Create a Monthly E-Commerce Report with AI

Turn your sales, traffic, advertising and product data into a monthly action plan, not just another spreadsheet.

Most e-commerce businesses have plenty of data.

Shopify has sales data.

Google Analytics has traffic and conversion data.

Google Ads and Meta have advertising data.

Amazon, Walmart and other marketplaces have their own reports.

Your accounting system has costs.

Your inventory system knows what’s sitting in the warehouse.

The problem isn’t getting more numbers.

The problem is figuring out what those numbers are actually telling you.

In this Practical Workshop, we’re going to use AI to turn your monthly e-commerce data into something much more useful:

A management report that explains what happened, why it may have happened, what deserves attention, and what you should do next.

Let’s build it.

Step 1: Decide What Your Report Should Answer

Before downloading anything, decide what management actually needs to know.

A useful monthly report should answer six questions:

1. How did we perform?

2. What changed compared with last month or last year?

3. Which products and channels drove the change?

4. What worked and what didn’t?

5. Are there any problems or opportunities we should investigate?

6. What should we do next month?

This prevents your report from becoming 20 pages of numbers nobody uses.

Step 2: Collect Your Core Sales Data

Start with your e-commerce platform.

For Shopify, export the relevant monthly sales reports.

At minimum, collect:

• Total sales
• Net sales
• Orders
• Units sold
• Average order value
• Discounts
• Returns/refunds
• Sales by product
• Sales by channel

If you sell through multiple channels, also collect comparable reports from Amazon, Walmart, Best Buy or other marketplaces.

Put everything into one spreadsheet.

Your summary table might look like:

Channel	Revenue	Orders	AOV	Units	Returns
Shopify	$28,500	175	$163	214	$1,240
Amazon	$41,200	310	$133	372	$2,180
Walmart	$18,600	121	$154	148	$760
Best Buy	$12,900	63	$205	71	$430

Now AI has structured information it can analyze.

Step 3: Add the Comparison

A number without context isn’t very useful.

Suppose August revenue was:

$101,200

Is that good?

We don’t know.

But now add:

July: $91,800

August last year: $86,400

Suddenly the number means something.

Ask AI to calculate:

Month-over-month change

and, when available:

Year-over-year change

Do this for:

• Revenue
• Orders
• AOV
• Units
• Conversion rate
• Advertising spend
• Returns
• Profit

Your report should make direction immediately visible.

Don’t make the reader calculate it mentally.

Step 4: Add Website and Conversion Data

Now bring in Google Analytics or your store analytics.

Useful metrics include:

• Sessions
• Users
• Product views
• Add-to-carts
• Checkout starts
• Purchases
• Conversion rate
• Traffic sources

Now AI can start separating traffic problems from conversion problems.

Imagine:

Traffic increased 22%.

Revenue increased only 4%.

That tells you something.

Or:

Traffic decreased 8%.

Revenue increased 12%.

That tells you something completely different.

Ask AI:

“Compare traffic, conversion and sales performance. Identify any unusual relationships between the metrics, but do not assume causation where the data doesn’t prove it.”

That final instruction matters.

AI should identify questions to investigate, not invent explanations.

Step 5: Add Advertising Data

Next, add Google Ads, Meta Ads, Amazon Ads or marketplace advertising.

Include:

• Ad spend
• Impressions
• Clicks
• CPC
• Conversions
• Revenue attributed to ads
• ROAS

Then ask AI:

“Which campaigns improved, which deteriorated, and which deserve investigation?”

But don’t stop at ROAS.

A campaign can have a great ROAS and still generate poor profit.

Which brings us to the most important step.

Step 6: Add Product Economics

Suppose a campaign shows:

Revenue: $10,000

Advertising: $2,000

ROAS: 5X

Looks excellent.

But now add:

Product cost: $5,000

Marketplace fees: $1,500

Shipping/fulfilment: $900

Advertising: $2,000

Suddenly:

$10,000
− $5,000
− $1,500
− $900
− $2,000
= $600 contribution

The campaign still made money.

But that 5X ROAS looks very different now.

Where possible, give AI:

• Selling price
• COGS
• Platform fees
• Payment fees
• Fulfilment
• Shipping
• Advertising
• Discounts
• Returns

Then calculate contribution profit.

Now you can ask a much better question:

“Which products and channels generated the most contribution and not simply the most revenue?”

That’s management information.

Step 7: Ask AI to Find What Changed

Now upload your completed spreadsheet to your AI tool.

Don’t begin with:

“Analyze this.”

Give it a job.

Try:

“You are an e-commerce performance analyst. Analyze this month’s results against last month and the same month last year. Identify the five most important positive and negative changes. For each change, show the supporting numbers. Separate facts from possible explanations. If the data does not prove why something happened, tell me what additional information I should investigate.”

