Chris Lingenfelter works on sales development and outbound at LevelUp Leads. Lingenfelter's posts describe building targeted prospect lists and combining sales technology, AI, and human SDRs to turn outreach into qualified sales appointments.
Most useful for practical outbound pipeline design and sales development examples for B2B industrial and software sales.
Best for
B2B sales development agency or software firm selling into a specialist industrial market.
Not for
Consumer or mass-reach brand wanting a paid ambassador or spokesperson placement.
Key takeaways
Manufacturing software outbound system reportedly produced 23 sales-qualified appointments per month.
Niche asphalt plant mapping used lookalike search when industry filters were too broad.
Analysis of 1.2M+ cold calls covered when to dial.
Check first: Confirm methodology and date range behind reported figures and audience composition.
Main ideas
A pipeline needs a working conversion system, not just more activity · For a niche market, build the market map before pulling contacts · Test outbound practices against operating data
Chris Lingenfelter works on sales development and outbound at LevelUp Leads.
The posts focus on turning prospecting into a working pipeline: finding accounts that genuinely fit, reaching them across channels, and connecting sales technology, AI and human SDRs so interested prospects receive timely follow-up.
For a clear example, they describe a manufacturing software company’s outbound system and report that it produced 23 sales-qualified appointments per month.
Ask for the underlying measurement before treating those figures as independently verified results.
Follow their work for practical examples of pipeline design, niche-market research and outbound operations, alongside occasional posts about tools and agency culture.
The clearest contribution in these posts is treating outbound as a connected system rather than a list, a tool or a volume target.
In the manufacturing software example, Lingenfelter links account vetting, calls, email and social outreach, CRM and intent workflows, AI support, and SDR judgment—and explains how each part can fail when separated from the others.
Other posts make that approach more specific: one describes finding lookalike asphalt-plant operators when ordinary industry filters are too broad; another offers reported call-timing data.
Ask for the methodology and date range behind those figures.
The mix is useful to readers looking for concrete operating ideas.
Lingenfelter’s posts treat outbound sales as a system to design and test, not just a matter of sending more messages.
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Recurring priorities include precise account selection, coordinated technology and human follow-up, and practical information about what works. The agency-growth story adds a competitive, execution-focused note.
A pipeline needs a working conversion system, not just more activity
Lingenfelter argues that a pipeline stalls when a team reaches too few prospects or lacks a system to turn interest into meetings.
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Targeting, outreach, and conversion must work together: technology can surface and route signals, AI can help research and prioritize accounts, and SDRs bring calls, judgment, and objection handling. None of these parts works well alone.
Lingenfelter describes rebuilding outbound for a manufacturing quality software company that had generated 10 to 12 opportunities a year. The post says the new motion produced 23 sales-qualified appointments per month, 78 booked appointments in one quarter, and five closed-won deals.
For a niche market, build the market map before pulling contacts
When standard industry labels do not identify the right buyers, list building becomes a research problem.
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Start with accounts known to fit, then use them to find similar companies; a large contact list is not useful if its accounts are poorly matched.
For a company selling to asphalt plants, Lingenfelter says broad searches for construction or manufacturing returned data that was too broad, mislabeled, or outdated. The approach used known-fit companies as the basis for finding lookalike accounts with Ocean.io.
Lingenfelter argues that teams should measure performance rather than rely on sales folklore.
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The example suggests a specific test, but the post does not provide the underlying breakdown or methodology. Treat the figures as Lingenfelter’s account, not a universal rule; ask for the data and methodology before using them as a benchmark.
Lingenfelter reports that an analysis of more than 1.2 million cold calls found stronger connect and booking rates in morning blocks, especially 8–9 a.m., and a smaller late-afternoon rebound. There was no meaningful difference between Monday and Friday, and Lingenfelter credits SDR manager Eddie Cortez with knowing the pattern.
Choose tools by the job they do in the outbound system
Lingenfelter’s tool posts describe a working stack, not software as a substitute for sales work.
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They name tools for finding similar companies, enriching account data, dialing, handling CRM updates, and running email campaigns. Lingenfelter invites vendors to suggest tools, leaving the stack open to evaluation.
Lingenfelter describes Ocean.io for finding companies like existing customers, Clay for custom enrichment, Orum and Trellus for dialing and call-related tasks, and Smartlead for email and re-engagement.
