According to a document from January 9, 2026, here is a unified guide that starts with Data Warehouse (BigQuery) and weaves in AI-Powered Report Builder, AI-Powered Report Viewer, and AI-Powered Report Dashboards so non-technical users can build custom reports and dashboards end-to-end.

In today's competitive e-commerce landscape, a rigid, one-size-fits-all analytics strategy is no longer sufficient. Agility and depth are paramount. This handbook details UltraCart's powerful, layered approach to data analysis, which combines the rapid, conversational insights of an AI Report Builder with the robust, scalable power of a production-grade Google BigQuery data warehouse.

The recommended workflow is designed for maximum efficiency and accuracy. Best practice is to start with the AI Report Builder for rapid exploration, ad-hoc questioning, and hypothesis testing. Once a query has been validated and proven valuable, the next step is to promote the generated SQL to BigQuery. This allows you to build robust, scheduled, and long-lived business intelligence assets like automated reports and executive dashboards.

This dual-pronged strategy empowers your entire organization. Non-technical business managers can ask questions in plain English to get immediate answers, while data-savvy analysts can leverage the same underlying source of truth for complex modeling and deep-dive analysis. It democratizes data without sacrificing depth, ensuring everyone is working from the same playbook.

This entire analytics ecosystem is built upon a solid, high-performance foundation: the BigQuery Data Warehouse. Understanding this foundational layer is the first step to unlocking its full potential.

UltraCart’s Data Warehouse (BigQuery) streams your store’s operational data into Google BigQuery so you can analyze customers, items, orders, and auto orders (typically with a 1–2 minute delay).

On top of that data warehouse, UltraCart provides an AI-Powered Report Builder that lets you ask questions in plain English and generates:

• SQL

• parameters (like date ranges)

• sample data previews

• visualizations

• an “expert analysis” prompt for insights when viewing the report later

You then use:

• AI-Powered Report Viewer to inspect results (visuals, raw data, SQL, AI insights, and chat)

• AI-Powered Report Dashboards to assemble saved reports into shareable dashboards and schedule snapshot emails

Open

Prerequisite: The UltraCart OWNER USER is the only user who can grant access to the Data Warehouse (BigQuery) integration.

Before you begin:

• You have access to your UltraCart account as Owner (or can request the Owner grant access).

• Any user who needs BigQuery access has an email that is a Google account email.

• You know where to find your BigQuery project URL in UltraCart:

  • Configuration → Developer → Data Warehouse (BigQuery)

UltraCart streams changes into BigQuery tables in a streaming dataset so changes appear quickly (typically under ~2 minutes).

However:

• You should not query the streaming tables directly because they contain one row per mutation, which can produce unexpected results.

• Instead, you use the “regular” datasets (views) that provide consistent snapshots and apply security rules.

UltraCart covers the first $1.00 of BigQuery project expense, which is typically enough to cover storage and update bandwidth; your variable cost is mainly the queries you run. UltraCart applies the previous month’s warehouse usage to your bill monthly.

The document summarizes BigQuery pricing as:

• storage: $0.02/GB (first 10GB free)

• queries: $5.00/TB processed

To let someone access your data warehouse:

1. Go to Configuration → Account & Users → Users.

2. Edit the user.

3. Grant the appropriate BigQuery permission(s).

Tip: Grant the minimum level of data access needed for the user’s role.

Go to:

• Configuration → Developer → Data Warehouse (BigQuery)

That page provides the project URL and dataset descriptions.

[Image Placeholder: “Configuration → Developer → Data Warehouse (BigQuery) screen showing Project URL + datasets”]

UltraCart uses:

• Google Cloud IAM to restrict access to only users granted access

• BigQuery column-level security to restrict sensitive fields by permission level

• SHA-256 hashes for restricted values so you can still query/join without exposing raw PII

The document describes these datasets and security levels:

If you use linked accounts, the parent account also has “linked” versions of these datasets that include data across all linked accounts.

The document lists objects streamed into the warehouse, including (among others):

• affiliates, affiliate clicks, affiliate ledger

• auto orders, customers, items, orders

• gift certificates

• storefront customers, storefront experiments

• upsell offers, upsell offer events, upsell paths

• storefronts

• screen recordings

• rotating transaction gateway history

• towerdata email intelligence

Note: All datetime values stored in BigQuery are UTC, which differs from UltraCart’s typical EST usage.

If you don’t want to write SQL, UltraCart’s AI-Powered Report Builder lets you query your BigQuery datasets in plain English and outputs a complete report.

