Deepnote

Connect to Salesforce data source.

Use SQL to extract historical sales data.

Use Python and machine learning models to forecast future sales.

Visualize predictions using interactive charts.

Schedule the notebook to run weekly and update forecasts automatically.

Connect to customer database.

Use SQL to extract customer data and identify churn indicators.

Use Python to build a churn prediction model.

Create a dashboard to visualize churn risks and patterns.

Share the dashboard with the customer success team.

Connect to A/B testing data from Google Analytics.

Use Python to analyze test results and calculate statistical significance.

Generate a report summarizing the findings.

Present the report to stakeholders.

Connect to AWS Cost Explorer API.

Use Python to analyze cost data and identify cost-saving opportunities.

Create visualizations to show cost trends and breakdowns.

Schedule the notebook to run monthly and provide cost reports.

Connect to NPS survey data.

Use Python to analyze survey responses and calculate NPS.

Identify key drivers of customer satisfaction and dissatisfaction.

Present the findings to product and marketing teams.

Connect to Salesforce, marketing automation platforms, and customer support databases.

Use SQL and Python to consolidate customer data into a unified view.

Create a dashboard to visualize customer interactions, purchase history, and support tickets.

Provide access to sales, marketing, and support teams.