PLG telemetry in weeks, not quarters
Most product led companies are promised the same thing.
Sign a 90–120 day telemetry project, get a beautiful funnel, and revenue will speed up once it is all live.
That timeline looks fine on a slide.
Inside a real company, it is a killer.
Three or four months of “implementation” means:
No reliable view of where users stall
No way to prioritize engineering based on actual behavior
No credible story for sales when they ask who is ready for a call
By the time anyone realizes the project is sliding, they have already paid a big invoice and are politically locked in.
My work at GTM Harmony is about cutting through that.
Take the same outcome those projects promise and compress it to a few sprints.
This is a recent example.
From scattered data to a live PLG funnel in about 40 days
The client is a healthy PLG company. Good product. Real usage.
But their tools could not tell a simple story.
Product events lived in BigQuery.
The CRM was in decent shape but disconnected from behavior in the app.
There was no common event model. No CDP strategy anyone trusted enough to act on.
Here is how we moved from that starting point to a working telemetry spine in roughly six weeks of active work.
Start with the outcome, not the tools
Before touching schemas or configs, we defined the questions that needed proof, not opinions. For example.
What does “activated” really mean for this product
Which behaviors separate casual users from customers who are likely to pay
What should sales see before they spend time on an account
That led to a simple but concrete PLG funnel from first touch, through product milestones, into paid plans and expansion.
Once that picture was clear, tools became implementation details.
Learn the data fast
I did not pull the team into weeks of workshops.
Instead, I sat with the existing BigQuery datasets, read the structures, and asked targeted questions when something did not line up.
Total stakeholder time to unblock the modeling work was under two hours.
From there I wrote the models that tie:
Product events to users and accounts
CRM records to the same identities
Revenue data to the accounts and time frames that matter
Now everyone is looking at one version of reality, not five dashboards that tell competing stories.
Wire CRM, CDP, and marketing around that spine
With the models in place, the next step was to expose the right pieces where teams do their daily work.
New properties were created in the CRM for key milestones and funnel stages
Workflows populate those properties from the modeled data
The CDP receives the stitched events so marketing and product can act on behavior, not just form fills
Importantly, we did not hire a small army or wait on a massive integration plan.
We used a mix of automated feeds and lightweight manual loads to prove the value of each signal before asking engineering to build anything permanent.
Get to production testing at week six, not week twelve
Traditional telemetry projects push all the risk to the end.
Developers spend weeks instrumenting events, setting up webhooks, and wiring destinations.
Only then does anybody open the dashboards and realize half of it needs revision.
Here we flipped that order.
By week six the team was already:
Seeing live funnel movement by user and account
Targeting outreach by actual behavior, not guesswork
Reviewing which signals actually predicted paid conversions
Engineering had not written a single webhook that would need to be ripped out later.
When backend work begins, user stories will be based on proven signals.
Each story gets done once, with empirical evidence that it connects to revenue.
What changed for the client
Once the spine was live, a few important things happened quickly.
Outreach stopped being generic
Marketing and sales can now message based on where someone truly sits in the journey.
Registered but never hit the key value moment.
Activated but never explored a critical feature.
Heavy usage in one part of the product but no activity in another.
Different behaviors. Different plays.
Same underlying data.
Roadmap conversations got sharper
Instead of arguing about opinions, the team can see where users fall out of the funnel and how long healthy accounts take to move between stages.
That lets leadership decide which friction to remove first and which ideas can wait.
Dev time became a scarce resource, not a default solution
Because telemetry was working before any major engineering investment, there was no need to “try” a bunch of might-help events in the codebase.
The stack is informed by real usage.
Backend work is smaller, cleaner, and more obviously tied to outcomes.
What actually made this possible:
On paper this kind of project often gets framed as a group effort.
You see titles like RevOps lead, data engineer, analytics consultant, CDP architect, marketing ops, and so on.
In this case the execution lived with one operator.
RevOps experience to understand how pipeline, churn, and expansion work in a PLG motion
Analytics engineering skills to model data in BigQuery
CDP and integration knowledge to route the right signals into the right tools
CRM and marketing automation experience to make that data useful in day to day work
That blend is what lets a 90–120 day telemetry project collapse into a six to seven week spine the business can act on.
It is bias for action backed by technical depth.
Not waiting on perfect. Shipping something real, validating it, and then hardening it.
Why this matters for PLG founders
If you run a PLG company, you do not have unlimited quarters to figure this out.
Every month you operate without trustworthy product telemetry is a month where:
Paid acquisition runs on partial information
Sales chases accounts that are not actually ready
Product bets do not compound because nobody can see the full journey
You do not need another pitch deck about telemetry.
You need a working system that ties product behavior to revenue and is live in weeks, not quarters.
