Predictable Pipeline Playbook: B2B Fintech Case Study

In short: A predictable pipeline playbook measures every marketing channel against one CRM outcome, allocates budget by proven influence on that outcome, and runs the measurement on a schedule. A Northern European B2B fintech ran it with Cogny from March 2026: impression-level LinkedIn attribution lifted influenced pipeline 53% and value per closed-won deal 33% on the same budget, lead-quality reconciliation showed the highest-volume channel produced the weakest customers, organic impressions rose 58%, and AI search became a tracked source of qualified deals. This page describes the four pillars, the changes behind each, and how to run the same playbook.

Background

The company provides working capital to product businesses. Sales is a classic B2B motion: a company enters the CRM, gets qualified, goes through credit approval, and a deal closes with an approved financing limit. Marketing runs on a fixed monthly budget across LinkedIn Ads, Google Ads and Meta, plus a website that ranks well for its own brand.

When the company connected Cogny in late March 2026, the problem was not effort or spend. It was that every channel was being judged by its own platform's metric. LinkedIn was read on clicks and click-through rate. Google Ads was read on platform conversions. Meta was read on lead count. The CRM held the only outcome that mattered, credit-approved deals, and nobody could connect the ad spend to it without a week of exports.

Six months later, the same budget produces a pipeline the team can forecast. This is how that happened, told through the four pillars of the playbook and the change behind each.

Before vs. After

	Spring 2026	September 2026
How LinkedIn was judged	Clicks and CTR	Influenced pipeline per campaign, at company level
LinkedIn-influenced pipeline	Full-year 2025 baseline	+53%, with 29% of the year still to go
Value per LinkedIn-influenced closed-won deal	2025 baseline	+33%
Definition of a good lead	Different in every platform	One: a credit-approved deal in the CRM
Google non-brand search	Over 90% of search spend, almost none of the search-influenced pipeline	Cut back; brand and high-intent kept
Organic impressions	January baseline	+58% by August
AI search referrals	Invisible	Tracked as their own CRM source, already producing qualified deals
Reporting	Manual, five dashboards, always out of date	Scheduled audits that end in tickets; routine operations automated

Sources: company-level LinkedIn attribution data joined to the CRM (2026 year to date vs. full-year 2025), the CRM's lead-reconciliation dataset in BigQuery, Google Ads, and Google Search Console. This page reports rates and changes only. See "A note on the numbers" below.

Pillar 1: Impression-level LinkedIn attribution, not click-level

Most B2B advertisers judge LinkedIn the way they judge search: on clicks. But a LinkedIn ad does its work when the right company sees it, whether or not anyone clicks. Over a multi-touch window, exposure is what moves a deal. (For the wider picture of how B2B attribution models differ, see B2B marketing attribution models explained.)

Cogny connected the company's LinkedIn campaigns to company-level exposure data and to the CRM, so every campaign could be scored on the deals it influenced. Measured the same way in both years, 2026 year to date (through mid-September) compares with full-year 2025 like this:

LinkedIn-influenced, 2026 YTD vs. full-year 2025	Change
Influenced deals	−20%
Influenced open pipeline	+53%
Influenced closed-won value	+8%
Value per closed-won deal	+33%

Fewer deals, bigger deals, and more pipeline, with more than a quarter of the year still to run. That is what a campaign mix tuned on outcomes rather than clicks looks like: the ads reach the companies that fit, not the companies that click.

Two findings from the weekly LinkedIn audit drove the mix:

Retargeting and thought-leader video carried roughly four fifths of the pipeline-weighted influence from under a tenth of spend. Prospecting reaches new companies; retargeting and founder-voice content close them. Budget shifted within the fixed monthly total accordingly. (The same warm-up effect on booked calls is documented in LinkedIn ad warm-up increases call booking rates.)
A size-matched comparison of exposed vs. unexposed companies showed exposed deals were about twice as large. The same comparison showed a lower close rate for exposed companies, which is the selection-bias warning every attribution model should carry. Cogny reports these as influence, never as causation.

The result is a LinkedIn channel with a known spend-to-pipeline ratio the team can set a budget against. That ratio is what makes the pipeline predictable.

Pillar 2: Lead quality by channel, scaled on quality rather than volume

The second pillar answers the question the CRM was built to answer: which channel brings in customers, not just leads?

