# Tom Casano

Tom Casano shares practical advice on LinkedIn Ads for B2B companies, including lead generation, pipeline acceleration, and measuring ad influence on revenue.

- Profile: https://b2binfluencer.marketing/creators/tom-casano/
- LinkedIn: https://www.linkedin.com/in/tomcasano
- LinkedIn followers: 15,367
- Topics: b2b-marketing, b2b-demand-generation
- Based on 12 dated posts, latest 2026-10-08

## Summary

They offer practical LinkedIn Ads advice for B2B lead generation, pipeline acceleration, and measuring ad influence on revenue.

- Best for: B2B SaaS teams with stalling late-stage deals and a need for measurable LinkedIn Ads pipeline.
- Not for: It does not fit general awareness, long-form video or audio, or offline buying committees without account data.
- Check first: Ask for account data behind their figures and recent comparable campaign results.

### Takeaways

- LinkedIn Ads can look unprofitable because last-touch attribution hides their contribution.
- Remove mobile friction with pre-filled Lead Gen Forms before changing creative.
- Retarget open deals using customer thought leader posts, not company-made testimonials.

## Main ideas

- **LinkedIn Ads look unprofitable because of last-touch attribution, not the channel**: Casano's central argument is that executives who call LinkedIn Ads a waste are misreading the numbers. 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.
- **Remove campaign friction before rewriting the creative**: Casano's most concrete campaign argument: LinkedIn Ads underperform because of account configuration, not weak ads. 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.
- **Two roles for one channel: influence layer and direct lead capture**: The posts pull in different directions. 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.
- **Recalculate return against customer lifetime value, not year one**: Casano answers a finance objection with arithmetic, not tactics. 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.
- **Spend that may already work: retarget open deals with customer thought leader ads**: Casano's most tactical priority is targeting later-stage deals that have gone quiet. 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.

## Work to read

- [Retarget open deals with thought leader ads from real customers](https://www.linkedin.com/posts/tomcasano_your-late-stage-deals-dont-need-another-activity-7463620763489714176-ME2W)
- [Mobile friction and the case for LinkedIn Lead Gen Forms](https://www.linkedin.com/posts/tomcasano_80-of-linkedin-users-are-on-mobile-your-activity-7467968671517212672-8i6z)
- [Why last-touch attribution hides LinkedIn's contribution](https://www.linkedin.com/posts/tomcasano_stop-defending-linkedin-spend-to-your-cfo-activity-7466145681758466049-AcUM)
- [Measuring influenced pipeline with CRM-connected reporting](https://www.linkedin.com/posts/tomcasano_proving-your-linkedin-roi-is-embarrasingly-activity-7465412856654647296-p5gp)
- [Reframing ROAS against lifetime value instead of year-one revenue](https://www.linkedin.com/posts/tomcasano_your-cfo-thinks-your-linkedin-ads-are-losing-activity-7465769837022085120-5V8C)

## Brand relationships

- **LinkedIn** (mention)
- **HubSpot** (mention)
- **Salesforce** (mention)
- **Dreamdata** (mention)
- **Fibbler** (mention)
- **HockeyStack** (mention)

## Partnerships

Tom Casano's posts do not say whether they offer a partnership or sponsorship program. The five posts are tactical breakdowns of LinkedIn Ads: lead gen forms and mobile friction, retargeting open opportunities with thought leader ads, last-touch versus influenced attribution, and recalculating ROAS against lifetime value. None announces a sponsorship, carries a paid-partnership disclosure, lists a media kit or rate card, or describes booking terms or lead times. Mentions of LinkedIn's Revenue Attribution Report, HubSpot, Salesforce, Fibbler, Dreamdata, and HockeyStack appear as tools they recommend in their own advice, not as proof of paid relationships. Buyers should ask directly whether a sponsored placement is available, at what price, and under what disclosure before including one in a campaign plan.

## Alternatives

- Jonathan Bland: Choose Jonathan Bland over Tom Casano when the immediate problem is getting finance and leadership to agree on how to measure LinkedIn. 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.
- Justin Rowe: Choose Justin Rowe over Tom Casano when the bottleneck is the data connection between LinkedIn and the CRM. 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.
- Joe Putnam: Joe Putnam is the better choice when the buyer's task shifts to a Shopify or ecommerce storefront and the immediate risk is a site change or an ad-copy claim. 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.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/