For three years, B2B SaaS marketers were told the same story: the third-party cookie is dead, prepare accordingly.
Teams rebuilt dashboards. Vendors rebranded products around “cookieless futures.” Boards were briefed on first-party data strategies. Then, regulators hesitated, browsers delayed, and suddenly the thing we had been eulogizing decided to hang around a little longer.
Now we live in the worst possible world: a half-deprecated cookie ecosystem. You have to operate in both realities at once.
Some teams are treating this as a reprieve. It is not. It is a stress test.
If you are still leaning on third-party cookies to make key go-to-market decisions, you have just been given proof that your stack is fragile. The way out is not cleverer tagging. It is building what I call the First-Party Signal Stack: an identity and measurement foundation that does not care whether third-party cookies finally disappear next year or five years from now.
If you want to hit T2D3 growth, you cannot afford to have your attribution model tied to whatever Google decides to do with Chrome.
The real problem in B2B SaaS marketing is not cookies. It is the black box.
Ask most leadership teams a simple question: “Where did last quarter’s best deals actually come from, and what messages moved them?” Then watch the room pull up three dashboards and still disagree.
When that is your reality, the worst thing you can do is hand more money to performance channels and hope the pixels tell you the truth.
The fix is boring and hard: a real commercial operating system.
That means:
MarTech setup precedes paid spend
No exceptions. You do not pour fuel into a car that does not have a working dashboard. Before a dollar goes into Google Ads, LinkedIn, or programmatic, you have:
Clean Google Analytics (or equivalent), with internal traffic excluded and events defined.
A CRM that mirrors your real funnel: suspects, subscribers, leads, MQLs, SQLs, opportunities, customers, churned.
At least one weekly funnel review where everyone agrees on definitions.
The CRM is the master, not an afterthought
Your CRM, ideally HubSpot for most B2B SaaS stages, is the brain. Marketing automation, landing pages, and ad platforms orbit around it. Every contact, every opportunity, every key event lives there. If your “real” numbers live in an ad platform and your CRM is a glorified rolodex, you do not have an operating system. You have a collection of tools.
Kalungi’s own methodology reflects this. The first 90 days for a new client are not about “more leads.” They are about foundations: CRM implementation, analytics, tracking, permission structures, and Q1 OKRs that focus on setup. It is intensely unsexy work. It is also the only way Pay-for-Performance marketing makes sense later.
Once you have one brain, you can start building the Signal Stack: the layers of owned data and infrastructure that let you operate in a noisy measurement environment without lying to yourself.
You cannot optimize anonymous.
The first layer is brutally simple: design your digital touchpoints so that when someone raises their hand, you capture a clean identity and context.
That means:
Forms that are ruthlessly clear about value
Demo request, discovery call, free trial, benchmark report, industry research. No vanity gates. If you are going to ask for business email, role, and company, make the asset something a serious buyer would actually trade for.
Minimum viable enrichment at capture
Get only what you need at the form (name, email, company URL, role) and rely on enrichment later. Every extra field you add to “make analytics easier” is a conversion tax.
Trials with identity from day one
Stop pushing anonymous trials. If someone will not share who they are, they are not a serious prospect. You want a smaller number of identifiable users, not a large volume of ghosts.
Browser-based tracking is a moving target. Ad blockers, ITP changes, privacy settings: they all chip away at what you can see.
Server-side tracking moves the critical tags from the user’s browser to your own infrastructure:
Events like sign-ups, key feature usage, and upgrades are sent from your server to your analytics and ad platforms.
You control the logic, not a vendor’s JavaScript.
You get resilience against browser changes and less variance between what your product sees and what your dashboards show.
Server-side tagging is not about spying. It is about accurately understanding how your own product and site are used so you can make better decisions.
Raw form data is weak signal. You need enrichment to power ABM and agentic GTM.
This is where you attach:
Firmographics (industry, size, segment)
Technographics (stack, key tools, complementary platforms)
Behavioral tags (content consumed, features used, events attended)
Buying role if you can infer it (P1 user, P2 manager, P3 executive)
You can use tools, agents, and manual work here. The key is that this enrichment flows back into the CRM and becomes the backbone for:
Tiering accounts (Tier 1, 2, 3)
Routing leads
Designing pod-level account plans
Feeding agents with clean ICP data when you eventually automate outreach
This is where many teams fall back into the comfort of cookies. They want granular retargeting across half the internet. That era is ending, even if the deprecation timeline is a mess.
The future is:
Lookalike modeling based on your own clean audiences inside ad platforms
Privacy-preserving ways of understanding performance across partners (for example, data clean rooms)
Contextual and category-based targeting that leans on your brand and content, not secret tracking
You still build reach, but you do it in a way that does not depend on being able to follow individuals around forever.
The Signal Stack in one sentence:
Capture identity cleanly, track behavior reliably, enrich it intelligently, and scale reach in a way that does not depend on fragile tracking gimmicks.
In a world where cookies are half-alive and half-dead, every model will be wrong. The question is whether yours is useful.
Three principles help.
You cannot pay salaries with impressions. You do not get renewed funding based on click-through rates. For B2B SaaS, the lagging indicators that matter are:
MQLs and SQLs that match your ICP
Opportunities created and advanced
Pipeline value and velocity
New ARR and expansion ARR
Payback periods and CAC
Everything else is a supporting actor.