Cogny reconciled every acquisition channel in the CRM against two stages: qualified deal, and credit-approved deal with an approved limit. The picture that came back contradicted the lead-count dashboards.

Channel	Share of qualified deals	Approval rate	Avg. approved limit (paid social = 1×)
Paid social (Meta)	36%	18.5%	1.0×
Self-onboarded	26%	27.7%	1.25×
Direct	18%	27.3%	0.9×
Paid search (Google)	9%	12.5%	3.9×
Organic search	6%	9.1%	—
AI search	2%	early	—

Meta was the volume leader and the weakest converter: the largest share of qualified deals, the lowest approval rate among the big sources, and small approved limits. Paid search sent few but large customers (a handful of approved deals, so treat the multiple as directional). Self-onboarding and direct traffic, the channels that reflect brand strength, approved at the highest rate.

The Google Ads side told the same story at campaign level. Brand keywords cost about a seventh of the per-click price of generic and competitor keywords. Generic and competitor campaigns consumed over 90% of search spend and produced almost none of the search-influenced pipeline. Cogny's SEM signals flagged the waste week after week until the budget was moved.

Cogny also caught a tracking gap that would have hidden all of this: in one quarter the Meta pixel saw roughly one in nine of the Meta-sourced qualified deals the CRM recorded. The platforms disagreed with the CRM, and only the CRM counts. (Meta leads vs. HubSpot leads: the real cost per lead walks through the reconciliation method.)

The playbook rule that came out of it: allocate by value per qualified lead, never by lead count. Scale LinkedIn's proven deal-influencing campaigns and high-intent search, keep brand search, contain Meta at the volume the approval rate justifies, and fix tracking before trusting any platform's number.

Pillar 3: SEO and GEO, owning the search demand you already rank for

The website ranked well. Search Console showed an average position under 10 for most of 2026 and a long list of commercial queries in the top five. The problem was what happened after ranking: over 90% of organic clicks came from brand queries, and the commercial queries the company ranked for had no page built to convert them.

Cogny's content intelligence and search-intent analyses turned this into a plan: a money page for each commercial cluster the site already ranks for, in each market language, with the CMS connected so pages can ship from a ticket.

Search Console, 2026 (impressions indexed to January):

Month	Impressions	Avg. position
January	100	9.7
March	121	7.5
June	144	7.5
August	158	8.7

Impressions rose 58% from January to August. Clicks have not followed yet, which is the honest state of this pillar: visibility on non-brand terms is growing, and the money pages that convert it are the work in progress.

The part that was invisible before Cogny is AI search. The content analysis found ChatGPT as a live referrer of qualified leads, and AI assistants cite the company on the first page of answers for category questions. The CRM now records AI search as its own source, and it is already producing qualified deals. Generative engine optimization stopped being a slide and became a line in the pipeline report. (Background: what GEO is and why it matters in 2026, and how to read AI-search traffic in Search Console.)

Pillar 4: Automated marketing reporting, so the team acts instead of assembling

Before Cogny, answering "what is our marketing ROI this month?" meant exporting from GA4, HubSpot, Google Ads, Search Console and LinkedIn, joining them by hand, and presenting numbers that were already out of date.

Every source now lives in one workspace: the three ad platforms, the CRM, web analytics, Search Console, the tag manager, the SEO data provider and the CMS. The audits run on a schedule: the LinkedIn audit every Monday, cross-channel reconciliation weekly, SEM signals twice a week, Meta reconciliation against the CRM, content intelligence, and executive business signals for leadership. Each one ends in a ticket with the action, the evidence and the expected effect. The shipped tickets are the changes described above: the budget shifts within LinkedIn, the search cuts, the Meta tracking fix and the money-page plan.

Some of the routine work no longer needs a human at all. Cogny pauses the LinkedIn campaigns on Friday evening and resumes them on Monday morning. The marketing lead wrote a short set of house rules for how LinkedIn should be judged (prospecting on CTR, CPM and frequency, never on cost per lead; pause a campaign only when every ad fails; baselines by format and market), and every weekly audit follows them.

An analysis is only worth what it changes. The value of this pillar is not the reports, it is that the marketing lead and their growth consultant now spend their week reallocating budget, shipping pages and tightening targeting, and none of it building spreadsheets.

