Most SaaS marketing teams drown in data but starve for answers. You track impressions, clicks, MQLs, and pipeline – yet when leadership asks which channels actually produce revenue, the room goes quiet. The problem is not missing data. It is missing structure.
The Problem
Vanity metrics dominate reporting while revenue attribution stays broken
Impressions and click-through rates look busy but tell leadership nothing about revenue. The gap between top-of-funnel activity and closed deals is still a black hole at most SaaS companies. When finance asks for cost-per-acquisition by channel, marketing spends days rebuilding a spreadsheet that is stale before it ships.
Tool sprawl creates data silos that nobody owns
The average SaaS marketing team runs 15-20 tools, each with its own reports and its own definition of a lead. Your ad platform, CRM, and product analytics tool tell three different stories about the same funnel. Without a shared data layer, teams burn hours reconciling numbers instead of acting on them.
Attribution models either oversimplify or overcomplicate
First-touch credits the ad that started the journey. Last-touch credits the demo request page. Neither reflects B2B SaaS, where buyers cross 10-15 touchpoints over weeks. It gets harder in 2026 as ad platforms report more modeled, probabilistic conversions instead of clean click data, and most marketing teams lack the engineering time to build a real multi-touch model.
Dashboards get built but decisions still happen on gut instinct
Companies buy BI tools, hire analysts, and ship dashboards nobody opens after week one. The reports answer the wrong question, update too slowly, or show numbers without context. When the data conflicts with what a VP already believes, the data loses and the team reverts to instinct because the reporting never earned trust.
How We Help
We build measurement systems that answer business questions, not dashboards that just display metrics. Every engagement starts by naming the three to five questions your leadership team actually needs answered – not what data you can collect, but what decisions you need to make.
Our [growth strategy](/services/strategy/) work sets the metrics that matter for your stage. A seed-stage PLG company needs different KPIs than a Series C enterprise seller. We map a metrics hierarchy from board-level outcomes down to channel-level inputs, so every number in your reporting ladders up to something leadership actually cares about.
Next we audit your stack and connect it. Ad platforms, CRM, product analytics, and finance systems get tied into one source of truth with consistent metric definitions – not more tools, just tools that agree with each other. Attribution gets modeled against your real buyer journey rather than a theoretical framework: for most B2B SaaS clients that means a weighted multi-touch model with clear rules for how credit distributes across touchpoints.
[Marketing](/services/marketing/) performance reporting turns raw data into narratives that drive action. Each report answers one specific question, flags what changed and why, and recommends the next move. We build automated weekly reports and deeper quarterly analyses, both written for operators, not analysts.
[Measurement](/services/measurement/) does not end at handoff. We set an ongoing reporting cadence, train your team to run the dashboards without us, and refine the models as the business changes – the goal is a self-sustaining analytics function, not a permanent consulting bill.
What we deliver
- Metrics hierarchy – board-level KPIs mapped to channel-level inputs with clear definitions
- Attribution model – multi-touch framework built around your buyer journey and data stack
- Automated dashboards – weekly and monthly reports wired to live data sources
- Quarterly analytics playbook – templates and decision frameworks your team keeps using
The point of analytics is not to measure everything. It is to answer the five questions that determine where you invest next quarter. Everything else is noise.
Our Methodology
Analytics engagements run 90-day sprints. The first two weeks are stakeholder interviews and a data audit – we talk to your CEO, VP Marketing, VP Sales, and finance to learn what questions they need answered and where current reporting falls short, while mapping every data source in your stack and how it defines its key metrics.
Weeks three through six build the infrastructure: metric standards, tracking configuration, attribution models, and the dashboard framework, all validated against historical data so the numbers pass a sanity check before anyone sees them.
Weeks seven through twelve are implementation and calibration. Dashboards go live, reports run on schedule, and we sit with your team through two full reporting cycles to fix what does not work. By day 90, your team owns a system it understands and trusts.
How We Work
Engagements typically run 3 months for the infrastructure build, with optional ongoing support after that. Phase one needs access to your marketing and sales tools, your data warehouse or BI platform, and 4-6 hours of stakeholder time for interviews.
Our team is a marketing analytics lead who designs the framework, a data engineer who builds the infrastructure, and an analyst who builds the reporting layer. You provide tool access, context on what has already been tried, and feedback on what is actually usable.
We deliver in two-week sprints with a demo at the end of each one, so you see working dashboards inside the first month instead of waiting on a three-month build in isolation. Later phases build on what you are already using.
- Attribution clarity – understand which channels and campaigns drive revenue, not just leads
- Reporting trust – leadership uses dashboards for decisions instead of requesting ad hoc spreadsheets
- Budget efficiency – reallocate spend based on actual performance data rather than assumptions
If your saas / tech company needs data, reporting & analytics leadership, we should talk.
Expand your marketing team output with our experts
Let us take a custom approach to your growth goals by assembling and leading the best-in-class marketing team to support your next stage.
Frequently asked questions
How much does marketing analytics setup cost for a SaaS company?
Most engagements run $25K-$75K, depending on the size of your tool stack, the number of data sources involved, and how deep the attribution modeling needs to go. Clean CRM data and a modern stack land toward the low end. Legacy systems, multiple business units, or custom data warehouse work push it higher. Ongoing analytics support is scoped and priced separately from the initial build.
What tools do you recommend for SaaS marketing analytics?
We are tool-agnostic and work with whatever you already run. The platform matters less than how it is configured and connected. We most often touch Looker, Tableau, HubSpot, Salesforce, Segment, and Amplitude across SaaS clients, and only add a new tool when there is a real gap your current stack cannot close.
How long does it take to build a reliable attribution model?
A working multi-touch model takes 6-8 weeks to build and validate: three weeks on data collection and pipeline setup, two weeks building and testing the model logic, and the rest calibrating against known outcomes. Models keep improving as more data flows through them, so most teams feel real confidence in the numbers around the 90-day mark.
Can you work with our existing BI team or do you replace them?
We work alongside your team, not instead of them. Our job is setting up the framework, defining the methodology, and building the first version of the infrastructure – your BI team owns and maintains it afterward. We transfer knowledge throughout so there is no cliff when the engagement ends. If you do not have a BI team yet, we can cover that gap until you hire one.
What if our CRM data is messy or incomplete?
It almost always is – messy CRM data is the norm, not the exception. The first two weeks include a data quality pass where we flag the biggest gaps and build workarounds or a remediation plan. We would rather ship directionally accurate insights from imperfect data than wait for perfect data that never shows up. Cleanup happens in parallel with dashboard work, not before it.
How do you handle attribution for product-led growth models?
PLG changes the picture because the product itself is a major acquisition and conversion channel. We fold product usage signals – trial activation, feature adoption, expansion triggers – into the attribution model alongside marketing touchpoints, so you get one view of how users move from first visit through self-serve signup into paid conversion and expansion.
