Gonçalo Prates shares practical advice on paid media for B2B SaaS, with a focus on connecting advertising decisions to pipeline, revenue, and sales activity.
Gonçalo Prates is most useful for connecting B2B SaaS paid media decisions to pipeline, revenue, and sales data.
Best for
A B2B SaaS team with paid media running that needs to link lead generation to pipeline.
Not for
Consumer or lifestyle campaigns targeting general consumers, or work needing verified job title, seniority, or region data.
Key takeaways
Judge paid media by money in versus money out, not by leads.
Set cost targets from your own funnel, not channel benchmarks.
ABM fails when sales is not working the same accounts right now.
Check first: Confirm whether they offer paid partnerships or sponsorship, and ask for audience data before commissioning.
Main ideas
Judge paid media by money in versus money out, not by leads · Set cost targets from your own funnel, not channel benchmarks · Diagnose the funnel before changing campaigns
Gonçalo Prates shares practical paid-media advice for B2B SaaS, with a consistent question at its core: does advertising help the business create pipeline, or just improve platform metrics?
Their posts connect campaign choices to CRM data, sales activity, and business economics.
They also discuss attribution, account-based marketing (ABM), and emerging ad formats, including ChatGPT ads.
Readers will find concrete ways to assess performance, explain trade-offs to leadership, and decide when a campaign needs a change of course.
Prates turns measurement into decisions, showing how to work backward from a pipeline-to-spend target, average contract value, and lead-to-opportunity rate to set a defensible cost-per-lead target.
They explain why hitting a Google Ads cost-per-action goal can still mean poor performance if the account is optimizing for the wrong conversion.
For ABM, they look beyond targeting lists: compare the accounts Sales is actively pursuing with their actual ad exposure, and reassess when Sales cannot identify positive signals.
Their view is not that every campaign must generate a last-touch lead: awareness ads may reach the right accounts before they convert, and attribution is more persuasive when it helps examine individual customer journeys rather than relying on a single pipeline total.
In posts about ChatGPT ads, they recommend grouping campaigns around distinct buyer situations so results can still guide decisions when search-term reporting is unavailable.
This advice is especially useful to marketers who need to connect paid-media choices to pipeline performance and explain them across marketing, sales, and finance.
They also discuss these ideas on Performance Therapy, including episodes with Silvio Perez.
Gonçalo Prates argues across most of their posts that B2B SaaS paid media should be judged by pipeline, pipe-to-spend and cost per opportunity—not leads, clicks or platform-reported conversions.
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They apply that logic to funnel arithmetic built from CRM data, attribution claims that hold up in executive meetings, ABM programs tied to sales timing, and the emerging challenge of describing buyer situations for LLM ad platforms.
Judge paid media by money in versus money out, not by leads
Across several posts, Prates argues that executives care about pipeline, pipe-to-spend and ROI, while marketing teams report MQLs and cost per MQL.
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Ad channels want credit and pixels overcount, so marketers who do not actively monitor cost per opportunity or pipe-to-spend will eventually be exposed when performance dips or a new executive looks under the hood.
One post asks whether a CFO would keep funding Google non-brand search after seeing the CRM sub-statuses behind the MQLs — “Too small,” “Not interested,” “Not ICP” — and says that, in the accounts they have seen, cutting non-brand often has little or no effect on the numbers leadership cares about.
Set cost targets from your own funnel, not channel benchmarks
Prates argues that a maximum cost per lead should be reverse-engineered from CRM and contract data — average lead-to-opportunity rate, average contract value and a target pipe-to-spend ratio — so the ceiling reflects the business model.
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The same logic applies when comparing campaigns: a campaign with a lower lead-to-MQL conversion rate can still be the right buy if its leads are cheaper, because cost per MQL matters more than cost per lead.
Their worked example uses a 10% lead-to-opportunity rate, $30,000 average contract value and a pipe-to-spend target of 3, producing a maximum CPL of $1,000. They note that the calculation can also use lead-to-closed-won rate and a target ROI.
