Data Analytics & Visualisation | Pelio Studio
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ANALYTICS + DATA

Data Analytics (for Business)

From ‘what happened’ to ‘what to do next’—analysis that surfaces growth levers, efficiency wins, and risks.

DESCRIPTION

What this includes

From “what happened” to “what to do next.” We interrogate your data to surface growth levers, efficiency wins, and risk signals—cohorts, funnels, unit economics, pricing sensitivity, retention drivers, forecasting, and scenario planning—translated into plain‑English recommendations executives can act on.

Overview
Key Highlights
6 items
What matters most—at a glance.
Insight report with narrative findings, charts, and decision implications
Analysis workbooks/notebooks & SQL for transparency and reuse
Scenario models (revenue, CAC/LTV, pricing/discount impacts, hiring ramps)
KPI tree & dashboard brief to operationalise measurement
Experiment backlog with expected impact and measurement plans
Data dictionary covering fields, transformations, caveats, and quality notes.
Content-linked
PROCESS

How we work

A transparent, outcome‑oriented workflow so you know what happens when.

  1. 1
    Objectives & hypotheses
    Frame the decisions and unknowns to test.
  2. 2
    Data audit & preparation
    Clean, join, and validate for trust.
  3. 3
    Analytical modelling
    Cohorts, funnels, forecasting, and sensitivity.
  4. 4
    Insight synthesis
    Narrative read‑out that prioritises what matters.
  5. 5
    Recommendations & action plan
    Concrete next steps with owners and metrics.
Project Steps
Aligned workflow
Step 01
Objectives & hypotheses
Step 02
Data audit & preparation
Step 03
Analytical modelling
Step 04
Insight synthesis
DELIVERABLES

What you receive

Clear, reusable assets you can ship, present, and scale.

  • Insight report with narrative findings, charts, and decision implications
  • Analysis workbooks/notebooks & SQL for transparency and reuse
  • Scenario models (revenue, CAC/LTV, pricing/discount impacts, hiring ramps)
  • KPI tree & dashboard brief to operationalise measurement
  • Experiment backlog with expected impact and measurement plans
  • Data dictionary covering fields, transformations, caveats, and quality notes.
Final Package
Ready to use
Insight report with narrative findings, charts, and decision implications
Analysis workbooks/notebooks & SQL for transparency and reuse
Scenario models (revenue, CAC/LTV, pricing/discount impacts, hiring ramps)
KPI tree & dashboard brief to operationalise measurement

OUTCOMES

Clarity on drivers

Know what moves the metrics and why.

Actionable roadmap

Prioritised initiatives tied to outcomes.

Operational alignment

Shared definitions that reduce debate.

Ready to get started?

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