Your data is clean.You still can't answer your LTV by channel without a half-day of work.
Your data lives in five disconnected places, so nobody can answer a channel question without a half-day of manual work. We build the holistic marketing dashboards, unifying every source into one number, updated in real-time. The next budget conversation is a number, not a gut feel.
The data exists to answer the question precisely. It just lives in five places and never gets joined.
Your MMP reports installs, product analytics shows retention, your payment processor shows revenue. Nobody has joined them into one view of CAC, LTV, and payback by channel. So budget goes to platform reporting and back-of-envelope math.
The cheapest source gets the most budget, because nobody can prove which one actually pays back.
No cohort LTV visibility
You know your CAC. You don't know whether LTV covers it over 6 or 12 months, or how that differs by channel, so you're guessing whether the user you just bought is profitable.
Payback calculated manually
Someone builds a spreadsheet the night before the board meeting, stitching three tools together. Everyone presents the number and quietly knows the assumptions are shaky.
Channel ROI built on platform reporting
Allocation decisions get made on the numbers each ad platform chose to show you. Not on LTV calculated from your own transaction records. You have no independent number to check it against.
Numbers don't reconcile yet? Build the reporting on clean data, not before it.
From scattered sources to one number per channel, updated every morning.
Six steps. Every metric defined once and used everywhere. Nothing handed off until your team can run the review without us.
Timeline
All of this in just 3 weeks!
“Muffaddal built out a very advanced and valuable analytics strategy for us and was able to successfully implement everything across Google Analytics, Google Tag Manager and Google BigQuery. Highly recommend working with Muffaddal!”
Keith Wright
Sweeplift
The infrastructure that turns your data into decisions.
A BigQuery data warehouse
Every source, MMP, product analytics, payment processor, unified in BigQuery and updated on a defined schedule. The single place your numbers come from, so reconciliation stops being a weekly chore.
A dbt model layer
Core metrics defined once: blended CAC, cohort revenue, LTV, payback period. Consistent across every report, dashboard, and conversation. No more two teams defending two different CACs.
A cohort LTV dashboard
Retention and revenue curves by acquisition cohort, channel, and campaign. Day-7, day-30, day-90. Visible at a glance, updated automatically. The number that tells you what a user is worth before you scale acquisition.
A channel performance dashboard and review framework
Blended CAC, payback period, and LTV-to-CAC ratio by channel, updated daily. Paired with a structured weekly and monthly review your team runs itself. The answer to "where does the next dollar go?" in thirty seconds, not three days.
Full documentation
Pipeline architecture, dbt model definitions, and dashboard documentation. Your team maintains and extends it as new metrics and sources are needed. Without us.
Before you reach out.
I build on BigQuery and Looker Studio. Both free to use at most scales, which keeps your ongoing cost near zero. If you're already on Snowflake, Redshift, or another BI tool, I can adapt. The dbt model layer is portable, so the metric definitions move with you if you ever change tools.
Your MMP does a lot of this well, but only for the app. It can't reconcile app data with web, product analytics, and revenue into one view, and it reports on its own fixed set of metrics. This builds a warehouse where every source sits together, then models custom metrics and calculations around your business and your north-star metrics, not whatever your MMP decided to measure.
You do. I build everything inside your Google Cloud project, on your billing, with your team's access. Nothing runs on infrastructure I control, so there's no lock-in and no dependency on me to keep the pipelines running. When the engagement ends, you own the warehouse, the models, and the dashboards outright.
Yours, defined explicitly and once. A large part of why the LTV question never gets a clean answer is that finance, growth, and the MMP each compute it differently. I put the metric definitions in a dbt model layer: one place where LTV is calculated from your actual transaction records, retention curves are joined to acquisition cohorts, and payback is modeled at the channel level. Because the definitions live in that layer and not in a dashboard, every report draws from the same math, and the numbers move with you if you ever change BI tools.
Scope depends on how many sources need to be connected and how much modelling is required. Email us your requirements and I will share the detail proposal with scope and timelines
Turn your data into a number you can defend in any meeting.
If LTV and payback by channel takes more than a glance to answer, that's budget being split on gut feel. Get in touch and let's fix that.