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Customer retention study

Retention analysis by plan

Customer behavior

10,162 observations

Retention

59.8%

Change

+9.3%

Confidence

High

PlanCustomersRetainedChange
Enterprise1,84272.4%+14.8%
Professional3,10661.9%+8.2%
Starter5,21448.7%+3.6%

A better analytics workflow

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01

Bring your data into focus

Upload CSV and Excel files into a versioned workspace built for inspection, preparation, and repeatable analysis.

02

Follow the evidence

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03

Keep the work reproducible

Preserve dataset versions, analysis artifacts, citations, and reports so important findings remain explainable.

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Data workspace

Prepare without losing history

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Statistics

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Visualization

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Build focused charts from the active dataset version and keep visual findings connected to their source variables.

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Dathuma AI

Use AI with analytical context

Plan and execute guided or autonomous analysis while preserving tool results, warnings, limitations, and citations.

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  • Grounded synthesis

Grounded by design

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Version lineage
Tool metadata
Artifact references
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Finding provenance

Customer retention · final synthesis

Finding

Retention improves most strongly for enterprise customers after the third active month.

Dataset

Version 4

Method

Cohort analysis

Artifact

retention-03

Citation

Table 2

Evidence statusFully grounded

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