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Finally, an editor designed for how analysts actually work. Combine narrative, code, and visualizations in a beautiful, distraction-free interface.
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Customer Churn Analysis Q4 2024
Executive Summary
Our analysis reveals a 23% increase in churn rate among enterprise customers...
SELECT customer_id, churn_date
FROM customers
WHERE status = 'churned'
FROM customers
WHERE status = 'churned'
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Transform isolated work into team intelligence. Every analysis becomes a conversation, every insight builds on the last.
- Inline Comments & DiscussionsAsk questions, suggest improvements, or share insights directly on any part of an analysis
- One-Click SharingShare with your team or specific stakeholders. Control who can view, comment, or edit
- Version HistorySee how analyses evolve over time. Compare versions, restore previous work, never lose insights
Share Analysis
ara.social/analyses/customer-churn-q4
Shared with Marketing TeamCan view
SC
Nathan Barker2 hours ago
Great analysis! Can we segment this by product tier? I suspect enterprise churn is driven by different factors.
From Messy Notebook to Polished Analysis in Minutes
Stop spending hours cleaning up exploratory work. Our AI understands your notebook's journey and transforms it into a clear, executive-ready narrative—complete with key findings, methodology, and recommendations.
- AI-Powered Narrative GenerationUpload any Jupyter notebook and watch as AI extracts the story, organizing scattered cells into coherent sections with proper flow
- Smart Chart SelectionAutomatically identifies your most impactful visualizations and embeds them at the right points in the narrative
- Clean Code ExtractionPulls out only the essential code snippets, removing all the trial-and-error cells while preserving your methodology
customer_analysis_v3_final_FINAL.ipynbBefore
# Cell 1
NameError: name 'pd' is not defined
NameError: name 'pd' is not defined
# Cell 14
import pandas as pd
df = pd.read_csv('data_v2.csv')
import pandas as pd
df = pd.read_csv('data_v2.csv')
# Cell 37
# TODO: fix this later
print(df.shape)
# TODO: fix this later
print(df.shape)
... 84 more cells ...
AI transforms in seconds
Customer Retention Analysis - Q4 2024After
Executive Summary
Our analysis of 50,000 customer records reveals three critical retention drivers...
Key Findings
- 68% of churn occurs within the first 90 days
- Premium users have 3.2x higher retention
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