Jonathan Bland

They acknowledge the pressure from CEOs, CFOs and budget holders for a black-and-white answer, but say many interactions precede a purchase: events, following an employee on LinkedIn, a Slack community conversation, an ad, a blog post, an SDR email and a pricing-page visit.

• Their practical recommendation is to run several attribution models at once and treat last-touch as one data point, not the verdict.
• They also note that most people never click ads, so click data misses much of what moved a buyer.
• Leading indicators can help when they are defined correctly and explained in language a CFO or CEO accepts.

Bland walks through a personal purchase of call recording software: an organic LinkedIn post from Yaagneshwaran Ganesh at Avoma, a visit to Avoma’s site and pricing page, no purchase, LinkedIn ads seen over the following months, a return visit, a quick check of G2 reviews, then a trial and paid conversion.

• Nick Turner of Dreamdata advises companies in the same post to use three to five attribution models simultaneously rather than choose one, because relying on a single model leads to bad budget decisions.
• In a separate post collecting positions they say they have repeated for nearly four years, Bland makes numbered claims, including that the funnel exists but is not a linear drip that looks good in a presentation.

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Those clicks often would have happened anyway, flattering the channel’s headline metric.

• They reject the bluntest test — switching brand off for 60 days — because one large deal or a slow month can swamp the result, and propose a staged diagnosis that starts cheap and escalates.
• They also flag a communication problem inside the business: attributed pipeline in Salesforce or HubSpot will drop when brand spend is pulled back because there are no clicks and no UTMs.
• The number to watch is total pipeline, and the company has to agree to that before running the test.

Bland describes an audit of a Series D SaaS company that spent more than $1M on ads over six months.

• Branded search received credit for 51.4% of pipeline on 7.4% of ad spend — roughly $70,000.
• The four diagnostics are: check auction insights first, because if competitors bid on the name, pausing hands them the top spot; match brand-click leads against CRM records to see how many were already contacts or had open opportunities; pause brand in a few matched regional markets rather than nationally and compare total pipeline; and try a bid-down before a full pause to learn what the marginal dollar is worth.
• They note that the fight over the test is not always worth having; it depends on internal alignment.

Bland’s skepticism about single-source credit also appears in an earlier multi-touch post. A brand search audit turns that argument into a specific, numbered test procedure with figures attached.

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The point behind that habit is focus: do a few things well rather than eight things poorly.

• Simpler campaign structures are easier to manage, maintain and explain to a team.
• Bland groups the ten repeated claims under three principles: most buyers are not buying yet, so build for them; most of what moves buyers never shows up as a click, so measure accordingly; and fewer, simpler things done well beat complexity.
• Two claims challenge common B2B habits: audience size matters less than reaching the highest-fit segments on channels those buyers already use, and LinkedIn should not be judged by Google’s standards because paid social is not paid search.

The post lists ten positions Bland says they have made in different ways for close to four years.

• They include building content to be consumed inside the channel rather than pulling people off it, and the claim that missed pipeline targets are usually misdiagnosed rather than caused by the channel.
• Bland presents the list as a consistency check on their own output.

Bland presents the positions as stable over nearly four years, making consistency of message a deliberate choice.

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That means cutting poor-quality traffic, dropping low-intent leads, narrowing targeting and often paying more to reach better prospects.

• Better conversion rates, creative or more budget could eventually rebuild volume, they allow, but volume cannot remain fixed while the business also demands that unwanted volume be removed.
• The argument surfaces this conflict before a channel plan or test is built, rather than after the results come in.

Bland makes the constraint concrete with an analogy: wanting the same number of restaurant options while removing everything rated below 4.8 stars. They list the tradeoffs as a short checklist: cut low-quality traffic, drop low-intent leads, narrow targeting and pay more per prospect.

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Many effective marketing moves never appear on dashboards, they argue, and the obstacle is rarely marketer belief.

• It is an executive team that understands marketing, plus measurement and incentive structures that allow the spend without a heavy approval process.
• They stress that this is not a hall pass on accountability, making the idea a governance proposal rather than a rejection of measurement: how much budget can be spent without a direct-response justification, who approves it, and how it is reviewed?

Bland cites PostHog, heard on a webinar, as allocating something like 3 to 5 percent of budget to testing new things and doing weird things without a heavy justification process.