Asked what they would do in their first 90 days leading paid media at a B2B SaaS company, Prates lays out a sequence: define the actual pipeline gap in SQLs and MQLs, find the small share of campaigns or keywords already producing pipeline, then fix the funnel step that is leaking.
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Campaign-structure changes come last because they can hurt volume and reset learning.
The leaks to check are landing-page conversion rate, demo-request-to-meeting rate, MQL-to-SQL and SQL-to-opportunity. Prates says teams often push for more launches when the real problem is further down the funnel.
Attribution screenshots need deal-level context, and scorecards decide what counts
Prates argues that influence dashboards are only as meaningful as the thresholds behind them: a handful of impressions or clicks can make LinkedIn look like a huge pipeline driver, but that claim will not survive five minutes of executive scrutiny.
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They prefer attribution tools that add journey context to deals already in the CRM: CRM data says what happened, while customer journeys explain how paid media contributed. Measurement design matters too: if leadership counts only last-touch conversions, awareness work that never produces a form fill will not make the scorecard.
A client asked for pipeline and revenue from the book of business, the deals paid media influenced and a clickable journey for each.
Prates points to Fibbler's customer-journey feature as an underrated way to show that a deal was created after people at one company had been exposed to LinkedIn ads for 190 days, across thousands of impressions and several engagements.
The basketball comparison drives home the scorecard point: if only dunks scored, nobody would run plays for three-pointers.
ABM fails when sales isn't working the same accounts right now
Prates argues that an ABM program can follow the playbook and still waste money if marketing targets accounts sales is not actively pursuing or accounts that cannot buy yet.
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They suggest comparing the sales team's active target list with LinkedIn impression data and setting a defined pause point instead of letting spend continue on ambiguous feedback. Weeks of “not sure” from sales are worse than a clear no, in their view, because they keep spend running without evidence of impact.
A marketing leader asked how ABM was going discovered that accounts sales was actively pursuing had 20, maybe 50 impressions company-wide over 90 days. Prates lists reasons accounts may be out of market — a renewed contract, leadership change, budget already allocated, restructuring, board pressure to cut costs, recent layoffs — and says that if sales still sees no signals after three months, a common outcome is $50k–$100k spent on LinkedIn while leadership starts asking questions.
Google Ads optimizes whatever you tell it to — check your conversion actions
Prates argues that Google is excellent at executing the instruction it is given, so the failure usually lies in how success is defined, not in the bidding.
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When several conversion actions are set as primary, Google treats them as equal and can deliver cheap form submissions instead of the conversion that predicts pipeline. The fix is to find the one conversion that actually predicts pipeline and has enough volume, then optimize only for that.
They describe an account with a $1,000 target CPA where conversions came in below target but pipeline did not move because multiple conversion actions were set as primary. The analogy: telling an intern to book as many meetings as possible without saying who with.
LLM ads reward describing buyer situations, not selecting audiences
Prates sees ad platforms converging on broader, less explicit targeting, with ChatGPT ads asking which conversations a brand wants to be part of rather than who it wants to reach.
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That raises the cost of weak customer understanding: effective targeting uses phrasing that sounds like the buyer's own problem. Since ChatGPT ads do not provide a search terms report, they advise building each ad group around a single buyer situation, ideally sourced from sales conversations.
A client split campaigns into pain-point, problem-focused and solution-comparison buckets.
The problem-focused bucket took most of the spend, while solution comparison barely registered.
Hints phrased as real questions — “How do I get reps to actually use the content we create” — worked.
The post gives three ways to phrase the same “scale the paid media program” situation inside one ad group.
An earlier post frames the shift as a threat to marketers who relied on platform controls; a later post works through the practical consequence: how to structure ad groups when you cannot see search terms.
Performance marketers should become performance advisors
In a hiring post, Prates defines advanced paid-media work as strategic excellence (understanding client economics, measurement and real constraints), channel expertise (planning and analyzing campaigns while linking platform decisions to CRM data), pattern recognition and judgment built from exposure across clients, and communication that turns complexity into clarity for stakeholders.
