Tom Casano

HubSpot and Salesforce track last touch by default, so closing credit goes to whatever the buyer touched last.

• But they may have encountered LinkedIn for months beforehand, in the feed, before they knew they were in a buying cycle.
• Casano cites an average of 88 touchpoints before a B2B buyer closes.
• They recommend measuring influenced pipeline and revenue rather than last-click credit, using LinkedIn's Revenue Attribution Report connected to the CRM or third-party tools Fibbler, Dreamdata and HockeyStack, which match closed-won deals to ad exposure even when the prospect never clicked.
• They also cite a Dreamdata finding that including paid engagement data in attribution modeling increases measured ROI accuracy by seven times.
• One post's closing line sums up the argument: the question is not where the buyer clicked last, but whether they were influenced.

A $150,000 deal closes and 100% of the credit lands on "Direct" or "Organic" in the CRM, while LinkedIn registers zero — Casano's illustration of how a channel that helped create the deal can disappear from the report. The two posts make the same argument at different depths: one explains the mechanism, the other names the reporting stack that replaces it.

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In their account, website visits and website conversions are the platform's most expensive, lowest-converting objectives.

• A mobile user has to leave LinkedIn, wait for a page to load, navigate the site, find a form and type in details with their thumbs — all at a premium.
• Casano advocates LinkedIn Lead Gen Forms instead.
• LinkedIn already holds the prospect's name, email, job title, company and phone number, so they confirm rather than type, and the record goes straight to the CRM.
• Casano reports audit ranges of 10–30% conversion for gated mid-funnel content, 1–5% for bottom-of-funnel demo requests and roughly 1% for a website landing page.

A $2,000 cost per lead falls to $400 on the same budget and audience, according to the conversion ranges in the post — Casano's headline figure for the friction argument. The 10–30%, 1–5% and 1% figures are working ranges from account audits, not published benchmarks.

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In the attribution argument, Casano says LinkedIn should not be measured like search: it builds intent over months rather than capturing existing intent, so click-based accounting misses its influence.

• In the lead-generation argument, they advocate Lead Gen Forms and cite bottom-of-funnel demo conversion rates of 1–5% — a direct-response case for capturing intent inside the platform and moving it to sales.
• Both can be true of the same account: an awareness layer still needs a conversion mechanism.
• But a buyer choosing what to buy from Casano is choosing between two different jobs for the channel, and the posts do not reconcile them.
• Ask the client to clarify the campaign goal, then choose the argument that fits.

One post describes LinkedIn as structurally impossible to judge by last click; another says a pre-filled form turns a $2,000 lead into a $400 one. Together, they make an awareness case and a direct-response case.

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The commonly cited LinkedIn ROAS benchmark of 1.2x is calculated on annual contract value, or first-year revenue, so $100,000 a month in spend appears to return just $120,000 — a loss on its face.

• Their counter: customers do not leave after year one.
• Stretch the same deal across five years of retention and 1.2x becomes roughly 6x on lifetime value.
• Casano also claims to average 9x ROAS on year-one revenue across clients.
• They recommend two steps: pull average customer lifetime value, then recalculate ROAS against LTV rather than ACV, switching to influenced attribution if the account still runs on last click.

A 1.2x ROAS becomes about 6x when the same customer relationship is measured over five years of revenue, in Casano's worked conversion of the benchmark. They claim a 9x client average; ask for the supporting calculation.

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Their play: sync open opportunities from the CRM to LinkedIn as a dynamically updated audience through the HubSpot or Salesforce integration — no CSV exports or manual uploads — then retarget those accounts with thought leader ads.

• The key is the creative: posts written and published by actual customers from their own profiles and in their own voice, rather than testimonial graphics or quotes from the vendor's company page.
• Casano says this gives someone already weighing the purchase a different psychological trigger.
• They argue the play is cheap because the opportunity list is already in the CRM and the customer posts usually already exist.

A deal stalled at proposal stage is retargeted with a customer's own post about the product, served from that customer's profile rather than the vendor's page. The LinkedIn audience updates automatically as the CRM opportunity list changes.

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It is the clearest play in the set: a named tactic, a described setup, and an explanation of how it differs from ordinary retargeting.

Casano argues that late-stage deals stall when follow-up is one-sided.

