Inflowdash
Forecast and Visualize Your Stripe Subscription Revenue

Inflowdash
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Home: https://inflowdash.com

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What is Inflowdash?

Inflowdash streamlines the process of monitoring and forecasting recurring revenue by integrating directly with your Stripe account. The platform offers a comprehensive dashboard and calendar view, allowing users to visualize upcoming revenue and analyze exact figures for usage-based subscriptions. By providing insights such as due, churned, canceled, and recovered revenue, Inflowdash helps users proactively identify potential financial risks and opportunities.

This tool enables users to make informed financial decisions through historical comparison and clear, concise email digest summaries of revenue forecasts. Inflowdash supports effective cash flow planning, churn management, and expansion revenue tracking, making it an essential resource for subscription-based businesses and financial teams.

Features

  • Stripe Integration: Directly connects to your Stripe account for real-time revenue tracking.
  • Upcoming Revenue Dashboard: Visualizes future revenue with detailed calendar and dashboard views.
  • Usage-Based Subscription Tracking: Displays exact revenue numbers for usage-based models.
  • Churn and Cancellation Monitoring: Tracks due, churned, and canceled subscriptions for better churn management.
  • Recovered Revenue Monitoring: Identifies revenue recovered from failed payments.
  • Historical Comparison: Compares current and previous period revenue.
  • Forecast Summary Email Digest: Sends email summaries with upcoming revenue forecasts.

Use Cases

  • Forecasting monthly or yearly subscription revenue flows.
  • Tracking and managing subscription churn and expansion revenue.
  • Identifying early signs of payment or retention issues.
  • Reporting forecasted revenue to executives or finance teams.
  • Planning business cash flow with greater accuracy.

Helpful for people in the following professions

Inflowdash Uptime Monitor

Average Uptime

100%

Average Response Time

114.38 ms

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