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They argue that simply knowing how to run ads is no longer enough as the landscape moves quickly.
They say the four pillars emerged from reviewing accounts, listening to client calls and looking for patterns behind both the strongest work and the situations where things went wrong.
It answers a newcomer’s first practical question: where should paid media work start when the goal is pipeline, not just leads?
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Gonçalo Prates outlines what they would do in the first 90 days leading paid media at a B2B SaaS company: define the pipeline gap in SQLs and MQLs, find the 80/20 of campaigns or keywords already producing pipeline, fix the leaking funnel step, and treat campaign-structure changes as a last resort. The post names leaks such as landing-page conversion rate, demo-request-to-meeting rate, MQL-to-SQL, and SQL-to-opportunity.
For brands planning a campaign
Use the three-step sequence to scope a brief: identify the pipeline gap, audit existing winners, and specify the conversion step to improve before proposing new creative or campaigns.
It explains how MQL volume can hide poor pipeline quality, a common gap between marketing reports and executive decisions.
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Prates asks whether a CFO would keep funding Google Non-Brand if shown pipeline, pipe-to-spend, and ROI instead of MQLs and cost per MQL. They point to CRM sub-statuses such as 'Too small,' 'Not interested,' and 'Not ICP,' and write that reducing Non-Brand often has little or no impact on the numbers leadership cares about.
For brands planning a campaign
When assessing paid search, consider CRM sub-statuses and pipeline impact, not just MQL count. A campaign may look defensible internally while failing the CFO’s economics test.
It offers a concrete diagnostic for a common symptom—cheap leads that do not turn into pipeline—and two questions to ask in the next meeting.
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Prates describes an account where the team set a $1,000 target CPA, got conversions below that cost, and still saw little pipeline impact.
The cause: multiple conversion actions were set as primary, so Google optimized for all of them equally.
The post compares this to telling an intern to book as many meetings as possible without specifying who with: the calendar fills with the wrong people.
For brands planning a campaign
Before scaling Google Ads, confirm which single conversion predicts pipeline and whether it has enough volume. Do not let form fills or secondary actions count as equal primary signals.
It distinguishes a dashboard claim from evidence an executive can inspect, often the real barrier to increasing LinkedIn budget.
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Prates criticizes LinkedIn attribution screenshots that count influence after as few as 20 impressions, 3 clicks, and 3 engagements.
They describe a client who wanted pipeline and revenue from the book of business, influenced deals, and clickable customer journeys.
They highlight Fibbler's customer-journey feature, with an example of a deal created after people from one company had been exposed to LinkedIn ads for 190 days across thousands of impressions and several engagements or clicks.
For brands planning a campaign
For LinkedIn campaigns, collect CRM deal journeys and company-level exposure history to support the budget request. Present influenced deals with context, not just an aggregate ROI number.
It explains how an ABM program can look right on paper but waste spend when sales and marketing are not aligned on timing.
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Prates describes a marketing leader asked how ABM is going, who finds that accounts Sales was actively pursuing had only 20, maybe 50 impressions company-wide over 90 days on LinkedIn. The post lists reasons target accounts may not be in-market: a renewed contract, leadership change, budget already allocated, restructuring, board pressure to cut costs, or recent layoffs.
For brands planning a campaign
Before increasing ABM spend, compare the sales team’s active target list with LinkedIn impression data. If actively pursued accounts are not being reached, fix alignment and delivery before adding budget.
It offers a stop rule for ABM programs, a decision many marketers put off until leadership asks questions.
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Prates argues that repeated “not sure” answers from sales about positive signals—better open rates, higher response rates, softer interest, or target accounts in SFDC activity logs—may mean the program is failing. Three months in, “not sure” is a red flag; a common outcome is spending $50k-$100k on LinkedIn while sales still feels no impact.
For brands planning a campaign
Build a pause criterion into ABM briefs: define the sales-observed signals and review point, so 'not sure' does not extend spend for months.
