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
Marketing reports filled with impressions and click-through rates look busy but tell leadership nothing about business impact. The gap between top-of-funnel activity and closed revenue remains a black hole in most SaaS organizations. When finance asks for cost-per-acquisition by channel, marketing teams spend days assembling manual spreadsheets that are outdated before they are finished.
Tool sprawl creates data silos that nobody owns
The average SaaS marketing team runs 15-20 tools. Each generates its own reports with its own definitions. Your ad platform says one thing, your CRM says another, and your product analytics tool tells a third story. Without a unified data layer, teams waste hours reconciling numbers instead of acting on them. The result is low confidence in any single report.
Attribution models either oversimplify or overcomplicate
First-touch attribution gives all credit to the ad that started the journey. Last-touch gives all credit to the demo request page. Neither reflects reality for B2B SaaS, where buyers touch 10-15 channels over weeks or months. But building custom multi-touch models requires data engineering resources that most marketing teams do not have and cannot justify hiring for.
Dashboards get built but decisions still happen on gut instinct
Companies invest in BI tools, hire analysts, and build dashboards that nobody checks after the first week. The dashboards answer the wrong questions, update too slowly, or present data without context. When the data does not match what people feel is true, the data loses. Teams revert to instinct because their reporting never earned trust.
How We Help
We build measurement systems that answer business questions, not dashboards that display metrics. Every engagement starts by identifying the three to five questions your leadership team actually needs answered. Not what data you can collect – what decisions you need to make.
Our [growth strategy](/services/strategy/) work defines the metrics that matter for your stage and business model. A seed-stage product-led growth company needs different KPIs than a Series C enterprise SaaS business. We map your metrics hierarchy from board-level outcomes down to channel-level inputs, so every number in your reporting ladders up to something leadership cares about.
Data infrastructure comes next. We audit your existing stack, identify gaps and overlaps, and design a measurement architecture that connects your ad platforms, CRM, product analytics, and finance systems into a single source of truth. This is not about buying more tools. It is about connecting the ones you have and defining consistent metric definitions across all of them.
Attribution modeling is built around your actual buyer journey, not a theoretical framework. We analyze how your customers really move from awareness to closed deal, then design an attribution approach that reflects that reality. For most B2B SaaS companies, this means a weighted multi-touch model with clear rules for how credit distributes across touchpoints.
[Marketing](/services/marketing/) performance reporting translates raw data into narratives that drive action. Each report answers a specific question, highlights what changed and why, and recommends what to do next. We build automated reports for weekly operations and custom analyses for quarterly planning – both designed to be understood by non-analysts.
[Measurement](/services/measurement/) is not a one-time project. We establish ongoing reporting cadences, train your team to use the dashboards independently, and refine models as your business evolves. The goal is a self-sustaining analytics capability, not a permanent dependency on outside consultants.
What we deliver
- Metrics hierarchy – board-level KPIs mapped to channel-level inputs with clear definitions
- Attribution model – multi-touch framework customized to your buyer journey and data infrastructure
- Automated dashboards – weekly and monthly reports connected to live data sources
- Quarterly analytics playbook – analysis templates and decision frameworks for ongoing use
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
Our analytics engagements run 90-day sprints. The first two weeks focus on stakeholder interviews and data audits. We talk to your CEO, VP Marketing, VP Sales, and finance lead to understand what questions they need answered and where current reporting falls short. Simultaneously, we audit every data source in your stack – what it tracks, how it defines key metrics, and where it connects to other systems.
Weeks three through six build the measurement infrastructure. We define metric standards, configure tracking, build attribution models, and create the dashboard framework. Everything gets validated against historical data to ensure 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 refine what works and fix what does not. By the end of 90 days, your team owns a measurement system they understand and trust.
How We Work
Analytics engagements typically run 3 months for infrastructure build-out, with optional ongoing support for analysis and optimization. The first phase requires access to all marketing and sales tools, your data warehouse or BI platform, and 4-6 hours of stakeholder time for interviews and requirements gathering.
Our team includes a marketing analytics lead who designs the measurement framework, a data engineer who builds the technical infrastructure, and an analyst who creates the reporting layer. Your team provides tool access, historical context on what has been tried before, and feedback on report usability.
We deliver in two-week sprints with demos at the end of each cycle. This means you see working dashboards within the first month, not after a three-month build in isolation. Each sprint builds on the last, and your team can start using early deliverables while later phases are still in progress.
- 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.
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Frequently asked questions
How much does marketing analytics setup cost for a SaaS company?
Analytics engagements typically range from $25K-$75K depending on the complexity of your tool stack, number of data sources, and depth of attribution modeling required. Companies with clean CRM data and a modern stack tend toward the lower end. Organizations with legacy systems, multiple business units, or custom data warehouse requirements invest more. Ongoing analytics support runs separately from the initial build.
What tools do you recommend for SaaS marketing analytics?
We are tool-agnostic and work with whatever stack you already have. The specific platform matters less than how it is configured and connected. That said, we frequently work with Looker, Tableau, HubSpot, Salesforce, Segment, and Amplitude across our SaaS clients. We only recommend new tools when there is a genuine gap that cannot be solved with your current stack.
How long does it take to build a reliable attribution model?
A functional multi-touch attribution model takes 6-8 weeks to build and validate. The first three weeks are data collection and pipeline setup. The next two weeks build and test the model logic. The final weeks calibrate against known outcomes to ensure the model produces trustworthy results. Models improve over time as more data flows through them, so the 90-day mark is where most teams feel genuinely confident in the outputs.
Can you work with our existing BI team or do you replace them?
We work alongside your existing team and make them more effective. Our role is to set up the framework, define the methodology, and build the initial infrastructure. Your BI team then owns and maintains it going forward. We transfer knowledge throughout the engagement so there is no cliff when the project ends. If you do not have a BI team, we can provide ongoing support 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. Our process includes a data quality assessment in the first two weeks where we identify the biggest gaps and build workarounds or remediation plans. We prioritize getting directionally accurate insights from imperfect data rather than waiting for perfect data that never arrives. Some data cleanup happens in parallel with dashboard development.
How do you handle attribution for product-led growth models?
Product-led growth changes the attribution picture significantly because the product itself is a major acquisition and conversion channel. We build attribution models that incorporate product usage signals – trial activations, feature adoption, expansion triggers – alongside traditional marketing touchpoints. This gives you a complete view of how users move from awareness through self-serve signup to paid conversion and expansion.