Compete through execution and build a team around the work
In an origin post, Lingenfelter frames competitiveness as outworking and outbuilding established agencies, then points to a fuller team and more efficient operations as the next stage.
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A hiring post gives that operational emphasis a practical counterpart: SDR management and client-facing campaign strategy are separate responsibilities.
Lingenfelter recalls studying larger competitors with John late at night and describes the agency’s early drive to outperform them.
They name team members in customer success, BDR management, and operations.
A separate post advertises an SDR Manager role and a Customer Success Manager role focused on diagnosing and fixing outbound campaigns.
Explains what Chris Lingenfelter means by a "conversion layer" and shows the full outbound motion in one worked example, making it the clearest first read for anyone trying to understand the method.
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The post opens with a diagnosis: teams either put too little into the top of the funnel or build a partial system with no conversion layer, and either way the pipeline stalls.
Chris Lingenfelter then walks through a manufacturing quality software company that was producing 10 to 12 opportunities a year.
They describe three stages: vetting accounts with AI research on technical fit before dialing; layering calls, email and social so the same accounts see the message in more than one place; and building the conversion layer with CRM and intent tooling, AI scoring and routing, and human SDRs making the calls.
The post argues that each part fails alone: tech without people becomes automated noise, AI without SDRs becomes a list nobody works, and SDRs without both end up guessing.
The reported result is 23 sales-qualified appointments a month, 78 booked appointments in a quarter and 5 closed-won deals, up from the original yearly figure.
Ask for the supporting case-study data before relying on the results.
For brands planning a campaign
For an agency pitching appointment setting, this is a worked example to borrow: it frames the offer as repairing the conversion layer rather than adding volume, and gives prospects a client-side narrative they can test against their own pipeline figures.
Answers the practical question of when sales reps should call and shows the kind of data-backed claim this account makes instead of offering general opinion.
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The post reports an analysis of more than 1.2 million cold calls across SaaS, IT, logistics, healthcare and manufacturing, targeting mid-market to enterprise buyers.
It says the 8–10 a.m. and 10 a.m.–noon blocks produced two to three times more connects and more meetings booked per dial, with 8–9 a.m. averaging 17 to 20 percent connect rates — the highest of the day across time zones — and a weaker rebound between 4 and 6 p.m. that works as a third sprint.
It also reports no meaningful difference between calling on a Monday versus a Friday.
SDR manager Eddie Cortez is credited with knowing the pattern.
Ask for the sample breakdown, methodology and date range before using the figures.
For brands planning a campaign
A concrete hook for outbound or dialer campaigns: a calling-window claim specific enough to invite a brand to request the underlying data or test it against its own call logs.
Explains how to build a target list when a market is too niche or messy for standard industry filters, a problem most list-building content skips.
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The post describes using Ocean.io lookalike search for a global company selling into asphalt plants, with an average deal size above $1.5M.
A normal sourcing approach would not work: searches for "construction companies" or "manufacturing" returned data too broad, mislabeled or outdated to identify who actually operated functioning asphalt plants.
Chris Lingenfelter started with accounts already known to fit and used the tool to find companies resembling real asphalt plant operators.
For a niche ICP, they argue, list building becomes market mapping — defining a usable total addressable market.
Ask for measured results if performance evidence matters to your decision.
For brands planning a campaign
A differentiated angle for ABM or data campaigns: rather than competing on database size, lead with lookalike modelling built from a client's existing best-fit accounts.
Shows which go-to-market tools this account uses day to day and the stack a campaign would plug into.
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A rundown of the tools used for GTM and outbound: Ocean.io for finding companies that look like existing best customers; ZoomInfo, SalesIntel and Clay for direct dials and firmographics, with Clay noted for custom enrichment logic; Orum and Trellus for AI dialing with transcripts, with Orum credited for connection rate and Trellus for handling notes and statuses inside the CRM; and Smartlead for email campaigns and re-engagement.
Each tool gets a line explaining its job.
The post invites other teams to pitch tools or drop recommendations, making this a live evaluation list rather than a settled endorsement.
For brands planning a campaign
For a tool or platform looking to reach this buyer, the post names where the agency already spends and openly asks for new pitches — a specific opening for a partner conversation.
Shows what the agency says about its own culture and origins, explaining the competitiveness framing that runs through the rest of the content.