It shows how LLM ads shift the marketer’s job from selecting audiences to clearly describing buyer problems.
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Prates observes that ad platforms are moving from precise targeting controls toward broader signals.
ChatGPT ads ask “which conversations do you want to be a part of?” rather than whom to target.
One client split campaigns into pain-point, problem-focused, and solution-comparison buckets.
The problem-focused bucket got most of the spend, while solution-comparison barely got any.
Phrasing context hints as real questions, such as “How do I get reps to actually use the content we create,” worked.
For brands planning a campaign
For LLM ad tests, phrase context hints as questions buyers would ask about their own problems, and put fundamentally different buyer situations in separate ad groups.
It answers a practical setup question for a new ad channel with limited reporting.
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Prates notes that ChatGPT Ads lacks a search terms report like Google Ads, making campaign structure important.
Rather than mixing demand generation, conversion tracking, attribution, agency hiring, and CPL reduction in one ad group, build each around one buyer situation, ideally drawn from sales conversations.
The post gives three phrasings of the same situation—scaling a paid media program—and says that if the ad group gets 20 form submissions, its structure still provides direction, even without showing which hint triggered each one.
For brands planning a campaign
For early ChatGPT Ads work, define one buyer situation per ad group and use multiple context hints within it. Don’t mix unrelated problems and then try to interpret the results.
It shows how Prates defines advanced paid-media work beyond running ads, helping marketers assess the depth of the advice.
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Prates outlines four pillars for a senior performance marketer: strategic excellence, channel expertise, pattern recognition and judgment, and communication and executive presence. The role requires understanding client economics and measurement, planning and analyzing paid media while connecting platform decisions to CRM data, recognizing patterns across clients, and turning complexity into clarity for stakeholders.
For brands planning a campaign
When evaluating a paid-media partner or hire, test their ability to diagnose business economics and analyze CRM-linked data, not just execute campaigns.
It shows how campaigns with different lead costs can perform equally well downstream.
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Prates writes that a CFO wants customer acquisition at an economically viable cost, not leads at $200 versus $400.
A campaign with a 5% Lead-to-MQL conversion rate can still work if it delivers cheaper leads; another with 15% conversion can justify a higher cost per lead at the same cost per MQL.
The post advises working backward from an affordable CAC to determine what must be true at each funnel stage.
For brands planning a campaign
When planning campaigns, compare blended cost per MQL or CAC rather than CPL alone, and model the funnel rates that make different lead costs viable.
It explains why upper-funnel LinkedIn work can look wasted when judged by leads.
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Prates compares LinkedIn to basketball: if only dunks score, nobody runs plays for 3-pointers.
Awareness campaigns are the 3s—cheaper CPMs that put a brand in front of the right accounts before a form fill.
But if leadership counts only last-touch conversions, those campaigns won’t make the scorecard, even when they’re the right play.
For brands planning a campaign
For awareness or demand-creation campaigns, agree on leading indicators such as target-account reach and engagement before launch; don’t judge them solely by last-touch lead volume.
Prates frames paid media in terms of money in versus money out: pipeline, pipe-to-spend, cost per opportunity and CAC.
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They warn that MQLs, CPL and platform-reported conversions can look healthy even when lead quality or pipeline impact is weak. They recommend working backwards from what the business can afford to pay for a customer and identifying which conversion actually predicts pipeline.
CFO-style questions, funnel math and reverse-engineered cost targets.
For brands planning a campaign
A brand can assess whether Prates will defend paid media budgets in CRM and pipeline terms, and build measurement around business outcomes rather than platform metrics.
In a scenario about increasing pipeline in 90 days, Prates first defines the actual gap in SQLs or MQLs, then identifies the 80/20 of what is already producing pipeline and improves the leaking funnel stage.
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They treat campaign structure changes as a last resort because they can hurt volume and reset learning.
A 90-day plan built around defining the gap, reviewing existing performance and finding funnel leakage.
For brands planning a campaign
Useful for a brand seeking a diagnostic or rescue engagement before adding campaigns or channels.
