Data science & analytics
We take noisy exports and daily numbers and turn them into a few KPIs your team can defend, charts that match how you run the business, and short write-ups so sales, marketing, and ops argue about actions, not definitions.
What this looks like in practice
Alignment first: we agree what “good” means — revenue, margin, cost per lead, time-to-close, or operational metrics — before anyone opens a notebook.
Repeatable pipelines: ingest from spreadsheets, CRMs, ad platforms, or databases; clean and document assumptions; output views your leadership can refresh on a rhythm (weekly, monthly).
Story, not noise: trends, anomalies, and “so what” in plain language — so the monthly review is short and decision-ready.
How we work with your data
Discovery & governanceWho can see what, where PII lives, and which systems are source of truth — so analytics don’t become a compliance surprise later.
Modeling & visualizationAnalysis and charts in tools your team can maintain — Python where it helps, dashboards where humans live, exports when finance asks.
Instrumentation tie-inWe connect this to how your site and apps are built: events, attribution, and structured facts so numbers match reality.
Research & deeper dives
For heavier experimentation and write-ups, see our research section — we keep product-facing analytics and long-form research in separate lanes so each stays readable.
A typical engagement
- Inventory sources. Exports, APIs, and who owns each field.
- Define KPIs & cadence. Few metrics, clearly owned.
- Build & validate. Reconcile to bank or CRM ground truth where possible.
- Review & iterate. Refresh narratives as the business changes — not a one-off PDF.
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Want one source of truth for leadership?
Tell us what you measure today and where the arguments start — we'll map a path from messy data to a story everyone can use.