• One example is a viral campaign that ranked for the term "feet pics."
• PostHog is not a typical brand, they note; the repeatable part is the permission and incentive design, not the specific stunt.

This sits in tension with Bland’s measurement arguments elsewhere: the same writer who advocates multi-model attribution and incrementality testing also argues for a protected pool of spend that resists that scrutiny. They name the organisational condition needed for both approaches to coexist.

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The primary channels are Google Ads, Bing Ads, YouTube and Display, with other social channels a plus.

• Responsibilities include day-to-day campaign management, budgets, optimisations and impact test creation aimed at driving pipeline, as well as pitching new strategies, bidding approaches, and creative and copy tests rather than running programmes on autopilot.
• The post also names OmniOS, described as the team’s proprietary AI platform for managing paid media at scale.

A first-person job listing for a Senior Performance Marketing Manager working with B2B SaaS brands asks for in-house or agency-side experience, lists $85,000–$110,000 OTE, remote work and a US-based requirement, and mentions an in-person team off-site. The tone is informal, including a note that it was written at 7:30 a.m. on the author’s driveway.

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It is the fastest way for a newcomer to see the positions Bland returns to before reading the longer arguments.

Bland lists ten claims they say they have made in different words for almost four years.

• Among them: audience size matters less than targeting the highest-fit customer segments on the channels those buyers already use; LinkedIn is not Google, so paid social should not be judged like paid search; the buying funnel exists but is not a clean linear drip; last-touch attribution is one weak data point in B2B because buyers encounter many touchpoints; focus beats running eight things poorly; simpler campaign structures are easier to manage and explain.
• They group the ten under three ideas: most buyers are not buying yet, so build for them; most of what moves buyers never appears as a click, so measure accordingly; fewer simple things done well beat complexity.

For brands planning a campaign

Useful as a pre-brief checklist. If the client team disagrees with the last-touch and complexity points, a test-and-measurement engagement will struggle, whatever the tactics.

The post pairs an argument with a concrete, step-by-step example, making it the clearest statement of the measurement position.

Bland argues that B2B buyers rarely convert after a single ad, blog post, or event, and walks through a purchase of call-recording software to show why.

• The sequence: an organic LinkedIn post from Yaagneshwaran Ganesh at Avoma, a direct visit to the company site, no purchase, later LinkedIn ads, a return visit months afterward, a quick G2 review check, a trial, then a paid conversion.
• They quote Nick Turner at Dreamdata advising companies to run three to five attribution models at the same time rather than picking one, because relying on a single model leads to bad budget decisions.
• They also acknowledge the pressure from CEOs and CFOs for a black-and-white answer, and say the honest one is that almost nothing in B2B comes from one touchpoint.

For brands planning a campaign

The Avoma walk-through is a ready-made teaching artifact for a workshop: replay the same journey under last-touch, first-touch, and a multi-model setup to show how differently the credit lands.

This is the most operationally specific post in the set, with numbers, an audit, and four concrete diagnostics.

Bland describes an audit of a Series D SaaS company that spent more than $1M on ads over six months.

• Branded search was credited with 51.4% of pipeline on 7.4% of spend, roughly $70,000.
• They argue many of those clicks would have happened anyway, because people already in a sales cycle search the company name and click the ad instead of the organic link.
• Rather than follow the common advice to switch brand off for 60 days, they offer four starting points: check auction insights for competitors bidding on the name, match brand-click leads against the CRM for existing contacts and open opportunities, pause brand in matched regional markets rather than nationally, and bid down before pausing entirely.
• They warn that attributed pipeline in Salesforce or HubSpot will fall when brand spend is pulled back, and say to watch total pipeline instead.

For brands planning a campaign

The four diagnostics make an effective workshop outline for a paid search team. The harder part is internal: agreeing in advance to read total pipeline rather than the attributed number that is about to drop.

It is short, self-contained, and offers a useful metaphor for a recurring client request.

Bland opens with a line they present as common from clients: they want the same volume of leads but also more high-quality opportunities.

• They compare it to asking for the same number of restaurant choices while removing everything rated below 4.8 stars.
• Their argument: becoming more selective means removing options—cutting poor-quality traffic, dropping low-intent leads, narrowing targeting, and often paying more per prospect.
• They allow that better conversion rates, creative, or more budget could eventually rebuild some volume, but say volume cannot be a fixed requirement while marketing is asked to remove the volume the business does not want.