Prates shows how average lead-to-opportunity rate, average contract value and target pipe-to-spend ratio determine a maximum CPL.
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They also explain how to substitute lead-to-closed-won rate and target ROI. The goal is to set paid media cost guardrails based on the business model, not channel benchmarks.
CRM inputs and simple arithmetic to derive maximum allowable CPL.
For brands planning a campaign
A brand with known ACV and conversion rates can use Prates to set defensible CPL and pipeline targets before scaling spend.
Prates criticizes screenshots claiming LinkedIn ads generated huge pipeline without context, noting that attribution settings determine what counts as influence.
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They argue that CRM data shows what happened, while customer journeys help explain how paid media contributed. They also highlight Fibbler’s customer journey feature as an underrated way to give leadership supporting evidence.
CRM records, customer journey context and scrutiny of attribution thresholds.
For brands planning a campaign
Consider Prates for executive reporting and attribution hygiene, especially if the brand needs to defend LinkedIn or paid media influence beyond last-touch dashboards.
Prates describes ABM programs that follow the playbook but fail because sales is not actively working the target accounts.
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They cite renewal cycles, leadership changes, allocated budgets, restructuring, board cost pressure and layoffs as reasons accounts may not be in market. If sales sees no positive signals after three months, they recommend pausing the program and having tougher conversations: a continuing “not sure” is worse than a clear no.
Sales feedback signals, LinkedIn impression data and a defined pause point.
For brands planning a campaign
Relevant for a B2B SaaS brand whose ABM program needs stronger sales alignment, account timing checks and earlier stop-or-continue decisions.
Prates asks whether a CFO would keep funding Google non-brand performance after seeing the CRM sub-statuses behind the MQLs, such as too small, not interested or not ICP.
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They argue that reducing non-brand often has little impact on the numbers leadership cares about. In a separate post, they explain that multiple primary conversion actions lead Google to optimize for cheap form submissions rather than the conversion that predicts pipeline.
CRM sub-status review and conversion-action audit.
For brands planning a campaign
A brand can assess Prates for forensic Google Ads and CRM reviews, particularly when MQL quality is poor or Google appears to be optimizing for the wrong action.
Prates says ad platforms are moving toward broader or less explicit targeting, while ChatGPT ads ask which conversations a brand wants to be part of.
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A client structured campaigns around pain points, problem-focused intent, and solution comparison.
Problem-focused intent received most spend; solution comparison barely did.
They advise phrasing context hints as real questions and avoiding mixed buyer situations in one ad group because ChatGPT ads lack a search terms report.
Intent buckets and real-question context hints tested in client campaigns.
Prates notes this is early evidence; they were still testing ChatGPT ads.
For brands planning a campaign
Useful for a brand testing emerging LLM ad inventory that needs help structuring intent buckets, context hints and ad groups without search-term data.
In a hiring post, Prates defines four pillars for the performance marketer role: strategic excellence, channel expertise, pattern recognition and judgment, and communication and executive presence.
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They believe performance marketers need to evolve into performance advisors because knowing how to run ads is not enough.
Four-pillar role definition.
For brands planning a campaign
Prates’s leadership standard for paid media talent can help brands evaluate an agency partner or consider team training and role design.
Prates uses a basketball analogy: if only dunks score, no one will run plays for three-pointers.
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They argue LinkedIn awareness campaigns may never produce last-touch leads but can still put a brand in front of the right accounts. If leadership counts only last-touch conversions in the CRM, those awareness campaigns will not count.
For brands planning a campaign
Brands should evaluate Prates for campaign measurement design, especially when awareness or demand-creation work is undervalued by a last-touch scorecard.
Gonçalo Prates leads the Performance team at AdConversion, whose YouTube channel carries the Performance Therapy series they co-host.
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Other named products and events appear as recommendations or speaking appearances, not as disclosed commercial ties: Fibbler is praised as an attribution tool, and Prates will speak about ChatGPT ads at CyberMarketingCon. For diligence, ask about any client, sponsor, affiliate, investment or owned-business ties; none are named in the posts.