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A first-person account of the early days with John: studying larger legacy lead-gen agencies at 1 a.m. on a Friday and concluding "we're better than these guys, in every way" — a claim about execution rather than ego.
Chris Lingenfelter names the people now leading customer success, the BDR team and operations, and says the agency has grown into one of the fastest-growing sales development agencies in the world with a full team behind it.
It is a motivational origin post rather than a method post.
For brands planning a campaign
Useful for judging tone and culture fit rather than capability: the agency foregrounds competitive drive and internal promotion, which matters when a brand is choosing who represents it.
Explains the team size and the roles the agency builds around, addressing the practical capacity question behind any campaign brief.
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A hiring post for LevelUp Leads listing two roles.
The SDR Manager leads a high-output team of more than 30 reps and is responsible for accountability, performance and building the systems that make cold outbound work.
The Customer Success Manager owns outbound campaign strategy, works directly with founders to identify what is broken and fix it, and is described as consulting rather than support.
The post shows the intended team shape and how the agency defines each role, not a service menu.
For brands planning a campaign
Signals team size and the split between SDR execution and client strategy, useful for judging whether the agency can absorb a brief. The roles were open when the post was written; ask whether they have since been filled.
Pipeline as three connected layers: targeting, activation, conversion
Chris Lingenfelter argues that stuck pipelines come from one of two failures: too little top-of-funnel activity or a partial system without a real conversion layer.
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Their most detailed post walks through a full rebuild for a manufacturing quality software company that was generating 10 to 12 new opportunities a year.
Targeting used AI research to pre-vet accounts for technical fit and quality certifications; activation layered calls, email, and social so the same accounts saw the message more than once; conversion combined deep tech (intent signals, routing, CRM hygiene), AI (research, scoring, hand-raiser detection), and human SDRs (calls, judgment, objection handling).
They report 23 sales-qualified appointments per month, 78 booked appointments in a quarter, and 5 closed-won deals.
Account-based motion built in three named layers, backed by a case narrative and numbers rather than abstract advice.
For brands planning a campaign
For a sales development agency campaign centered on appointment setting, Chris Lingenfelter is worth evaluating when the challenge is a full outbound motion, not email volume alone. Their framing gives buyers a concrete question to test: does the proposal build the conversion layer, or only add top-of-funnel activity?
Market mapping for niche, high-ticket ICPs instead of generic list pulls
Lingenfelter treats list building as market mapping when the ideal customer profile is narrow or messy.
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For a global company selling into asphalt plants with an average deal size above $1.5M, they say standard filters failed: pulling "construction companies" or "manufacturing" returned data too broad, mislabeled, or outdated to identify who actually operated functioning asphalt plants.
Their approach was to start with companies already known to fit, then use Ocean.io to find lookalike accounts resembling real operators.
They distinguish pulling contacts from building a usable total addressable market for a capital solution sold into a specialized market.
Seed the account list with known-fit companies, then expand through lookalike matching rather than broad industry tags.
For brands planning a campaign
Relevant to campaigns where the target market does not map cleanly onto database filters. Ask Lingenfelter to show the exclusions and vetting criteria behind the target list, not just its size: they say that step determines whether outreach reaches anyone worth the effort.
Cold-call timing tested against a large call dataset
Lingenfelter reports analyzing more than 1.2 million cold calls across SaaS, IT, logistics, healthcare, and manufacturing, aimed at mid-market to enterprise buyers.
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They highlight two morning blocks, 8:00 to 10:00 AM and 10:00 AM to 12:00 PM, that delivered two to three times more connects and meetings booked per dial.
They call 8:00 to 9:00 AM the golden hour, with 17 to 20 percent connect rates, and note a smaller late-day rebound from 4:00 to 6:00 PM that works as a third sprint.
Day of week showed no meaningful difference between Monday and Friday.
They credit Eddie Cortez, the agency's SDR manager, with knowing this area best.
Large-scale call-data analysis, with connect and booking rates reported by time block rather than as a single anecdote.
For brands planning a campaign
This operational benchmark can help a brand structure dialing windows before adding SDR headcount, and makes Lingenfelter a testable source of outbound performance data, not just a services vendor. Ask for segment-level detail behind the aggregate before applying the windows to a specific industry.
