E-Commerce Analytics: AI in Google Sheets for Revenue

E-commerce analytics has a data export problem. Your revenue data lives in Shopify. Your traffic data lives in Google Analytics 4. Your ad spend is split between Meta Ads Manager, Google Ads, and maybe TikTok. To understand what is actually working, you need to pull all of it into one place, which for most e-commerce teams means Google Sheets.

The exports are easy. Every platform has a CSV download or an API connector that dumps data into Sheets automatically. The hard part is turning those raw exports into answers. A Shopify order export gives you thousands of rows of transaction data, but it does not tell you which customer cohort has the highest lifetime value or whether your Black Friday buyers actually came back. A GA4 export shows you sessions and conversions by channel, but connecting that to actual revenue requires matching it against your order data manually.

Most e-commerce teams spend more time wrangling exported data than analyzing it. o11 For Google Sheets closes that gap by reading your raw exports and producing the analysis directly.

Revenue Cohort Analysis From Shopify Exports

Cohort analysis is one of the most valuable things an e-commerce team can do, and one of the most annoying to build in a spreadsheet. You need to group customers by their first purchase month, track their repeat purchase behavior over subsequent months, and calculate retention and revenue per cohort. In a raw Shopify export, this means deduplicating customers by email, identifying first purchase dates, and building a matrix that updates as new orders come in.

o11 reads your Shopify order export and handles the cohort construction natively.

“Using the Shopify Orders tab, build a monthly cohort analysis. Group customers by their first purchase month. For each cohort, show the number of customers, total revenue, and repeat purchase rate at months 1, 3, 6, and 12. Highlight cohorts with repeat rates below 15%.”

This analysis typically takes an analyst half a day to build from scratch and breaks every time the export format changes slightly. o11 works from the data structure it finds in your sheet, so a new column or a renamed field does not require rebuilding the entire analysis.

The cohort view immediately surfaces actionable patterns. You might discover that customers acquired during a 20%-off promotion have significantly lower repeat rates than those acquired through organic search, which changes how you think about your promotional calendar.

“Compare repeat purchase rates between customers whose first order included a discount code versus those who paid full price. Break it down by acquisition quarter.”

That follow-up query, which would take another hour of manual pivot table work, runs in seconds against the same data.

Marketing Channel Attribution From GA4 Data

GA4’s data model is powerful but dense. The raw export gives you events, sessions, and attribution data across multiple dimensions, and making sense of it in Sheets usually means building complex QUERY formulas or pivot tables that take hours to get right.

o11 reads your GA4 export and produces attribution analysis in plain terms.

“Analyze the GA4 Channel Report tab. Calculate revenue per session by channel. Show ROAS for paid channels using the ad spend data in the Ad Spend tab. Rank channels by revenue contribution and flag any channel where cost per acquisition exceeds $45.”

This query does something that would normally require three separate analyses: it calculates channel efficiency, computes ROAS by joining GA4 data with your ad spend sheet, and flags channels that are above your CPA threshold. In a manual workflow, each of those is a separate pivot table or VLOOKUP exercise.

The attribution analysis also helps you spot trends you would otherwise miss.

“Show how our channel mix has shifted over the last 6 months. Which channels are growing as a percentage of revenue and which are declining? Overlay the CPA trend for each.”

Understanding not just where revenue comes from today, but how the mix is changing, is critical for budget allocation decisions. Most e-commerce teams only run this analysis quarterly because of the time involved. With o11, it becomes something you check weekly.

Inventory Forecasting From Sales Velocity Data

Stockouts kill revenue. Overstocking kills cash flow. Getting inventory levels right requires combining sales velocity data with lead times, seasonal patterns, and promotional calendars, a calculation that most e-commerce teams do in Sheets but rarely do well because the formula complexity grows quickly.

o11 builds inventory forecasts by reading your sales history and current stock levels together.

“Using the Sales Velocity tab and the Current Inventory tab, calculate days of stock remaining for each SKU at current sell-through rates. Flag any SKU that will stock out within 21 days based on average daily sales over the last 60 days. Factor in the lead times from the Supplier tab.”

This gives you an immediate view of which products need reorders now, not next week when someone gets around to checking the inventory report. The analysis cross-references three separate tabs, something that in a manual workflow means building INDEX/MATCH chains between sheets and maintaining them as data changes.

For seasonal planning, o11 can layer in historical patterns.

“We’re approaching our summer peak. Using last year’s June-August sales data from the 2025 Sales tab, project demand uplift by product category. Compare projected demand against current inventory to identify gaps.”

Seasonal forecasting in a spreadsheet usually means a dedicated planning exercise. With o11, it becomes a query you run as part of your regular inventory review.

Before and After: E-Commerce Weekly Analytics

Before o11:

• Export data from Shopify, GA4, and ad platforms into separate tabs manually
• Spend 3-4 hours building pivot tables and VLOOKUP chains to connect datasets
• Cohort analysis built once per quarter because it takes half a day
• Channel attribution calculated in a separate workbook with manual data entry
• Inventory checks done reactively when a product stocks out
• Insights trapped in Sheets, manually recreated when someone needs a slide

After o11:

• Raw exports land in Sheets; o11 analyzes them in place
• Cohort analysis built on demand from current Shopify data
• Channel attribution with ROAS calculated across GA4 and ad spend tabs automatically
• Inventory stockout alerts and reorder recommendations available in minutes
• Seasonal demand projections layered onto current inventory data
• Analysis and charts flow directly to Google Slides for stakeholder reviews

Why o11 Instead of Shopify’s Built-In Analytics or a BI Tool

Shopify’s analytics dashboard covers the basics, but it cannot join your Shopify data with your GA4 attribution data and your ad spend to calculate true ROAS by channel. BI tools like Looker or Tableau can do that analysis, but they require a data warehouse, a setup process, and someone who knows SQL or their query language. For most e-commerce teams under $50M in revenue, the BI tool is aspirational but the spreadsheet is real.

o11 works where your data already lives. It reads the exports you already pull into Sheets and connects them without requiring a data pipeline. When your analysis needs to go beyond the spreadsheet, into a Google Slides deck for your weekly leadership meeting or a Google Doc for your quarterly planning memo, o11 handles that transition natively.

The result is BI-grade analysis at spreadsheet speed, without the six-month implementation project.

Try o11 For Google Sheets