AdConversion
Employment
Prates writes about taking AdConversion’s Performance team in a direction they define, sets out the four pillars used to judge performance marketers, and announces that AdConversion is hiring a Senior Performance Marketing Manager.
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They co-host the first episode of the Performance Therapy series, distributed on AdConversion’s YouTube channel.
Gonçalo Prates writes about paid media for B2B SaaS, and almost every post asks one question: what did the spend actually produce?
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Start with the posts that build cost targets from CRM data — average lead-to-opportunity rate, average contract value and a pipe-to-spend goal — to see the framework behind the rest.
Then follow the attribution and ABM posts on defending paid media’s influence in executive conversations, the ChatGPT ads posts on describing buyer situations instead of picking audiences, and the hiring post for the standard they apply to performance marketers.
The recurring focus is pipeline, pipe-to-spend and cost per opportunity, not leads, clicks or platform-reported conversions.
Both write for B2B companies weighing whether their paid media agency is delivering pipeline.
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They argue that accountability should extend beyond the form fill to what happens to leads inside the business.
Koffler frames the problem around the agency–client handoff and the cost of building the function in-house, while Prates uses CFO-style pipeline arithmetic: cost per opportunity, pipe-to-spend and CRM conversion rates.
Both treat B2B paid media as a full-funnel system, not a set of campaigns, and distinguish demand creation from demand capture, with different messaging for each.
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Bowen focuses on campaign creative direction and how messaging shifts by funnel stage, while Prates focuses on measurement, cost targets and the results leadership will accept as paid media’s contribution.
Both write for marketers who suspect their ad reporting misses part of the story.
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They argue that platform metrics should connect to revenue and to what happens after a lead arrives.
Chapman writes from the perspective of a UK PPC lead-generation agency, offering step-by-step explanations of broken tracking and which metrics to report. Prates goes deeper into CRM funnel arithmetic, ABM and defending budgets in executive conversations.
The only measured audience figure published on the LinkedIn profile is a follower count: 3,776, recorded on 10 October 2026.
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Gonçalo Prates's posts are aimed at people who already buy or operate paid media for B2B SaaS: they cover funnel and cost-per-MQL arithmetic, decisions about continuing or stopping ABM and Google non-brand spend, and how to defend paid media numbers to a CFO or executive team.
Two posts show the assumed reader: the hiring post describing four pillars for a senior performance marketer, and the ChatGPT-ads posts that presuppose live campaigns and interest-specific ad-group structures.
The content points to an audience of in-house and agency paid media practitioners in B2B SaaS, rather than general business readers or consumer marketers.
This is a reasonable fit for campaigns aiming to reach B2B SaaS paid media and demand-generation practitioners with an educational or point-of-view message.
It is a weak basis for campaigns requiring verified job titles, seniority, or geography, such as targeting VP-level demand-generation leaders in one region.
Ask for recent post and campaign results on topics, depth, and formats comparable to the placement, plus any audience survey covering job function, seniority, company size, and geography.
LinkedIn followers
3,776
LinkedIn profile record with this the research · 2026-10-10
Gonçalo Prates's posts do not offer a partnership or sponsorship.
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Across 13 LinkedIn posts published between August 2025 and October 2026, commercial references relate to their own work: a hiring post for a senior performance marketing role on their team at AdConversion; repeated pointers to the AdConversion YouTube channel and the Performance Therapy podcast they co-host with Silvio Perez; and a note that they were speaking about ChatGPT ads at CyberMarketingCon in November.
None is presented as a sponsored placement, rate card, or partnership package.
The posts state no terms, deliverables, pricing, audience guarantees, or disclosure practices.
A buyer interested in working with them would need to start a direct conversation through their LinkedIn profile and ask about scope, formats, and terms; any collaboration would be agreed case by case.
Gonçalo Prates is a strong fit for a B2B SaaS team with paid media running that needs to connect lead generation to pipeline.
