Forecasting Dashboard

Note
The Predictive Forecasting Dashboard provides data-driven projections of your future business performance. Use it to make informed decisions about inventory management, marketing spend, and financial planning by viewing anticipated revenue, subscriber growth, and churn.

Before you start

Check your dashboard version: This guide covers the latest version (V3) of the Predictive Forecasting Dashboard. If you're using an older version, some features and metrics may appear differently.
Permissions required: You need admin access or access to analytics to view the Predictive Forecasting Dashboard. Learn more about permissions.
Data refresh frequency: The dashboards update every four hours. Data for the current day may lag slightly compared to real-time Shopify reporting.
What you should know: Forecasts are projections based on historical data and current subscription schedules. The dashboard uses a self-correcting model that learns from past performance to improve accuracy over time. The longer you use the dashboard, the more it adapts to your specific business patterns.
Exporting data: You can export the aggregated forecast data as it appears in the dashboard's tables and charts to a CSV file.

What is the Predictive Forecasting Dashboard?

The Predictive Forecasting Dashboard transforms your historical data into actionable projections of future performance. It answers critical questions like:

What revenue will my existing subscribers generate in the upcoming period?
How many new subscriptions will I acquire?
How many subscriptions will cancel?
What's my projected net subscriber growth?
How many product units do I need for inventory planning?
How many orders will I need to fulfill?

The dashboard models three interconnected aspects of your subscription business: revenue from existing customers, acquisition of new customers, and churn of existing customers.

Filters and KPI cards

At the top of the dashboard, you'll find controls for customizing your view:

Date Range Picker: Select the time period you want to analyze
Granularity Selector: View data by Daily, Weekly, Monthly, or Quarterly periods
AI-enhanced filters: Applies predictive logic that adjusts forecasted values based on learned accuracy ratios from past performance.
- Include add-ons: Option to include one-time add-on product revenue in the forecast.
- Include historical trends: Enables historical self-correction logic that refines forecast accuracy based on how prior forecasts compared to reality.
- Factor in activations and cancellations: Projects future net revenue by factoring in forecasted subscriber churn and new activations.
Queued Revenue: Displays the total projected net revenue from all subscription renewals scheduled within the selected date range, based on current subscriber data. This serves as the “baseline committed income” before any forecast adjustments.
Queued Units: Shows the total quantity of individual product units expected to be shipped within the selected forecast window. Useful for inventory forecasting and planning.
Queued Orders: Represents the count of unique subscription orders currently queued (i.e., scheduled) to renew within the selected date range. This is a direct forecast of fulfillment volume.
Projected Revenue: The total expected subscription revenue for the selected period. This includes only recurring charges (not one-time upsells) unless configured otherwise. Matches the Forecasted Revenue definition from the glossary.
Projected Activations: The number of new subscriptions predicted to be created during the selected time window. This is based on historical activation trends, day-of-week patterns, and week-over-week momentum.
Projected Cancellations: The number of subscriptions expected to cancel during the same period. Derived from recent churn patterns and trends. Comparing this to activations gives a quick snapshot of projected net subscriber growth.
Projected Units: Total quantity of product units forecasted to ship through upcoming subscription renewals in the selected timeframe.
Projected Orders: The count of unique subscription orders expected to process in the same date range. Useful for forecasting fulfillment workload.

Revenue Forecast vs. Historical chart

Projects upcoming revenue, product units, and order counts based on the billing schedules of all active subscriptions. This forms your baseline of predictable income.

This chart compares forecasted revenue against historical and queued data to evaluate model performance and build trust with merchants. Hover over any point of the graph to see a breakdown for that day:

Forecasted (pink): Projected revenue based on the self-correcting predictive model.
Queued (purple): Revenue from already-scheduled future subscription orders.
Historical (gray): Actual revenue from the same past period, used to calculate adjustment ratios.

Activation vs Cancellations Forecast

This section helps merchants visualize projected subscriber growth by directly comparing forecasted activations (new sign-ups) against cancellations (churn).

Activations: The total number of new subscriptions predicted to begin within the selected date range.
Cancellations: The projected number of existing subscriptions expected to cancel. Shown as a negative value to reinforce that this subtracts from net growth.

This bar chart compares daily activations (in pink) and cancellations (in red) over time.

Pink bars = Projected activations: These show when new subscriptions are expected to start, based on historical acquisition patterns and growth trends.
Red bars = Projected cancellations: These represent days when existing subscriptions are likely to churn, using past cancellation behavior as a predictor.

This chart helps you understand whether your subscriber base is projected to grow or shrink over time. You can see how many new subscribers you’re likely to add versus how many you may lose, and on which days.

Product Forecast

A detailed forecast view by product variant.

Product: Name of the subscription product.
Variant: Specific version of the product (e.g., flavor, size).
SKU: Inventory SKU tied to that variant.
Queued Revenue: Committed revenue already scheduled from future queued subscription orders.
Projected Revenue: Forecasted revenue expected based on predicted growth trends.
Queued Units: Number of individual units already scheduled to be fulfilled.
Projected Growth: Additional units projected based on activation and expansion models.
Queued Orders: Number of unique subscription orders already queued.

Note
Use this section to plan inventory per SKU and understand which products are expected to generate the most future revenue.

Subscription Actions Breakdown

A summary of customer actions affecting the forecast.

Action: Type of subscription change (e.g., Canceled, Paused, Skipped).
Projected Growth: Expected impact of that action type on future revenue (positive or negative).
Total Actions: Number of times this action occurred in the selected date range.

Note
This breakdown explains why your forecast might shift. For example, a spike in “Paused” actions will temporarily reduce forecasted revenue.

How the date range affects revenue forecast calculations

The date range selector in your Revenue Forecast dashboard determines whether you're viewing a stored historical snapshot or a live query of current data.

Calculation date

The revenue forecast is generated by analyzing all active subscriptions on a specific date and projecting future payments based on their status at that moment.

When you select a date range, the start date determines which forecast snapshot you're viewing:

Historical date range (e.g., Nov 1 – Nov 30): The dashboard displays the forecast that was calculated on November 1st, using subscription data as it existed on that day. This is a stored snapshot.
Current date range (e.g., today's date): The dashboard runs a live query and calculates the forecast based on your current active subscriptions.

Why this matters

Historical snapshots let you compare how your forecast has changed over time. For example, if you view the forecast for November 1st today, you'll see the prediction that was made on November 1st, not a recalculation of that date using today's data.

This approach helps you track forecast accuracy and understand how changes to your subscription base (like cancellations, upgrades, or new signups) impact projected revenue.