• The proposed fix: take open opportunities in the CRM, sync them to LinkedIn as a dynamically updated audience through the HubSpot or Salesforce integration, and retarget those accounts with thought leader ads.
• The ads feature posts written and published by actual customers on their own profiles, not testimonial graphics or quotes from the vendor's company page.
• Casano says a peer's own post carries a different psychological weight for someone weighing a purchase than a marketing asset does.
• The play also uses opportunity lists already in the CRM and customer posts that usually already exist, making it mostly a matter of running the integration rather than creating new assets.

For brands planning a campaign

Test this pipeline-acceleration play against the reader's brief.

• First check whether the company's CRM plan and LinkedIn account support dynamic audience sync, and whether enough customers can be persuaded to post in their own voice.
• Without customer posts, the play lacks creative input and takes considerably longer.

Casano attributes underperforming LinkedIn Ads not to bad creative but to sending mobile users to a website.

• The friction builds at each step: see the ad, click, wait for the page to load, navigate the site, find the form, and type details with your thumbs.
• They call website visits and website conversions the platform's most expensive, lowest-converting objectives, and recommend LinkedIn Lead Gen Forms instead.
• These pre-fill name, email, job title, company, and phone number, then pass the details to the CRM in one tap.
• The post gives conversion ranges of 10 to 30 percent for gated mid-funnel content, 1 to 5 percent for bottom-of-funnel demo requests, and roughly 1 percent for a website landing page.
• Its headline example: a $2,000 cost per lead falling to $400 on the same budget and audience.
• These are Casano's working ranges, not published benchmarks; ask for campaign-specific results before using them in a forecast.

For brands planning a campaign

Directly relevant to the reader's LinkedIn Ads ROI brief: if the account is optimized for website conversions, the conversion-rate gap described here is the first thing to test.

• It could change cost-per-lead expectations by a factor of about five.
• Expect a trade-off in lead quality and sales follow-up: a pre-filled form asks less of the buyer than a page visit does.

Read it before the other two attribution posts; it lays out the mechanism they build on.

Casano's explanation is structural, not tactical.

• HubSpot and Salesforce use last-touch attribution by default, so when a $150,000 deal closes, Direct or Organic gets full credit and LinkedIn gets none.
• The post cites an average of 88 touchpoints before a B2B buyer closes, arguing that those interactions happen in the feed over months, before the buyer knows they're in a buying cycle.
• Casano's recurring point: LinkedIn is an awareness and consideration channel, so measure the pipeline and revenue it touched, not the channel the buyer came from last.
• The recommendation is to switch to an influence-based attribution model.

For brands planning a campaign

Bring this measurement change to a budget review with finance.

• Because it requires no media change, you can test it without new spend: set the CRM to an influenced attribution model and re-report the same campaigns.
• Treat the 88-touchpoint figure as a statistic cited in the post, not data about your own buyers.

Here Casano names tools, not just the principle.

• LinkedIn's Revenue Attribution Report connects to the CRM to show which deals and revenue were touched by accounts exposed to ads.
• Third-party platforms Fibbler, Dreamdata, and HockeyStack match closed-won deals to ad exposure, even when the prospect never clicked.
• Casano cites a Dreamdata finding that including paid engagement data in attribution modeling increases measured ROI accuracy by seven times, contrasting this approach with search, where buyers arrive with intent rather than developing it over time.
• The central question, they argue, is not where the buyer clicked last, but whether they were influenced.

For brands planning a campaign

Useful for designing a proposal's measurement layer: it names a native reporting path and three external tools, giving readers concrete options to scope and price. The seven-times accuracy figure comes from a vendor study cited in the post; check the study's methodology before including it in a client document.

It is the most numerical of the five and easiest to challenge, making it a useful starting point for a finance conversation.

Casano takes the commonly cited industry benchmark of 1.2x ROAS on LinkedIn and shows why it looks like a loss: at $100,000 per month in spend, 1.2x returns only $120,000.

• They say ROAS is calculated against annual contract value, or year-one revenue, even though customers typically stay longer.
• A five-year retention period turns the same 1.2x into roughly 6x on lifetime value.
• Casano also reports averaging 9x ROAS on year-one revenue across their clients, then recommends pulling average customer lifetime value and recalculating ROAS against LTV rather than ACV.
• Ask for the underlying client data before relying on the 9x figure; the post does not substantiate it.

For brands planning a campaign

This gives readers a specific, arithmetic way to re-present the same campaign results to a CFO without changing spend.

