General description

Why this page? To give you a shared understanding of Datama’s philosophy, the market equation methodology, and the use cases covered by each solution (Compare, Detect, Pivot, Assess, Journey, Prep).

🎯 What is Datama?

Datama helps you analyze performance faster and more reliably by quantifying the drivers of variation (mix, volumes, prices, conversion rates, etc.) and guiding you toward actions with measurable impact.

• For teams: data, marketing, product, e-commerce, finance.
• Expected outcome: clear explanations, quantification and faster decisions.

Datama Solutions

Datama has 5 solutions to help you make the right decision faster

✅ When to use which solution?

• You see a variation (up/down) → Use Compare to explain it with a waterfall.
• You want to be alerted in case of anomaly → Use Detect to spot and track it.
• You want to explore segments/ opportunities → Use Pivot to dig deeper.
• You need to measure significance of a variation → Use Assess to quantify impact.
• You want to know which step to rebuilt in priority → Use Journey to understand paths and drop-offs.
• Your want to connect your data to our solutions! → Use Prep to make it clean and usable.

🧮 Our methodology: the “market equation”

The goal is to break down any KPI variation (e.g. revenue, leads, margin, conversion) into multiplicative factors: volume × avg. price × …
Then to separate mix effects (composition changes) from true performance effects (rate, price, conversion at constant mix).

Core principles:

• Factorized decomposition of KPIs (e.g. Revenue = Visits × Conversion Rate × AOV).
• Mix effect: variation due to changes in composition between segments (countries, channels, devices, products…).
• Performance effect: true improvement/decline at constant segment mix.
• Comparability: across periods, cohorts, audiences (B2B/B2C), channels, or products.

💡 Best practices

• Always define a clear baseline period (period vs period, vs LY, vs reference).
• Use a fixed taxonomy (channels, devices, products) before comparing.
• Disentangle mix vs performance effects to avoid paradoxes.

📬 Need help?

Questions on the methodology, a specific case, or a segment?
Reach out to us, we’ll be glad to help.