A practical “merchant-friendly” workflow is:

1. Ask a question in AI Report Builder (plain English)

2. Save the report

3. Open it in Report Viewer to validate results and read AI insights

4. Add saved reports to a Dashboard to create a KPI view for your team

5. Schedule dashboard snapshots for recurring visibility

[Image Placeholder: “Workflow diagram — Builder → Viewer → Dashboard → Scheduled snapshots”]

AI Report Builder allows store owners to explore and visualize storefront data using natural language; the AI transforms your question into SQL and returns a complete report.

As you create a report, the builder supports:

• SQL transparency (copy/reuse SQL elsewhere; clearly labeled inputs like @start_date and @end_date)

• Sample Data Preview to validate structure efficiently (full dataset appears in the Viewer after saving)

• Visualization Panel (request chart tweaks via chat; refine later in the saved report)

• Expert Analysis Prompt (controls what the AI will analyze when you view the report later; you can refine it now)

• Save options:

  • Report Name (alphanumeric + spaces only)

  • optional Group Name for organization

[Image Placeholder: “AI Report Builder screen — prompt + sample data + visualization + analysis prompt + save dialog”]

The AI can access UltraCart BigQuery datasets including:

• Orders, Auto Orders, Customers, Items

• Marketing: UTMs, affiliate data, click streams

The AI auto-selects a chart type, but you can request specifics (e.g., “Use a stacked bar chart” or “Highlight top 5 regions”).

Supported chart types include (examples):

• line, bar/stacked bar, pie/donut

• heatmaps/choropleths (U.S. state maps supported)

• scatter, funnel, area

• cohort grids, dual-axis overlays

• tables with inline graphics/trendlines

(Visualization rendering references Apache ECharts.)

Best practices:

• Think in outcomes (“what do I want to learn?”)

• Reuse saved reports for recurring needs

• If you’re technical, reuse the generated SQL directly in BigQuery for advanced analysis

Troubleshooting:

• If the AI misunderstands, rephrase and add context like timeframe and grouping (“last 30 days”, “by product category”).

After saving, open the report in the Viewer to:

• see the visualization and full data

• inspect the exact SQL

• read the AI “expert analysis”

• ask follow-up questions in chat about the results

[Image Placeholder: “Report Viewer screen — visualization + data + SQL + analysis + chat”]

Tip: Use the Viewer as your “sanity check” step before adding a report to dashboards. If something looks off, go back to the prompt and specify filters, groupings, or date ranges more explicitly.

Once you have a few saved reports, you can build dashboards for teams and recurring review.

A good pattern for non-technical users:

1. Create 5–12 “tile-ready” reports (each one should answer one clear question).

2. Add them to a dashboard grouped by theme (Sales, Marketing, Subscription, Ops).

3. Add pages if you need separate views for different teams (e.g., Marketing vs Operations).

4. Schedule snapshots for leadership.

[Image Placeholder: “Dashboard layout example — 2 columns, KPI tiles on top, trend charts below”]

Below are practical examples that map directly to common merchant questions and the types of sample analyses referenced in the BigQuery documentation list.

Tile 1 — Revenue and order count trend

• Prompt: “Show total revenue and order count by week for the last 12 weeks. Use a dual-axis line chart and call out the top 3 weeks by revenue.”

Tile 2 — New vs returning customers

• Prompt: “Show new customers vs returning customers by week for the last 12 weeks, and include a % returning metric.”

Tile 3 — Repeat customer rate

• Prompt: “Show repeat customer rate by month for the last 12 months. Use a line chart.”

(BigQuery doc includes “Repeat Customer Rate” as a sample query topic.)

Tile 1 — UTM sales by week

• Prompt: “Revenue by week segmented by UTM source/medium for the last 90 days. Use a stacked area chart.”

(BigQuery doc includes “UTM Sales By Week.”)

Tile 2 — Coupon performance

• Prompt: “Summarize coupon usage: count of orders and total discounted amount by coupon for the last 60 days. Show a sortable table.”

(BigQuery doc includes “Coupon Usage Summary” / “Coupon Usage Detail.”)

Tile 3 — Affiliate metrics

• Prompt: “Show affiliate clicks vs orders and revenue for the last 30 days. Include conversion rate from click to order.”

(BigQuery doc includes affiliate click/order metrics topics.)

Tile 1 — Auto order churn overall

• Prompt: “Auto order churn overall for the last 6 months by month. Define churn as canceled or disabled auto orders. Show as a line chart.”

(BigQuery doc includes “Auto Order Churn Overall.”)

Tile 2 — Auto order churn by item

• Prompt: “Auto order churn by item for the last 6 months. Show top 10 items by churn count.”