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Their advice starts with business economics, CRM data and funnel diagnosis—not a new campaign for its own sake.
In their 90-day pipeline plan, they recommend defining the pipeline gap, identifying what already produces pipeline, then fixing the funnel step that leaks before changing campaign structure.
Their CPL calculation shows how to set a lead-cost ceiling from a company’s own conversion rate, contract value and pipeline target.
Their work is especially relevant when lead volume looks healthy but sales quality or pipeline is weak.
They show why CRM sub-statuses such as “too small” or “not ICP” can matter more than cost per MQL, and why a platform’s conversion settings can steer spend toward form fills rather than pipeline-relevant leads.
Their Google Ads example explains the risk of giving the platform multiple primary conversions.
For an ABM campaign, their sales-alignment example makes the case for checking whether sales is seeing meaningful signals—and being willing to pause when it is not.
For a diagnosis, provide anonymized CRM and campaign data and ask for decision criteria and practical next steps.
Agree the collaboration scope and deliverables directly, as no particular paid-collaboration format is published.
When to choose a specialist
Prates’s published work centers on B2B SaaS paid media, pipeline measurement, Google Ads, LinkedIn ads and ABM.
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They are a poor fit for a consumer or lifestyle campaign whose success depends on reaching general consumers: their examples do not demonstrate that audience or subject matter.
They are also not supported by published data as a choice for campaigns requiring verified reach among a specific job title, seniority level or region.
The available follower count does not establish those audience attributes; ask for audience data before commissioning.
If a campaign requires a ready-priced placement or a particular sponsorship deliverable, rates and a standard offer are not documented, so agree terms directly.
A campaign that judges awareness solely by last-touch leads also conflicts with their argument that upper-funnel work can contribute before a lead is recorded.
In the LinkedIn campaign analogy, they argue that awareness can be valuable even when it does not get credit on a last-touch scorecard.
Settle how the work will be evaluated before commissioning it.
A worked campaign example
Brief: A B2B SaaS company has steady paid-media leads but flat pipeline. Its average contract value is $25,000, its lead-to-opportunity rate is 8%, and it wants a target pipeline-to-spend ratio of 3.
Campaign angle: “Are your paid leads producing pipeline—or just activity?” Use an anonymized account review to show how to test that question before increasing spend.
First, calculate the business-based lead-cost ceiling: $25,000 × 8% gives $2,000 in expected pipeline per lead; divided by a target ratio of 3, that implies a maximum CPL of about $667. This is an illustrative calculation using the brief’s assumptions, not a channel benchmark.
Next, compare campaign results with CRM outcomes. Check whether lead sub-statuses reveal a quality problem, and whether the conversion actions Google is optimizing for actually predict pipeline. For LinkedIn or ABM, compare delivery to the accounts sales is actively pursuing and look for sales signals such as responses or account activity—not just an aggregate attribution figure.
The output could be a practical audit checklist or a recorded discussion built around the company’s anonymized findings. The angle fits Prates’s diagnostic-first approach and gives the brand a concrete decision: where to reallocate, what to investigate, or whether to pause. The company would need to supply suitable CRM and campaign data and agree the format and scope directly.
Choose Brian Koffler when a client needs to fix what happens after the form fill, not just diagnose why paid media numbers look wrong.
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In B2B SaaS, pipeline can stall when the agency runs one leg of the race and nobody inside owns the handoff.
Koffler argues for holding the agency accountable for spend, targeting, testing, and lead quality while giving it access to sales feedback and assigning someone internally to turn decisions into action.
His agency-fee argument can also help when procurement compares a retainer with hiring in-house: he factors in a senior demand-gen hire, RevOps support, platform expertise across Google and LinkedIn, three to six months of ramp, management time, loaded cost, and backfill risk.
Pick Koffler when the client needs help making the case for agency accountability and internal ownership.
Koffler’s two available posts focus on accountability, handoff, and agency-versus-in-house economics, rather than the detailed, arithmetic-first pipeline diagnosis in Gonçalo Prates’s posts.