Hands-on evaluation of a GTM and outbound tool stack
Lingenfelter shares the sales tools the team uses daily and explains their roles in go-to-market and outbound work: Ocean.io for finding companies that look like current best customers; ZoomInfo, SalesIntel, and Clay for dials, firmographics, and custom enrichment; Orum and Trellus for AI dialing with transcripts and CRM-side note and status handling; and Smartlead for email campaigns and re-engagement.
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They say the team is always testing what is next and invite vendors to pitch new tools for evaluation.
Public tool stack with a one-line use case per tool, presented as a working stack under continuous testing.
For brands planning a campaign
Useful for campaigns involving tool adoption, integration, or a new product aimed at sales teams. Evaluate Lingenfelter for hands-on tool commentary; ask for evidence of any paid relationships before assuming a tool mention is sponsored.
Two posts show the operational side of the business.
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In one origin story, Lingenfelter describes sitting with John at 1:00 AM on a Friday to study legacy competitors, deciding they would outwork and outbuild them, and moving from a new entrant to what they call one of the fastest-growing sales development agencies in the world.
They name Patrick William Joyce on customer success and accounts, Eddie Cortez managing the BDR team, and Timothy Dahlstrom on operations.
The other post advertises open roles at LevelUp Leads: an SDR Manager to lead a team of 30-plus reps, and a Customer Success Manager role they describe as consulting work with founders rather than support, covering outbound strategy and fixing broken campaigns.
For brands planning a campaign
Signals an agency in active growth, with named operational leadership and capacity—relevant to campaigns about scaling an appointment-setting function or finding a partner who can absorb more volume. The hiring post also shows what Lingenfelter wants clients to expect from account management, giving brands a consulting-style promise to test in the proposal.
Chris Lingenfelter's posts describe LevelUp Leads as the sales development agency where the work happens and recommend a stack of outbound and GTM tools.
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The tool recommendations are mentions, not documented sponsorships or affiliate deals. Ask Chris to clarify their relationship with the agency.
LevelUp Leads
Disclosed partnership
Chris's posts speak from inside LevelUp Leads: one reports an analysis of 1.2M+ cold calls and names the agency's SDR manager; another announces open SDR Manager and Customer Success Manager roles.
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Ask Chris to clarify their relationship with the agency.
Chris names Ocean.io as a daily GTM tool, describing how they use it to find lookalike accounts for a global company selling into asphalt plants with a $1.5M+ average deal size.
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No sponsorship, affiliate, or client relationship is disclosed.
Chris Lingenfelter writes about outbound sales as a system to build and check, not a volume contest.
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Start with the long post on pipeline layers, which walks through a rebuild for a manufacturing quality software company and separates targeting, activation, and conversion; then read the niche market-mapping post about finding asphalt-plant operators, where list building becomes research rather than a contact pull.
If call execution is your interest, the post on 1.2 million cold calls gives specific time windows and connect rates, and the tool post shows the stack behind the work — software for finding lookalike accounts, enrichment, dialing, CRM updates, and email, presented as tools in service of the sales job.
The agency-growth and hiring posts explain how the business is staffed and what it promises clients.
Read for the operating detail and the numbers attached to it; ask for supporting data behind reported results.
The similar creators below take the same subject in different directions: one spreads GTM across more channels, one argues the opposite lever matters most, and one runs a largely content-led engine.
Both treat pipeline as an assembled system rather than a single tactic, break that system into named layers, and back the argument with client results and tool-level detail published as tactical LinkedIn breakdowns.
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Chris Lingenfelter's conversion layer — technology, AI, and human SDRs working together — reflects the same instinct Vacca applies in describing the layers that decide reply rate before copy is written.
Vacca spreads the motion across five parts, with AI search visibility, founder and employee content, paid ads, and RevOps alongside outbound, and emphasizes stacking signals and running channels in sync before email lands. Lingenfelter stays closer to the sales-development motion itself: vetting accounts for fit, cold-call timing, and a conversion layer built around human reps on the phone.
Both publish operational analysis of outbound programs for smaller and mid-sized buyers, reduce the motion to measurable components, and offer practical levers — how accounts are selected, how much reps dial, how calls convert — rather than general advice.
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They speak to the same reader deciding how to spend a sales team's time.
