Analytics & Business Intelligence
Define the metrics that matter, build the required infrastructure, and set up self-serve reporting
What I deliver
- Metrics framework — identify the KPIs that drive your business and define the required data
- Dashboard design & build — clean, automated dashboards
- Data pipeline setup — reliable data processing to feed your reporting layer
- Tool selection — help you pick the right data technology stack for your stage
- Team enablement — documentation, training, and handoff so you and your team owns the system
Project examples
B2B SaaS — unifying product and sales data
A typical starting point: product usage events in Mixpanel, customer and deal data in Salesforce, and subscription data in Stripe — with no way to connect them. The sales team has no visibility into how prospects are using the trial product, and the product team can’t see which features correlate with expansion revenue.
The project would involve building a unified data model in a warehouse that joins product events to accounts, linked to deal stage and ARR. The outcome is a single view of the customer: which features drive conversion from trial to paid, which usage patterns predict churn, and where high-value accounts are under-utilizing the product.
B2B SaaS — first product instrumentation and cohort analysis
A common early-stage situation: signup numbers come from the auth system, but there’s no insight into what happens after — activation rates, feature adoption, or where users drop off in onboarding.
This type of project starts with designing an event tracking schema and wiring it up across the product, then building cohort-based dashboards that break down signup-to-activation rates by acquisition channel, plan type, or product version. The goal is to give the product and growth teams a reliable foundation for understanding what’s actually working.
Who this is for
- B2B SaaS teams whose product, sales, and customer data live in separate tools with no shared view
- Early-stage companies that need their first real product instrumentation and funnel visibility
- Growth and product teams making decisions from incomplete or manually assembled data
- Founders who need credible metrics for investor conversations
Why Selvi Data
Working with a solo practitioner means no account managers, no handoffs between teams, and no overhead baked into the price. You work directly with the person doing the work, which keeps costs lower and timelines shorter.
I’m not aligned with any particular platform or vendor, so recommendations are based on what’s right for your situation, not what generates the largest implementation. And as an independent, I have no incentive to scope a project larger than it needs to be.