They show how to calculate a maximum cost per lead from lead-to-opportunity rate, average contract value, and target pipe-to-spend, and how to inspect Google conversion actions or compare LinkedIn delivery against the sales list.
For CRM sub-status reviews, attribution thresholds, or a step-by-step account audit, ask Koffler for a working example.
Choose Prates for measuring and repairing pipeline economics; choose Koffler when the main issue is who owns the process after the lead arrives.
Choose Mickey Bowen when a paid media pipeline problem is really a creative-brief and demand-creation problem.
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A B2B SaaS campaign can fail before anyone opens Figma if the brief does not say which buyer motion the creative is meant to support.
Bowen writes about a five-field brief—motion, audience, story, offer, and success metrics—and a full-funnel system that reframes the usual TOFU, MOFU, BOFU split as Create and Capture, with Accelerate, Revive, Expand, and ABM as further motions.
This is useful when a brand has a working measurement model but its campaigns are not connecting with buyers who are months or years away from switching tools.
Pick Bowen for creative direction, campaign concepting, or a demand-creation program built around buyer awareness stages.
Bowen’s material focuses less on the finance and CRM questions that dominate a pipeline post-mortem.
If a CFO wants to know why non-brand Google spend is not producing pipeline, or whether LinkedIn influenced-pipeline claims will survive scrutiny, Bowen’s creative-brief and demand-creation framing does not answer those questions directly.
Gonçalo Prates’s posts tie spend to pipeline, set a cost-per-lead ceiling from the business model, and help decide when to pause a program.
A brand that needs both can use Bowen for the creative system and Prates for measurement and account diagnosis; ask either for examples beyond their respective focus.
Choose Victoria Chapman when the pipeline problem is suspect data rather than bad campaign strategy.
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A B2B SaaS buyer task can break because conversion tracking stopped logging properly months ago, a website update broke a tag, a CRM integration lost source data, or reports show impressions, clicks, and conversion rates without connecting them to revenue.
Chapman writes about silent tracking failures and the difference between vanity metrics and revenue metrics such as cost per qualified lead, lead-to-opportunity conversion rate, and customer acquisition cost against lifetime value.
She also explains why agencies often report impressions and clicks instead of integrating CRM data, and recommends regular tracking audits.
Pick Chapman when dashboards look fine but the underlying data may be partial or wrong, or when the current agency’s reporting never reaches revenue.
Chapman’s available posts are narrower on full-funnel paid media strategy than Gonçalo Prates’s.
She covers tracking audits and revenue-focused reporting, while Prates also addresses ABM sales alignment, LinkedIn company-level impression checks, ChatGPT ads campaign structure, and sequencing a 90-day pipeline plan.
For a broad diagnosis across Google, LinkedIn, CRM, and sales alignment, Prates covers more of the buyer’s task; Chapman is stronger when the first fix is confirming that the numbers can be trusted.
They are not interchangeable: Prates asks whether the conversion Google is optimizing for actually predicts pipeline, while Chapman asks whether the tracking that records it is still working.
For the broader strategy topics, ask Chapman for examples of that work.
Gonçalo Prates has 13 complete LinkedIn posts dated August 2025 to October 2026, based on their post identifiers.
They cover paid media for B2B SaaS: pipeline and finance framing, funnel diagnostics, cost-per-lead arithmetic built from CRM data, attribution, account-based marketing, Google Ads, LinkedIn Ads, ChatGPT ads and performance-team hiring.
The LinkedIn account lists a follower count of 3,776 dated 10 October 2026.
The posts mention a co-hosted YouTube series (Performance Therapy with Silvio Perez), a conference talk on ChatGPT ads, the agency AdConversion and a job description for a role at AdConversion; these items appear only as mentions.
No sponsorship terms, pricing, deliverable list, audience demographics or post-performance data are published in the posts here, and those gaps remain open.
Claims about what Prates does particularly well, the ideas they return to and their campaign fit come from the full text of the posts and the overview, ideas, relationships, discovery and fit sections of this profile.