• It requires a defensible LTV or retention figure, so ask for one before using the play; companies without one will be stuck.
• Treat the 9x client average as a claim to verify in a sales conversation, not a planning benchmark.

Accounts send mobile users to website visits or website conversions, which they call the platform's most expensive and lowest-converting objectives.

• They trace the friction step by step: see ad, click, wait for page load, navigate, find form, type with thumbs.
• They also point out that the platform charges a premium for sending users off-site.
• Their fix is LinkedIn Lead Gen Forms, where LinkedIn supplies name, email, job title, company and phone pre-filled in one tap straight to the CRM.
• They report conversion ranges of 10–30% for gated mid-funnel content, 1–5% for bottom-of-funnel demos, and roughly 1% for website landing pages, with a $2,000 CPL becoming $400 at the same budget and audience.

Replace website-conversion objectives with Lead Gen Forms and gated mid-funnel assets to reduce form-filling friction on mobile.

For brands planning a campaign

For a demand-generation campaign, treat Lead Gen Forms plus gated content as the default structure, not a test variant. Use Casano's conversion-rate ranges as hypotheses to validate against the client's account history.

Examples: Source 1

HubSpot and Salesforce run last-touch attribution by default, so when a deal closes, the credit lands on Direct or Organic and LinkedIn receives zero.

• They describe LinkedIn as an awareness and consideration layer that builds pipeline for months before a form fill, citing an average of 88 buyer touchpoints before a close.
• Their recommended replacement is influenced pipeline and influenced revenue, tracked through LinkedIn's Revenue Attribution Report connected to the CRM, with third-party tools such as Fibbler, Dreamdata and HockeyStack matching closed-won deals back to ad exposure even when the prospect never clicked.
• They cite a Dreamdata finding that including paid engagement data in attribution modeling increases measured ROI accuracy by seven times.

Switch from last-click to an influence-based attribution model and report on influenced pipeline and influenced revenue.

For brands planning a campaign

Agree on an influence-based measurement plan before launch, and ask which CRM-connected reporting will be available. The argument depends on exposure data that the client's default attribution setup would otherwise discard.

Examples: Source 1 · Source 2

Casano takes the commonly cited 1.2x LinkedIn ROAS benchmark and points out that it's computed on ACV, meaning first-year revenue only.

• If customers stay five years, they say, that 1.2x becomes roughly 6x against lifetime value.
• They report averaging 9x ROAS on year-one revenue across their clients, presenting it as a stronger version of the same story.
• Their prescribed steps are to pull average customer LTV, recalculate ROAS against LTV rather than ACV, and switch to influenced attribution if the account is still on last click.

Pull average LTV, recalculate ROAS against LTV instead of first-year ACV, then move attribution from last click to influenced attribution.

Casano presents the 9x client figure and LTV conversion as reported experience, not audited benchmarks.

For brands planning a campaign

Use the ACV-versus-LTV reframe to structure the budget case, but supply the client's actual LTV and retention assumptions rather than reusing Casano's numbers. Treat the 9x claim as their reported client average, not a forecast.

Examples: Source 1 · Source 2

The process: sync open opportunities from the CRM to LinkedIn as a dynamically updated audience—no CSV exports or manual uploads when the account runs HubSpot or Salesforce—then serve thought leader ads: real posts written by actual customers, in their own voice and from their own profiles, not testimonial graphics published by the company page.

• They argue this creates a different psychological trigger for a buyer sitting on the fence.
• The play is easy to run because the list is already in the CRM and the posts usually already exist.

Sync open opportunities from the CRM into a dynamic LinkedIn audience and retarget them with customer-authored thought leader ads.

For brands planning a campaign

This is the most directly reusable play for a pipeline-acceleration brief, but it depends on customer-authored content and customers willing to be promoted. Scope that production before promising the retargeting sequence.

Examples: Source 1

Several posts are framed as ammunition for an internal argument—what to show a CFO before a LinkedIn budget gets cut, or why to fix attribution instead of defending spend. The implied reader is a practitioner who already believes in the channel and needs evidence to protect it.

Claim, diagnosis, numbered consequences, then one concrete next step, usually with cost or conversion figures attached.

Ask for linked data sources and account details before repeating the benchmark and client-performance numbers internally.