Patterson-Cole argues that volume is the lever to pull first and that conversion is harder to move even with good signals, framing outcomes as volume times connect rate times conversion. Lingenfelter puts the emphasis elsewhere: a conversion-layer rebuild that took a manufacturer from 10 to 12 opportunities a year to 23 sales-qualified appointments a month, with account vetting and conversion infrastructure doing the work.
Both describe a working go-to-market engine assembled from tools, AI, and people, name the stack component by component, and tie it to reported business numbers.
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Vrancsik's posting-and-outbound system and Lingenfelter's conversion layer both show how the parts connect instead of selling one tactic.
Vrancsik's engine is largely inbound-led: posting across seven or more profiles, SEO resources, comment-gate lead magnets, AI agents for research and routing, with most deals described as inbound-led.
Lingenfelter focuses on hands-on SDR execution — cold calls, dialing windows, outbound tooling, and the human conversion layer.
Ask for evidence of any content-led acquisition engine beyond what their posts present.
For a B2B sales development agency assessing an appointment-setting campaign, Chris Lingenfelter’s six posts focus on outbound pipeline, cold-call timing, SDR management, sales tools, and a conversion-layer system that combines technology, AI, and human SDRs.
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That content is aimed at sales and go-to-market practitioners, so it is a reasonable reason to test a placement with an audience of SDRs, sales leaders, and agency operators.
The only measured platform audience figure in the posts here is 8,165 LinkedIn followers at the 8 October 2026 snapshot.
That establishes a modest LinkedIn following; it does not show who those followers are.
The posts here do not measure follower geography, company size, job seniority, industry, engagement rate, or campaign performance.
Content appeal is therefore the buyer’s current basis for judging relevance, while measured audience composition remains unverified for an appointment-setting campaign.
Before committing, request recent campaign results for comparable topics and formats, plus any audience survey or platform data covering geography, seniority, and industry.
Without that, the buyer cannot confirm whether the audience is mainly agency-side sales leaders or a broader mix of sales-tool users and job seekers.
Chris Lingenfelter's posts show no sponsorship or paid-partnership format.
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The closest thing to partnership activity is a public product endorsement: one post lists GTM and outbound tools the LevelUp Leads team says they use daily — Ocean.io, ZoomInfo, SalesIntel.io, Clay, Orum, Trellus, and Smartlead — and invites vendors to pitch tools for evaluation.
That is a recommendation and an open invitation to be evaluated, not a paid placement.
The posts provide no pricing, deliverables, exclusivity terms, content-approval process, or campaign timeline.
A buyer considering paid work should ask directly through their LinkedIn profile about availability and commercial terms before planning spend.
An outbound sales development agency, or a software business selling into a specialist industrial market, needs more appointments and has tried adding top-of-funnel activity without building a system to convert it.
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The buyer wants a partner to build the motion, not just supply lists.
Chris Lingenfelter's posts speak directly to this need.
A pipeline post lays out a three-stage motion: pre-vet accounts for technical fit before dialing; layer calls, email and social so the same accounts see the message more than once; then build what they call the conversion layer from CRM and intent tooling, AI scoring and routing, and human sales development reps making the calls.
The diagnosis: each part fails on its own.
Automation without people becomes noise, AI without reps becomes a list nobody works, and reps without either end up guessing.
For a buyer with a stuck pipeline, the test is concrete: does the plan build the conversion layer, or only add volume?
A niche-market mapping post fits the same need.
For a company selling into asphalt plants with an average deal size above $1.5M, they describe starting with accounts already known to fit, then using lookalike search to find similar companies.
Broad industry tags returned data too broad, mislabeled or outdated to identify who actually ran functioning plants.
A buyer with a messy or specialised ideal customer profile can ask to see the exclusions and vetting criteria behind a target list, not just its size.
Two further signals support the fit.
An analysis of 1.2M+ cold calls reports that 8–10 a.m. and 10 a.m.–noon produced two to three times more connects and more meetings booked per dial, with 8–9 a.m. at 17–20 percent connect rates, and no meaningful Monday-to-Friday difference.
That operational claim is specific enough to test against the buyer's own call logs.
A hiring post describes a sales development manager leading a team of 30-plus reps and a customer success role defined as consulting with founders on outbound strategy, suggesting the agency can take on a campaign to build this motion.
Before proceeding, ask for documentation of the appointment and closed-won figures, which are the author's account of one project and are not independently documented in the posts.
The call-timing post gives a total call count but no sample breakdown, methodology or date range.
