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Unified funnel analytics
End-to-end visibility from acquisition through activation and conversion.
Best Marketing & Growth Analytics Platforms
Growth analytics that ties spend, product behavior, and revenue into one trusted view.
Context
As marketing spend grows across channels and tools, teams lose clarity on what actually drives revenue. Data lives in silos, attribution is disputed, and reports arrive too late to guide decisions. Growth slows not because of lack of effort, but because insight is unreliable.
Who this is for
We usually work best with teams who know building software is more than just shipping code.
This is for teams who
Marketing and growth teams managing multiple channels
Product-led companies connecting usage to revenue
Revenue teams needing clearer ROI visibility
Organizations tired of manual reporting and metric conflicts
This may not fit for
Teams looking only for channel-level dashboards
Businesses without defined growth goals
One-off reporting or spreadsheet-based analysis
Organizations unwilling to unify data sources
Problem framing
The operating reality
Marketing data exists everywhere, insight exists nowhere
Most teams track activity but cannot confidently link it to outcomes. Channel reports disagree, funnels break between tools, and attribution models oversimplify reality. As a result, teams argue over numbers, rely on gut decisions, and struggle to prove ROI or scale what works.
How this is usually solved (and why it breaks)
Common approaches
Relying on individual ad platform reports
Using last-click attribution by default
Manually stitching data in spreadsheets
Separating marketing, product, and revenue metrics
Where these approaches fall short
Conflicting metrics across teams
Unclear connection between spend and outcomes
Slow feedback on experiments and campaigns
Low trust in analytics outputs
Delivery scope
Core capabilities we implement
Structured building blocks we use to de-risk delivery and keep enterprise programs predictable.
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Attribution and ROI measurement
Multi-touch and rules-based attribution tied to revenue and lifetime value.
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Experimentation tracking
A B tests and growth experiments measured consistently over time.
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Cohort and retention analysis
Understand retention, churn, and long-term value by channel and campaign.
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Integrations and activation
Connect ad platforms, CRM, and product data with APIs for real-time use.
How we approach delivery
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Align on growth questions and shared metrics first
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Unify marketing, product, and revenue data
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Implement realistic attribution and cohort models
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Deliver insights directly into decision workflows
Engineering standards at PySquad
We build growth analytics as an operating system for decisions. The focus is shared metrics, realistic attribution, and insights that teams trust enough to act on.
Expected outcomes
Measurable results teams plan for when we ship the full stack, integrations, and governance together.
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Clear visibility into what drives real growth
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Better budget allocation based on trusted ROI
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Faster learning from experiments and campaigns
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Reduced reporting friction across teams
Turn marketing data into decisions your team can trust.
Share scope, constraints, and timelines. We respond with a clear delivery approach, not a generic pitch deck.
Start the conversationAbout PySquad
Short answers if you are deciding who builds and supports this kind of work.
- What is PySquad?
- We are a software engineering team. PySquad works with people who run complex operations and need tools that fit how they work, not software that forces them to change everything overnight.
- What do you get from us on a project like this?
- Discovery, build, integrations, testing, release, and follow up when real users are in the product. You talk to engineers and leads who own the outcome, not a rotating cast of handoffs.
- Who do we work with most often?
- Teams in logistics, marketplaces, marina, aviation, fintech, healthcare, manufacturing, and other fields where downtime hurts and clarity matters. If that sounds like your world, we are easy to talk to.
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