The Predictable Pipeline Playbook, step by step

Pulled together, the four pillars are one repeatable operating model:

Measure LinkedIn at the impression level, against CRM outcomes. Credit campaigns for the companies they reached that later became deals. Move budget within the fixed total toward proven influence.
Define lead quality once, in the CRM, and score every channel against it. Approval rate and value per approved deal decide the allocation. Lead count decides nothing.
Own the search demand you already rank for, on Google and in AI answers. Money pages for ranked commercial queries; AI search tracked as a source.
Put the reporting on a schedule. Analyses, tickets and routine operations run without a human, so the humans act.

The output is a spend-to-pipeline ratio per channel that holds month to month. That is what predictable pipeline means: not more budget, but a budget whose outcome you can forecast.

A note on the numbers

This page reports rates and changes, never absolute amounts, so that nothing can be traced back to the customer. Influenced pipeline and influenced closed-won value count a deal when a company was exposed to a campaign within a twelve-month, multi-touch window. That is more generous than last-click and less strict than a holdout test, which is why every figure here is reported as influence, not attribution, and why no ROAS is quoted. The 2026 figures cover January 1 to September 15, so the comparison with full-year 2025 understates the 2026 result. The channel table is the CRM's full reconciliation dataset; the paid-search multiple rests on a handful of approved deals. Client-identifying details, including campaign names, keywords and deal names, have been removed.

Key takeaways

Clicks are the wrong unit for LinkedIn. Company-level exposure joined to the CRM is what turns LinkedIn from a brand line item into a pipeline channel with a known cost per opportunity.
The channel with the most leads was the channel with the worst customers. Reconcile platform leads against the CRM stage that means money before scaling anything.
Platforms disagree with the CRM. Trust the CRM. A pixel that sees one lead in nine is a tracking bug, and it hides the real allocation decision.
Ranking is not the finish line. The demand you already rank for needs a page built to convert it, and AI answers are now part of that demand.
An analysis is worth what it changes. Scheduled audits that end in tickets with evidence are the cadence that makes the other three pillars runnable.

Frequently asked questions

What is a predictable pipeline playbook? A repeatable operating model where every marketing channel is measured against the same CRM outcome, budget is allocated by proven influence on that outcome, and the measurement runs on a schedule. Predictability comes from a known spend-to-pipeline ratio per channel, not from bigger budgets.

How does impression-level LinkedIn attribution work? Instead of counting clicks, company-level exposure data records which companies were shown which LinkedIn campaigns. Those companies are matched to CRM deals, so a campaign is credited when a company it reached later creates or closes a deal. It is a statistical comparison of exposed versus unexposed companies, not a controlled experiment, which is why Cogny reports it as influence rather than attribution.

Why doesn't this case study report a LinkedIn ROAS? Because influenced-value ROAS is inflated by construction. When campaigns reach a large share of the whole target market every month, almost every deal has been exposed to an ad, and dividing all of that deal value by ad spend gives a three-digit multiple that means little. Cogny reports the change in influenced pipeline year over year, the change in value per deal, and the size-matched comparison of exposed versus unexposed companies instead, because those hold up in front of a CFO.

How do you get leads from AI search (ChatGPT, Gemini, Perplexity)? Be the cited answer for category questions, then measure it. Cogny's generative engine optimization (GEO) analyses check which pages AI assistants cite, fix the content and structured data on the pages that should be cited, and record AI Search as its own source in the CRM so referrals from ChatGPT, Gemini and Perplexity show up as qualified deals rather than as unattributed direct traffic.

Does Cogny replace the marketing team or the agency? No. Cogny does the measurement, the reconciliation and the routine operations, then files the recommended action with its evidence. The marketing lead and their growth consultant decide, and their time moves from assembling reports to acting on them.

Which integrations did this case use? LinkedIn Ads, Google Ads, Meta Ads, HubSpot, Google Analytics 4, Google Search Console, Google Tag Manager, a company-level LinkedIn attribution source, an SEO data provider and the website CMS, all connected to one Cogny Cloud workspace with BigQuery underneath.

Can I run this playbook on the Solo or Cogny AI tier? The channel audits and the CRM reconciliation run on any tier with the integrations connected. The scheduled analyses, BigQuery warehouse and automated operations that make it hands-off are Cogny Cloud features.

The Predictable Pipeline Playbook: How a B2B Fintech Built Forecastable Pipeline

The Challenge

The Solution