The posts do not specify whether sponsorship or paid-partnership terms are available, so discuss any commercial arrangement directly rather than assume a published package.
When to choose a specialist
A consumer or mass-reach brand wants a paid ambassador or spokesperson placement: defined deliverables, broad demographic reach and a rate card to plan spend against.
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Chris Lingenfelter's posts do not establish a fit for this brief.
The only measured audience figure is 8,165 LinkedIn followers as of the 8 October 2026 snapshot, on one platform.
The posts give no follower geography, company size, job seniority, industry, engagement rate or past campaign performance.
A brand buying reach or a defined demographic cannot verify the audience from this material.
The subject matter also works against the brief.
The six posts cover pipeline construction, list mapping for high-ticket industrial sales, cold-call timing, sales tooling, agency hiring and growth.
They target sales and go-to-market professionals, not a general consumer audience.
A consumer-facing campaign would need evidence that the audience extends beyond that.
No commercial format is documented: the posts publish no rate card, media kit, deliverable list, exclusivity term, content-approval process, campaign timeline or verified partnership inquiry route.
The closest thing to commercial activity is a public roundup of tools the team says it uses daily, plus an open invitation for vendors to pitch tools for evaluation.
That is a recommendation and an invitation, not evidence of paid placement; do not treat tool mentions as sponsored.
Before planning a campaign, ask for audience composition, campaign performance and commercial terms.
The posts show no offer or terms, but do not establish that sponsorships are never accepted.
A worked campaign example
Worked campaign angle for the good-fit brief: a conversion-layer diagnostic, with the agency's own method as the product.
Angle: prospects are told their pipeline is stuck for one of two reasons — not enough outreach, or no system behind the interest that exists. The campaign sells the second problem. Instead of a general thought-leadership series, it puts the agency's three-stage method into practice publicly on a subject the buyer's market will recognise.
Shape it in four pieces: 1. A short series of three LinkedIn posts from Chris Lingenfelter, each covering one stage of the motion: how accounts were vetted before dialing, how the same accounts were reached across multiple channels, and what the conversion layer comprises. Use the manufacturing quality software case as the spine of one post: 10 to 12 opportunities a year rebuilt to 23 sales-qualified appointments a month, with targeting, activation and conversion named in that order. Present the figures as the author's account of one project, and get the client's permission before naming it. 2. A one-page conversion-layer audit for readers to complete, with three columns matching targeting, activation and conversion. Include questions that establish whether each stage exists: which accounts were excluded and why, how many channels reached each account, and what routes an intent signal to a rep. Send completed and blank versions together so readers can see what a finished audit looks like. 3. A dialing-window test using the reader's own call data, with the morning and late-afternoon findings as a starting hypothesis, not a conclusion. The published analysis reports a total call count but no breakdown, so frame the findings as something to retest in the reader's segments, not apply blindly. 4. One next step: a working session with the agency's customer success function, which the hiring post describes as consulting with founders on outbound strategy and fixing broken campaigns rather than providing support.
Test the campaign by publishing the three posts, then checking whether inbound briefs describe a stalled pipeline or ask for more leads. Ask readers who complete the audit to send it in, and count how many identify a conversion gap rather than a volume gap. If briefs skew toward volume, the angle is addressing the wrong problem; lead the first post with the diagnosis instead of the case study. Track whether the manufacturing case or the call-timing test drives meetings: they point to different offers, and only one is a services conversation.
Choose Alex Vacca when outbound alone is not producing pipeline and the buyer is willing to rebuild the motion across multiple channels.
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Vacca argues that hiring sales development reps and sending cold email is only one of five parts, alongside AI search visibility, founder and employee content, paid ads and RevOps.
The evidence for buyers is the comparison within that argument: signal-stacked campaigns replying at 5 to 18 percent against 1 to 3 percent for a compiled list using the same copy.
Named client outcomes include Hemlane at 830 qualified contacts with a 5.5 percent reply rate and AirOps at $7.83M in qualified pipeline and $1.52M closed-won over ten months with zero new SDRs.
The appointment-setting question gets a different answer here: Vacca fits a brief seeking “more pipeline without more SDRs,” where the buyer can run enrichment, signal detection and channel work, and wants tech layers to capture existing demand.
Alex Vacca’s model challenges the sales development agency’s core offer.