For brands planning a campaign

Useful for resetting a client brief before a test. State which volume the business is willing to lose; otherwise, the test will be judged against an impossible baseline.

A good entry point for a campaign focused on test budgets or brand investment.

Bland proposes a spend category for things that make sense for the brand without requiring a perfect ROI report.

• The example is PostHog, which they say allocates roughly 3–5% of its budget to testing new things and running odd experiments without heavy justification, including a viral campaign that ranked for the term “feet pics.”
• They argue that many effective marketing moves never appear on dashboards, and that the usual obstacle is not marketer belief but executive understanding and an incentive structure that lets tests happen.
• They say this should not mean marketing escapes accountability.

For brands planning a campaign

A concrete proposal for a workshop: ring-fence a test budget with lighter approval, and define success and failure before the spend happens.

It is the only post in the set that names the employer’s platform and channel mix.

A recruiting post for a senior performance marketing manager at Omni Lab, working with B2B SaaS brands.

• Bland asks for in-house or agency experience with B2B SaaS.
• The primary channels are Google Ads, Bing Ads, YouTube, and Display; other social channels are a plus.
• Responsibilities include day-to-day campaign management, budgets, optimizations, and impact test creation aimed at pipeline; pitching new strategies and bidding approaches; and supporting client calls with insight into what is and is not working.
• They mention OmniOS, Omni Lab’s proprietary AI platform for managing paid media at scale.
• The post lists $85,000–$110,000 OTE, remote work, and a US-based requirement.

For brands planning a campaign

Sets scope expectations for a talk or workshop. The day-to-day work described is campaign management across Google, Bing, YouTube, and Display, with pipeline as the goal; keep a measurement session tied to those channels rather than drifting into pure analytics.

In one post, they walk through a software purchase step by step: an organic LinkedIn post from a vendor employee, a direct site visit, LinkedIn ads over months, a second pricing check, peer evidence, G2 reviews, then a trial and paid conversion.

• They quote Nick Turner of Dreamdata advising teams to run three to five attribution models at once rather than picking one.
• Elsewhere, they list last-touch as one weak data point among several and note that most buyers never click at all, so click data misses much of what moved them.

Builds the argument from a firsthand buying story, a named third-party quote, and a step-by-step breakdown of touchpoints rather than platform dashboards.

For brands planning a campaign

A useful speaker or workshop voice for a paid media team that reports on last-touch and wants a plain-language case for multi-model measurement. Ask for the Avoma-style walkthrough as a live exercise: have the team reconstruct a real deal's touchpoints before the session, then map them against the models they currently run.

Examples: Source 1 · Source 2

The post explains that buyers already in a sales cycle search the company name, click the ad instead of the organic result, and paid search gets the credit.

• Rather than recommend the usual 60-day brand switch-off, they lay out a staged diagnosis: check auction insights for competitors bidding on the name, match brand-click leads against CRM records for existing contacts and open opportunities, pause brand in matched regional markets instead of nationally, and try a bid-down before a full pause.
• They note paid brand pipeline in Salesforce or HubSpot will drop regardless, so total pipeline is the number to watch.

Uses an audit dataset plus a four-step test sequence that moves from the cheapest diagnostic to the largest intervention, with matched-market comparison instead of a national switch-off.

Bland concedes the test is not always worth running, writing that it depends on internal alignment and that sometimes the argument is not worth having.

For brands planning a campaign

Strong fit for a paid search or demand gen team that suspects brand campaigns are absorbing credit for pipeline created elsewhere. Propose a workshop built around the four-step diagnosis, with the team bringing its own auction insights and CRM match data so the session ends with a regional test plan rather than a slide deck.

Examples: Source 1

They include: audience size matters less than reaching highest-fit ICP segments on channels they already use; LinkedIn is not Google and paid social is not paid search; the funnel is real but not linear; content should be built to be consumed inside the channel rather than pulling people off it; most people never click ads, so clicks and engagement are proxy metrics at best; last-touch attribution is weak in B2B; doing a few things well beats doing eight poorly; simpler campaign structures are easier to manage and explain; leading indicators are not vanity when defined correctly and communicated well; and missed pipeline targets are often misdiagnosed rather than caused by the channel. They summarise the set as building for buyers who are not ready yet, measuring what does not show up as a click, and favouring fewer and simpler things.