For brands planning a campaign

Expect short text posts built around one testable operating instruction, not broad thought-leadership essays. A sponsored brief should provide the account-specific data and platform access the format assumes the practitioner already has.

Examples: Source 1 · Source 2 · Source 3

They name LinkedIn Lead Gen Forms as a fix for mobile conversion friction and LinkedIn's Revenue Attribution Report as a way to see which deals ads touched.

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They describe syncing a HubSpot opportunity list straight into LinkedIn as a dynamically updated audience.

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Both teach through short LinkedIn posts built around a diagnostic claim and a practical next step.

Bland's argument spans events, communities, content and sales outreach, and recommends running several attribution models at once. Casano focuses on LinkedIn Ads operations, including Lead Gen Forms and customer thought leader ads.

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Both discuss CRM-connected conversion signals and account-level engagement.

Rowe writes from hands-on agency implementation and product adoption of DemandSense, emphasizing Conversions API plumbing and account lists. Casano offers broader budget and attribution reframes, along with specific ad plays.

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Putnam's examples come from Shopify and ecommerce accounts, not B2B LinkedIn demand generation. The overlap is in method and accountability, not channel.

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Bland makes a compact case for multi-touch attribution: B2B buyers accumulate many interactions, last-touch in isolation is a poor judge, and teams should run three to five attribution models at once rather than pick one.

• They use a concrete Avoma purchase journey — organic post, direct website visit, LinkedIn ads, G2 reviews, trial, paid conversion — to show why a single touchpoint misleads.
• Bland also lays out a discipline list: focus on a few things, simplify campaign structure, treat leading indicators as proxies, and diagnose the right part of the buying journey.
• For the same B2B SaaS decision, Bland is more useful than Casano when the blocker is measurement governance and campaign simplification, not a specific late-stage deal play.

Bland is less specific about LinkedIn Ads execution.

• The posts do not describe a thought leader ad play, CRM opportunity-list sync, a lead gen form test, a lifetime-value ROAS calculation, conversion ranges or client performance figures.
• For a runnable pipeline-acceleration tactic, ask for more operational detail.
• Bland's strength is the measurement frame and the warning against single-model attribution; the tradeoff is fewer concrete campaign steps for pipeline acceleration.

Examples: Source 1 · Source 2

Rowe's posts focus on what LinkedIn can see: it optimises toward form fills, while later stages — MQL, SQL, opportunity, closed won — sit in the CRM and remain invisible unless sent back.

• They describe wiring LinkedIn's Conversions API by hand with a Zap for each lifecycle stage, and say the setup works but breaks often and must be rebuilt per client.
• Rowe also argues that cost per click is a poor proxy and recommends answering the budget question with a list of companies that engaged, visited the site, and moved in the CRM.
• For the same B2B SaaS decision, Rowe is the stronger alternative when the company needs an implementation path for closed-won and qualified-pipeline events back into LinkedIn, or account-level engagement reporting instead of a CPC conversation.

Rowe's posts promote DemandSense, a product with a 30-day trial and no card.

• They frame a problem and product from the vendor's perspective, so treat the tool recommendation as a commercial option to evaluate, not a neutral market comparison.
• The posts also do not cover customer thought leader ads or Casano's LTV-based ROAS reframe.
• The tradeoff is depth in conversion plumbing and account reporting versus less breadth on the full LinkedIn ROI argument, alongside an explicit product pitch.

Examples: Source 1 · Source 2

Putnam describes a client's conversion rate falling from 3.24% to 1.62% after a cart redesign, with ROAS dropping too, and recommends a shared log of site changes to diagnose performance shifts.

• Putnam also describes reviewing an ad headline claiming “USA-Made Golf Hats” when the hats were not made in the USA, and warns that AI-generated variations need human checking for accuracy and brand fit.
• For a B2B SaaS LinkedIn Ads brief, those examples do not answer the attribution or pipeline-acceleration question; they become relevant if the account sells through Shopify or creative compliance is the live issue.

Putnam's posts focus on ecommerce and paid-social operations, not LinkedIn B2B attribution.

• Choosing Putnam for a LinkedIn B2B attribution campaign would trade Casano's LinkedIn-specific measurement and late-stage deal play for storefront conversion diagnostics and ad-copy review.
• That trade makes sense when the bottleneck is the website or creative approval, not when the CFO is questioning LinkedIn's contribution to pipeline.

Examples: Source 1 · Source 2