(BigQuery doc includes “Auto Order Churn by Item.”)

Tile 3 — Auto order cohort revenue

• Prompt: “Auto order cohort revenue by first order month, show cohort grid, and highlight the best-performing cohort.”

(BigQuery doc includes auto order cohort revenue topics.)

Tile 1 — Experiment statistics

• Prompt: “Show StoreFront experiment statistics for the last 30 days: conversion rate and revenue per session by variation. Use a table and highlight the winner.”

(BigQuery doc includes “StoreFront Experiment Statistics.”)

Tile 2 — Upsell performance

• Prompt: “Upsell conversion rate and upsell revenue by upsell offer for the last 30 days. Use a bar chart.”

(BigQuery doc includes upsell statistics topics.)

The BigQuery documentation includes the following sample query topics (use these as ready-made ideas to turn into AI Report Builder prompts):

• Adding Calculations to Auto Orders

• Adding EST time zone dates to orders

• Active Auto Order Next Rebill

• Revenue Per Item Over a Certain Time Period

• Query Table Size in GB

• Order ID Experiment Variations

• Orders For Specific Items Shipped By a User on a Specific Date

• Auto Orders with Pre-shipment Notice Date more than one week prior to shipment.

• Orders with a Specific Custom Field Value

• List All Upsell Paths, Variation and Upsells

• Upsell Screenshot List

• Auto Orders with Missed Pre-shipment Notices

• Auto Order Logs Messages After Pre-shipment Notice and Before Shipment Should Take Place

• Customer LTV Modeling

• Find Order Id by Transaction Value

• Auto Order Weekly Cohort by Main Item Id

• Auto Order Future Predictions

• Extract a Marketing List from Order History

• Auto Order Churn Overall

• Auto Order Churn by Item

• Affiliate Metrics

• Customer Cohort Revenue

• Auto Order Cohort Revenue

• Auto Order Cohort Active Count

• Auto Order Cohort Active Percentage

• Order Upsells

• Affiliate Click ↔︎ Order Metrics

• All UTMs for an Order

• Projected Future Revenue Based Upon Initial Item Purchased (Non-Auto Orders)

• UTM Sales By Week

• UTM Clicks and Weighted Revenue

• StoreFront Experiment Statistics

• Auto Order Cohort Related Queries

• Count of Apple Pay/Google Pay/Microsoft Pay Transactions

• New Customers in a Specific Date Range By Email

• Conversion Rate from Analytics Sessions

• Auto Order Percentages with CC vs PayPal 30 Day Moving Averages

• Coupon Usage Summary

• Coupon Usage Detail

• Order Count and Revenue by Hour of Day

• Finding Expensive Queries in a Project

• Wholesale Customer Profile LTV

• Inventory Value at Start Of Each Month

• Auto Order LTV calculation

• Upsell Path Statistics

• Find Upsell Offers Containing Text

• Upsell Statistics by Offer and Path (across all linked accounts)

• Loyalty Balance (Points based Program)

• Loyalty Balance (Store Credit Based Program)

• Page View History

• SKUs Sent to 3PL

• Repeat Customer Rate

• Last X Replacement Shipment Order Ids and Placed By

• Replacement Shipment Count by User for a Period of Time

• Storefront Hostname Per Product Item ID

Even if you’re mostly using AI tools, BigQuery is still useful when you need to:

• join UltraCart data with other BigQuery projects (e.g., ad spend tables), as long as your user has permissions in both projects

• build views to “flatten” nested data for BI tools (a common BigQuery best practice)

• programmatically run queries using a service account pattern described in the document (advanced)

Use BigQuery as the trusted data foundation, and use the AI Report Builder → Report Viewer → Dashboards flow as your non-technical reporting stack. The easiest way to succeed is to:

• keep each report focused on one question

• be explicit about timeframes and groupings in prompts

• validate results in the Viewer before placing them on a dashboard

• reuse and organize reports with consistent naming/grouping

• Turn 3–5 items from the Appendix list into AI Report Builder prompts and save them as reports.

• Build your first dashboard with a “Weekly Performance” page and schedule snapshots.

• If you need help designing a KPI dashboard or building specialized reports, UltraCart Professional Services can write and publish custom reports (contact support).

Listen:

• Data Warehouse (BigQuery): Data Warehouse (BigQuery) | Google Project LocationPreview

• AI-Powered Report Builder: AI-Powered Report BuilderPreview

• AI-Powered Report Viewer: AI-Powered Report ViewerPreview

• AI-Powered Report Dashboards: AI-Powered Report DashboardsPreview