The post itself is the method: a list of repeated claims Bland uses to check their own output for consistency, grouped afterwards into three higher-level principles.

For brands planning a campaign

Useful as a readiness check for a brand brief.

• If the company's brief assumes follower count drives results, treats paid social like paid search, or insists on a linear funnel narrative, expect friction on those points and plan the workshop around them.
• The consolidated three-principle summary works as a handout.

Examples: Source 1

Their point is that becoming more selective means eliminating options by definition: teams cut poor-quality traffic, drop low-intent leads, narrow targeting, and often pay more per prospect. They allow that better conversion rates, creative, or more budget could eventually rebuild volume, but argue a campaign request cannot require both the same volume and the removal of unwanted volume at once.

Analogy used as a constraint test on a stated brief, followed by a checklist of the tradeoffs a quality push actually involves.

For brands planning a campaign

Use this when a marketing leader asks for higher-quality pipeline without accepting lower lead volume. Bland's framing gives the workshop a concrete way to surface that conflict before any channel plan is built.

Examples: Source 1

They cite PostHog, which they say on a webinar allocated roughly 3 to 5 percent of budget to testing new things, including a viral campaign ranking for "feet pics," with no heavy justification process.

• They argue marketers broadly agree with the idea yet are stuck with direct-response attribution and over-analysis of every CRM report.
• The practical enabler, they say, is an executive team that understands marketing, plus a measurement and incentive structure that allows the spend.
• They are explicit that this is not a hall pass on accountability.

Uses a named outside example to make an unmeasurable-spend argument, then names the organisational condition that has to exist before it can work.

PostHog is presented as a company unlike Bland's own audience, and the argument is about permission and incentive design rather than a repeatable test format.

For brands planning a campaign

Relevant for brands whose measurement culture blocks brand or creative tests.

• Frame it as a governance conversation: what percentage of budget can be spent without a direct-response justification, who approves it, and how is it reviewed.
• Ask Bland to bring the incentive and reporting structure angle, not just the creative example.

Examples: Source 1

The role covers day-to-day campaign management, budgets, optimisations, and impact-test creation to drive pipeline, plus pitching new strategies, bidding approaches, and creative tests.

• Channels named are Google Ads, Bing Ads, YouTube, and Display primarily, with other social channels a plus.
• It asks for in-house or agency experience supporting B2B SaaS brands, is US-based, pays $85,000–$110,000 OTE, and mentions OmniOS, described as the agency's proprietary AI platform for managing paid media scale.
• The tone is informal and first-person, written from Bland's driveway at 7:30 a.m.

Formats hiring posts as detailed first-person role descriptions rather than corporate recruiter copy, listing responsibilities, channels, pay, perks, and location in one place.

For brands planning a campaign

Shows Bland hiring and staffing paid media roles inside an agency serving B2B SaaS, with impact testing and pipeline outcomes built into the role they manage. Useful context if a brand is weighing an agency engagement, since the post names the channels and Omni Lab's internal tooling.

Examples: Source 1

The post also names OmniOS, Omni Lab's proprietary AI platform for managing paid media at scale.

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That overlaps with Bland's focus on multi-touch buying and measuring influence rather than clicks.

Casano's comparator posts are more tactical and platform-specific, built around LinkedIn lead-gen form conversion rates and replacing website-conversion objectives. Bland's evidence is more diagnostic and governance-oriented: staged brand-search tests, multi-model attribution, lead-quality tradeoffs, and budget for work that does not fit an ROI report.

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Like Bland, Jordan argues that channel value is often misread because the measurement is placed in the wrong frame.

Jordan's evidence centers on proving organic and AI-search value against paid benchmarks, including an organic cost-per-click comparison against high-intent paid keywords. Bland's subject is paid media, brand-search incrementality, and whether reported pipeline would have happened anyway.

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That is close to Bland's brand-search argument and interest in incrementality testing.

O'Brien's evidence is anchored in D2C AI discovery and enterprise apparel or CPG affiliate over-attribution, with quarterly AI visibility audits and affiliate incrementality baselines. Bland works in the B2B SaaS paid-media frame, with LinkedIn, paid search, lead quality, and executive budget rules as recurring subjects.

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His posts walk through the friction of sending mobile LinkedIn users to a website form: see the ad, click, wait for the page to load, navigate, find the form, and type details with a thumb.

