Smash Types
~6 min readEach smash type targets a specific area of your Shopify store data. Here’s what each one does and when to use it.
Revenue
What it shows: Net and gross sales revenue for the selected time window, compared to the previous period.
Best for: Tracking overall revenue growth. Set a percentage growth goal to turn this into an OKR.
Supported visualizations: Metric, Sparkline
Default window: Last 30 days
Example goal: “Grow 10% vs last period — reach $12,870”
Tip: Use Metric with a goal for your main revenue OKR. Use Sparkline on a secondary dashboard to see revenue trends day-by-day.
Orders
What it shows: Total order count for the selected window, vs. the previous period.
Best for: Tracking order volume — useful for promotions, seasonal campaigns, or daily operations.
Supported visualizations: Metric, Sparkline
Default window: Last 30 days
Example goal: “Hit 500 orders this period”
Avg Order Value (AOV)
What it shows: Average revenue per order (net sales ÷ orders), vs. the previous period.
Best for: Tracking upsell and bundle effectiveness. Rising AOV with flat order count means more revenue per customer.
Supported visualizations: Metric
Default window: Last 30 days
Example goal: “Grow 5% vs last period — reach $68”
Items Sold
What it shows: Total net items sold in the selected window, vs. the previous period.
Best for: Tracking product volume — especially useful for stores with variable order sizes.
Supported visualizations: Metric, Sparkline
Default window: Last 30 days
Revenue Trend
What it shows: A day-by-day Sparkline chart of net revenue over the selected period, with a period-over-period delta.
Best for: Spotting patterns — weekday vs. weekend peaks, promotional spikes, slow periods.
Supported visualizations: Sparkline, Chart
Default window: Last 30 days
Tip: Add smoothing (e.g. “Rolling 7-day”) to reduce day-to-day noise and see the underlying trend more clearly.
Store Sessions
What it shows: Online store visitor sessions for the selected window, vs. the previous period.
Best for: Tracking traffic during ad campaigns, organic search growth, or seasonal traffic analysis.
Supported visualizations: Metric, Sparkline
Default window: Last 30 days
Note: Sessions come from Shopify’s built-in analytics. They reflect visits to your online store, not the Shopify admin.
Sessions Trend
What it shows: A day-by-day Sparkline of visitor sessions over the selected period.
Best for: Seeing traffic patterns alongside revenue trends — put both on the same dashboard to spot conversion rate changes.
Supported visualizations: Sparkline, Chart
Default window: Last 30 days
Top Products
What it shows: The top 10 products ranked by gross revenue for the selected period, displayed as a table.
Best for: Identifying your best sellers and spotting which products drive the most revenue.
Supported visualizations: Table
Default window: Last 30 days
Tip: Compare with a longer window (Last 90 days) on a separate smash to see if your top products are shifting over time.
Inventory Health
What it shows: A summary of inventory status — total products tracked, how many are low stock, and how many are out of stock.
Best for: Operational dashboards where you need a quick signal that something needs attention.
Supported visualizations: Badge (summary numbers)
Configuration settings:
| Setting | Default | What it does |
|---|---|---|
| Low stock threshold | 10 units | Flags products below this quantity as low stock |
| Critical threshold | 3 units | Flags products below this quantity as out of stock |
Note: Inventory Health uses Shopify’s product inventory API directly. It does not use SmashQL.
Orders Tracker (Legacy)
What it shows: Order count with a Gauge visualization showing progress toward an order goal.
Best for: Older dashboards. For new dashboards, use the Orders smash instead.
Supported visualizations: Gauge
Revenue Monitor (Legacy)
What it shows: Revenue with a Gauge visualization showing progress toward a revenue goal.
Best for: Older dashboards. For new dashboards, use the Revenue smash with a goal configured.
Supported visualizations: Gauge
Query Lab (Developer)
Query Lab is a special smash type for exploring raw data. See Query Lab for full details.
Next Steps
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