If the buyer is paying for reps to make calls and book meetings, the five-layer diagnosis shifts spend from headcount to tooling, content and RevOps.
Vacca offers frameworks and reply-rate comparisons alongside named client results.
Lingenfelter offers one worked industrial rebuild: account pre-vetting, multi-channel activation, then a conversion layer combining CRM and intent tooling, AI scoring and human reps.
The program grew from 10 to 12 opportunities a year to 23 sales-qualified appointments a month.
A buyer choosing Vacca gets a broader channel model; choosing Lingenfelter gets one inspectable case.
Both sets of client figures are the authors’ own accounts, not independently documented results.
Choose David Patterson-Cole for cold-call-heavy selling into local SMBs, where deal sizes are small and dial volume is the main lever.
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The argument: nine times out of ten, the answer to improving SMB outbound is volume rather than conversion.
Conversion is hard to move — rarely more than a 50 percent lift across a total addressable market, even with the best signals — while dial volume can rise quickly.
The average SMB rep dials around 50 times a day; that can be doubled within a week, and internal reps being trained peaked at over 300 dials in a day.
The appointment-setting decision here is a diagnostic for buyers whose programs are unprofitable on LTV:CAC and payback.
They can use the formula — volume times connect rate times conversion — to find the broken component, with results such as Owner adding $1m+ ARR per rep.
Where Lingenfelter proposes building the conversion layer first, Patterson-Cole proposes fixing volume first — a more useful starting point when accounts are cheap, numerous and local.
This is a direct conflict of priorities, not a difference in style.
Lingenfelter’s worked case involves a high-ticket specialist market: a company selling into asphalt plants with an average deal size above $1.5M.
Broad industry tags could not identify who actually operated functioning plants, so accounts had to be vetted and mapped before any dialing.
Volume-first advice suits the opposite situation, where mapping each account is not worth the time.
A buyer planning a campaign for high-ticket or niche industrial accounts would under-invest in vetting by following that volume framing.
One of the two posts refers to a model screenshot that is not included; the only named result is Owner’s revenue-per-rep figure.
The buyer is trading Lingenfelter’s named industrial case for a stated formula and a referenced calculator.
Choose Jani Vrancsik when the buyer wants an inbound-led, AI-native go-to-market system rather than appointment-setting labor.
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The posts describe a $1.5M ARR agency run by 11 people with zero paid ads, a 7-figure pipeline built by the team plus three AI agents, 85 percent of deals inbound-led and closing in three to five calls, and LinkedIn content driving 80 percent of pipeline.
They also describe ATLAS, a set of six Claude skills that produces a full playbook — market research, ICP hypothesis, signal library, battlecards, messaging matrix and a consolidated 11-tab playbook — in about two days.
For the same campaign decision, this fits buyers who need positioning, ideal-customer-profile definition, buying signals and messaging built fast, or who want to launch an internal content engine and an outbound layer driven by first-party signals from website visitors and social engagement.
Jani Vrancsik’s system assumes the buyer has distribution and time.
The pipeline relies on consistent posting across seven-plus profiles, roughly 650,000 impressions a month in one quarter and 300,000 in the next, and a two-year inbound-led funnel.
The ATLAS output is a playbook the buyer’s own team edits and uses.
A buyer who needs booked appointments next month gets content and documentation infrastructure, not calling capacity.
The agency describes its own results without naming external clients.
Lingenfelter’s approach is operational and specific — vetting criteria per account, channels per account and the three components of the conversion layer — and includes a named manufacturing case and a call-timing analysis across 1.2M+ cold calls.
The tradeoff is a GTM playbook and content machine versus a conversion engine.
Choose based on whether the gap is documentation and demand generation or converting demand already present.
This profile covers six complete LinkedIn posts from Chris Lingenfelter: a pipeline rebuild case study, a cold-call timing analysis, niche market mapping for high-ticket industrial sales, a go-to-market tool stack, an agency growth post, and a hiring post, along with a LinkedIn account snapshot from 8 October 2026.
The posts present Lingenfelter's own account of the work; reported appointment and closed-won figures and call-timing numbers are not independently verified.
The posts here have no publication dates, so no timeline of change is claimed.
The posts here do not include books, shows, long-form articles, or earlier career material, so the profile describes what they show rather than claiming to cover Lingenfelter's full public record.