• His fix is LinkedIn Lead Gen Forms, which pre-fill name, email, job title, company and phone.
• The numbers he gives are concrete: gated mid-funnel content at 10–30% conversion, bottom-of-funnel demos at 1–5%, a website landing page at about 1%, and a $2,000 cost per lead becoming $400.
• In his second post, Casano argues that last-touch attribution was built for banner ads in 2003, not a nine-month, seven-stakeholder B2B deal.
• He points to influenced pipeline and influenced revenue, LinkedIn's Revenue Attribution Report, and tools such as Fibbler, Dreamdata and HockeyStack.
• If the team already agrees attribution is the problem and needs the LinkedIn program fixed at the point of capture, Casano is the closer match for that half of the job.

Casano's measurement case rests on platform-side reporting and vendor claims, including a cited figure that adding paid engagement data increases measured ROI accuracy sevenfold.

• That makes the case for measuring influence, but does not give a team a test it can run on its own account against a fixed baseline.
• Bland's material offers a staged diagnostic: check auction insights for competitors bidding on the brand name, match brand-click leads against CRM records for existing contacts and open opportunities, pause brand in matched regional markets rather than nationally, bid down before switching off, and watch total pipeline rather than attributed pipeline.
• If the deliverable is a test design with a decision rule, Bland's session fits better; if it's a LinkedIn campaign and form rebuild, choose Casano.

Examples: Source 1 · Source 2

Jordan puts LLM visibility data on the same slide as paid and affiliate, using the same format and metrics so the comparison needs no explanation.

• They report one campaign where non-branded traffic from organic plus AI search converted at 8%, matching the paid channels and people who already knew the brand, and describe the proxies to watch: when LLM visibility rises, branded search and direct traffic spike.
• The second post makes the cost argument directly: high-intent keywords run $3.50–$8.50 per click on paid, while the same keywords sourced organically come in under $0.50 by the end of year one, calculated by dividing total organic clicks over twelve months by total retainer spend.
• That's a ten-minute spreadsheet exercise.
• If the client's question is why the organic retainer should survive next quarter, Jordan answers in the same language the paid team uses.

Jordan's frame is organic and AI visibility versus paid search cost, using a retainer cost-per-click comparison.

• It does not cover paid brand incrementality, brand pause testing or multi-model attribution across paid channels, the core need here.
• Bland focuses on paid media: brand spend credited with 51.4% of pipeline on 7.4% of spend, four staged diagnostics, and a matched-market test that names the comparison metric and duration up front.
• Choose Jordan when the budget at risk is organic and the argument is cost per click against paid; choose Bland when it's paid brand spend and the argument is incrementality.

Examples: Source 1 · Source 2

The affiliate post is the most directly transferable: a performance lead at a $400M+ apparel brand called the channel's 12x blended ROAS fake, and an incrementality test put the lift within a margin of error.

• O'Brien lays out the mechanics (last-click platforms hand credit to the affiliate because the affiliate link is usually the last touch, networks self-report attribution, and nobody in the chain has an incentive to question it), the structural cause (affiliate and measurement teams are run separately), the deepest loss (brand-loyal repeat buyers using coupon sites, where the brand pays full commission to retain a customer it already had), and the reported outcome of switching to an incremental metric (20% to 50% of previous affiliate spend not generating incremental revenue).
• If the client wants the same question asked of affiliate instead of brand search, or a consumer-commerce analogue to persuade a skeptical finance lead, O'Brien's material is more relevant.
• The AI-discovery post adds another entry point for consumer brands watching direct traffic behave oddly.

O'Brien's posts focus on commerce and CPG, not B2B SaaS pipeline.

• The $400M+ apparel call, coupon-site example and AI product-discovery statistics (ChatGPT past a billion weekly users, AI Overviews reaching 2.5 billion people a month, 37% of consumers starting with AI, Reddit the most-cited source at 40%) need translation for a SaaS demand-gen team measuring pipeline in Salesforce or HubSpot.
• Bland's posts speak directly to that team: Omni Lab's day-to-day channels are Google Ads, Bing Ads, YouTube and Display, with pipeline as the goal and impact-test creation as part of the role.
• Choose O'Brien when affiliate or consumer discovery is the live channel decision; choose Bland when the need is a B2B SaaS paid media and attribution audit.

Examples: Source 1 · Source 2