For the next few years, you will have situations where:
Google Ads says it drove 30 conversions
LinkedIn claims 25 of the same
Your CRM has 15 opportunities tagged “direct”
The answer is not to obsess over assigning perfect fractional credit. It is to:
Pick a primary source of truth for financial decisions (the CRM)
Maintain directional channel performance tracking (ad platforms and analytics)
Make budget decisions based on a mix of CRM close data and platform trends, not either in isolation
When the numbers diverge wildly, that is a signal: your foundation is off and the stack needs debugging.
At Kalungi, Pay-for-Performance depends on being able to say, “Marketing influenced X MQLs, Y SQLs, and Z pipeline” in a way both sides believe.
The only way to get there is to define OKRs at the level of business outcomes:
Objective: Increase new pipeline in North America
KR: Create $3M in qualified pipeline for ICP accounts with ACV > $50k
KR: Improve MQL-to-SQL conversion from 20% to 30%
Channel is the tactic, not the goal. Cookies, no cookies, whatever: the KR does not care.
Consider my recent post, on AI GTM an Agents.
If you deploy AI agents into a weak Signal Stack, you are asking for trouble. They will optimize for the wrong things, “learn” from bad data, and scale activities that you should not have been doing in the first place.
If you deploy them into a strong Signal Stack, they become extremely powerful.
Examples:
Enrichment agents can keep your first-party data clean, complete, and up to date.
Outbound agents can build and maintain ABM audiences based on real buying signals instead of third-party segments.
Nurture agents can trigger sequences based on actual product usage recorded server-side, not unreliable front-end events.
Internal “analyst” agents can surface cohort performance and anomalies faster than any spreadsheet jockey.
As buying-side AI agents emerge, your first-party signal becomes even more important.
Buyer agents will not care about your impression counts. They will crawl your site, your documentation, your pricing, your reviews, and your product. They will pull structured data and compare you to competitors without ever tripping your pixel.
You will need a new metric alongside MQLs and SQLs: qualified agent interactions. Not how many humans filled out a form, but how often your content and APIs are used by buyer-side agents when they evaluate your category.
You do not get that visibility through third-party cookies. You get it by instrumenting your own properties and products well, and by structuring your data so it is easy for both humans and agents to consume.
The cookie saga exposed something deeper: the old MQL-centric playbook was fragile even before privacy changes.
Half of B2B marketers cannot consistently hit pipeline targets. SDR productivity is falling. CAC is rising. A big part of the problem is that we treated leads as gumballs: throw budget in, get form fills out, call it marketing.
The world we are moving into forces better questions:
Which buying groups are actually in motion in our ICP?
Who in those groups is engaging with what, over what time horizon?
Are we consistently present with useful content before they even declare a project?
Do we understand customer advocacy, expansion, and net retention at least as well as we understand net-new?
You will see a shift from individual MQL counting to:
Qualified buying groups and accounts
Buying group engagement scores over time
Team-based pipeline metrics shared by marketing and sales
Customer health, advocacy, and expansion metrics
The First-Party Signal Stack is the prerequisite for this. Without good identity across contacts at an account, you cannot see a buying group. Without stable tracking of engagement across your owned properties, you cannot see how that group moves.
This is not a measurement tax. It is an unlock. When you align on buying groups and accounts, the brand work and the long-horizon outreach that we used to hand-wave suddenly show up as meaningful, measurable contributions.
There is a temptation, when a deprecation timeline slips, to exhale and go back to business as usual. That is a mistake.
We are at a rare moment where you can do three valuable things at once:
Use the remaining lifespan of third-party cookies as a benchmark
Run your cookie-based numbers side by side with your First-Party Signal Stack. Where they diverge, investigate. Use the overlap period as a calibration window, not a comfort blanket.
Clean house before agents take over more of your GTM
Do the boring work now: unify the CRM, lock definitions, implement server-side tracking, and get basic enrichment in place. Every agent project you run later will be better for it.
Reframe marketing as a syntropy engine
Stop letting your tech stack dictate your strategy. Go back to first principles:
Who is it for?
What is it for?
How will we know if it worked?
Then design your First-Party Signal Stack to answer those questions reliably, regardless of what Google or Apple decide to do next.
If you wait until cookies truly vanish to build this, you will be doing surgery in the middle of a race.
Leadership questions to ask this quarter
If Google and every major browser killed third-party cookies tomorrow, how much of our measurement would break?
Is there a single place in our company where I can see the full journey from first touch to revenue for a given customer?
Do we have a clear, documented definition of MQL, SQL, opportunity, and customer that both sales and marketing agree on?
How much of our targeting relies on anonymous audiences we rent versus identities we own?
If a board member asked us which three channels actually drive profitable growth, could we answer without hedging?
The strategic choice is not whether to build a First-Party Signal Stack. It is only whether you build it now, while you still have the luxury of parallel signals, or later, under duress.
Seasoning the chicken after it is cooked does not fix a bad recipe. It just makes the waste more expensive.
The same goes for your data. Build the foundation first. Then light the fire.