value falls about 7.8-9.5% (0.95 / 1.03 or / 1.05, minus 1). An 88-92% renewal rate with a 4-7% price increase is a planning scenario, not a verified joint distribution of top operators. Test lost agreements, same-account revenue and contribution margin before deciding whether a price increase improves the book.

Q: Can Level audit this for us? A: Yes. Depending on available integrations, exports and signed scope, we reconcile eligible agreement and follow-on records with your accounting data. A person reviews population completeness, attribution and exceptions before comparing the supported ratio with the Level Index. The first audit is free. The audit measures your eligible ratio, aging quotes and expired cohort; a $150K-$500K scenario is not a typical first-month recovery guarantee. See pricing →.

Q: Where can I see the benchmark data in full? A: Pull-through and SA margin benchmarks with quartile breakouts are on the Level Index contractor page. For the deeper dives, SA renewal rates, pull-through by trade, and the power-law distribution inside SA books, the service agreements category includes further operating articles; check each article's source and eligibility notes.