Most SaaS marketing teams are drowning in dashboards but starving for answers. Winston Francois builds analytics programs that tell you where your pipeline actually comes from and what to do about it.
The Problem
You Have Dashboards, Not Answers
Your team has access to Google Analytics, your CRM, your marketing automation platform, and a half-dozen other tools. Each one tells a different story. Nobody agrees on which channels are working, what the real CAC is, or whether last quarter's campaign actually drove pipeline. More data without better analysis just creates more arguments.
Attribution Is Broken or Missing
Multi-touch attribution in SaaS is hard. Buying committees have multiple members. Sales cycles span months. Touchpoints happen across channels your analytics cannot see. Most SaaS companies default to last-touch attribution because it is easy, not because it is right. The result is systematically wrong investment decisions.
Marketing Cannot Prove Its Impact on Revenue
When the board asks what marketing contributed to pipeline this quarter, your team scrambles to assemble a credible answer from inconsistent data. Without a clear, agreed-upon measurement framework, marketing is always defending its budget rather than directing it. That defensiveness warps priorities.
Data Quality Undermines Every Analysis
Dirty CRM data, inconsistent UTM tagging, broken integrations between platforms, and manual processes that introduce errors – these problems compound. Every analysis built on bad data produces bad conclusions. And most SaaS companies know their data is messy but treat cleanup as a someday project.
How We Help
Winston Francois builds marketing analytics programs that start with the decisions your team needs to make and work backward to the data and frameworks required to make them well.
Our [growth strategy](/services/strategy/) team begins with a measurement audit. We map every data source, every integration, every dashboard, and every report your team currently uses. We identify gaps, inconsistencies, and the specific questions your current setup cannot answer.
From there, we design the measurement framework. This includes an attribution model appropriate for your sales cycle and buyer journey, KPI definitions that your marketing and sales teams both agree on, and a reporting cadence that matches your decision-making rhythm.
We then fix the foundation. Data quality issues, broken integrations, inconsistent tagging – these get resolved before we build anything on top of them. This is unglamorous work, but everything else depends on it.
Our [measurement](/services/measurement/) team builds the actual reporting layer. Dashboards are designed around decisions, not metrics. Each view answers a specific question: Where is pipeline coming from? Which channels have the best unit economics? Where should we increase or decrease spend?
We also build the [marketing](/services/marketing/) feedback loop – connecting analytics insights back to campaign optimization. The analytics program is not a separate workstream. It is the intelligence layer that makes every other marketing dollar more efficient.
The goal is a marketing team that walks into every budget conversation with clear, credible data on what is working and a specific recommendation on where to invest next.
What we deliver
- Marketing measurement audit and gap analysis
- Attribution model designed for your buyer journey and sales cycle
- Data quality remediation and integration fixes
- Decision-oriented dashboards and reporting framework
- Ongoing analytics support and optimization recommendations
A dashboard that does not change a decision is decoration. Every metric you track should connect to a specific action you are willing to take.
Our Methodology
Winston Francois runs analytics engagements on 90-day sprints. The first 30 days are audit and design – mapping current data infrastructure, identifying quality issues, and designing the measurement framework. This phase answers the question: what do we need to measure, and can our current stack do it?
Days 31 through 60 are build and fix. We remediate data quality issues, configure integrations, implement the attribution model, and build the initial reporting layer. This is where the messy, necessary infrastructure work happens.
Days 61 through 90 are validation and activation. We run the new measurement framework alongside existing reporting to validate accuracy, train your team on the new tools, and begin the first optimization cycle based on what the data reveals. You leave the sprint with clean data, working attribution, and actionable dashboards.
How We Work
In the first 30 days, we audit your entire marketing data ecosystem. Every platform, every integration, every dashboard. We document data quality issues, attribution gaps, and the specific questions your team needs answered but currently cannot. We deliver the measurement framework document that defines KPIs, attribution logic, and reporting structure.
During days 31 through 60, we execute the build. UTM frameworks get standardized. CRM data gets cleaned. Integrations get fixed or built. The attribution model gets implemented. Dashboards get built. This is hands-on-keyboard work in your actual platforms.
Days 61 through 90 focus on validation, training, and the first optimization cycle. We run parallel reporting to confirm the new framework produces accurate results. We train your team on interpreting and maintaining the dashboards. And we deliver the first set of optimization recommendations based on what the clean data reveals.
Ongoing engagements provide monthly analytics reviews, quarterly attribution audits, and continuous optimization support.
- Clean, reliable marketing data across all platforms
- Attribution model your marketing and sales teams both trust
- Decision-oriented dashboards that drive budget allocation
- First optimization cycle with specific investment recommendations
If your saas / tech company needs marketing 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
What attribution model works best for SaaS companies?
There is no single best model. The right choice depends on your sales cycle length, deal size, and buying committee complexity. For SaaS companies with longer sales cycles and multiple stakeholders, we typically recommend a multi-touch model weighted toward first-touch and opportunity-creation touches. We design the model around your specific business, not a textbook answer.
How long does it take to fix data quality issues?
It depends on how deep the problems go. Simple issues like inconsistent UTM tagging can be fixed in weeks. Structural problems like a CRM with years of dirty data take longer – usually 30 to 60 days for meaningful improvement. We prioritize the fixes that have the biggest impact on analytical accuracy rather than trying to make everything perfect at once.
What analytics tools do you work with?
We work with your existing stack. That typically includes Google Analytics, your CRM (Salesforce, HubSpot), your marketing automation platform, and your BI tool. We also have experience with Mixpanel, Amplitude, Heap, Looker, Tableau, and dbt. We recommend tool changes only when your current stack genuinely cannot support the measurement framework.
How do you handle the attribution gap between marketing and sales?
This is as much a people problem as a data problem. We involve both marketing and sales leadership in defining the measurement framework and attribution model. When both teams agree on definitions and methodology before the dashboards are built, the resulting data is trusted by everyone. We also build views specifically designed for sales and marketing to see the same pipeline through their respective lenses.
Can you help us understand our true customer acquisition cost?
Yes, and this is often the first question we answer. We calculate fully-loaded CAC that includes all marketing spend, sales costs, and overhead allocated to acquisition. We also break it down by channel, segment, and cohort so you can see not just your average CAC but where your most efficient acquisition happens. This usually reveals significant variation that the blended number hides.
Do you provide ongoing analytics support or just the initial setup?
Both. The initial sprint builds the foundation – clean data, working attribution, and decision-ready dashboards. Ongoing support includes monthly analytics reviews, quarterly attribution audits, and continuous optimization recommendations. Most clients find that having a dedicated analytics partner keeps the system maintained and ensures insights actually translate into action.