That last part is extremely important.

Good AI analysis should say:

“Conversion declined from 3.1% to 2.4%.”

It should not automatically say:

“Customers disliked your website redesign.”

Unless you have data supporting that conclusion.

AI should help you investigate, not manufacture certainty.

Step 8: Analyze Products Individually

Overall sales can hide important product movements.

Ask AI:

“Identify products with unusually large changes in sales, units, conversion, returns or advertising efficiency.”

You might discover:

Product A: Revenue +38%

Product B: Traffic +44%, sales −7%

Product C: Revenue +21%, contribution −12%

Product D: Returns doubled

Product E: Strong conversion but low traffic

Each one suggests a different action.

Product A:

Can we scale it?

Product B:

Why isn’t traffic converting?

Product C:

Are discounts or advertising destroying margin?

Product D:

What is causing returns?

Product E:

Should we send more traffic to it?

This is where the report starts becoming a strategy document.

Step 9: Ask AI to Create an Executive Summary

Business owners usually don’t want to read every table.

Give them the story first.

Ask AI:

“Write a five-point executive summary for the business owner. Focus only on changes that materially affected revenue, profitability, customer behaviour or future risk. Include numbers supporting every major conclusion.”

A useful summary might say:

Sales increased 10.2% month-over-month, primarily driven by Shopify and Amazon.

Traffic increased 17%, but conversion declined from 2.9% to 2.5%, suggesting the additional traffic was less efficient.

Product A contributed 34% of total revenue growth.

Product C achieved 5.1X ROAS but produced relatively low contribution after discounting and marketplace costs.

Product D’s return rate increased significantly and should be investigated before increasing advertising.

That’s something management can act on.

Step 10: Make AI Recommend Actions

Now ask:

“Based only on the evidence in this report, recommend five actions for next month. Rank them by expected business impact and explain which data supports each recommendation.”

You might get:

Priority 1: Investigate Product B’s conversion decline

Traffic increased substantially without corresponding sales growth.

Priority 2: Increase exposure for Product E

Conversion is strong but traffic remains low.

Priority 3: Review Product C promotion economics

Revenue increased while contribution declined.

Priority 4: Investigate Product D returns

Return rate increased materially.

Priority 5: Reallocate advertising budget

Shift spend from low-contribution campaigns toward products with stronger economics.

Now your monthly report ends with:

What are we going to do?

Not:

Here are some charts.

Step 11: Turn It Into a Repeatable Monthly Workflow

The real benefit comes next month.

Create a standard folder:

Monthly E-Commerce Report

Inside:

01 — Sales

02 — Website Analytics

03 — Advertising

04 — Marketplace Reports

05 — Product Costs

06 — Inventory

Then use the same spreadsheet template and the same AI analysis instructions every month.

Your workflow becomes:

Export data → update spreadsheet → AI analyzes → human verifies → management report → action plan.

Once the structure works, much of this can eventually be automated.

Your Final Report Can Be Surprisingly Simple

You don’t necessarily need a 30-page presentation.

A strong monthly e-commerce report could contain:

1. Executive Summary

Five things management needs to know.

2. Sales Performance

Revenue, orders, AOV and growth.

3. Channel Performance

Shopify vs marketplaces.

4. Product Performance

Winners, losers and unusual movements.

5. Marketing Performance

Traffic, conversion, advertising and ROAS.

6. Profitability

Contribution by product or channel where data allows.

7. Risks & Opportunities

Inventory, returns, conversion issues and emerging winners.

8. Next-Month Action Plan

Three to five specific priorities.

That’s enough.

One Important Rule: AI Should Explain the Number Before Recommending the Action

This is where AI reporting can go wrong.

Don’t accept:

“Increase advertising for Product A.”

Ask:

Why?

A good recommendation should look like:

Product A generated $18,400 in revenue, up 31% month-over-month. Conversion increased from 3.2% to 4.1%, contribution margin remained stable and inventory coverage is sufficient for approximately eight weeks. Based on these indicators, testing additional advertising may be justified.

Now you can evaluate the reasoning.

Number → interpretation → recommendation.

Not:

AI says we should spend more money.

Start With a Spreadsheet, Not an Expensive AI System

You don’t need a complicated business-intelligence platform to begin.

Start with:

Your existing reports + Excel or Google Sheets + an AI assistant.

Build the reporting structure manually.

Learn which metrics actually matter.

Improve your prompts.

Identify the recurring analysis.

Then decide what should be automated.

That’s much safer than automating a reporting process you haven’t properly designed yet.

Your Monthly Report Should Tell You What to Do Monday Morning

The purpose of an e-commerce report isn’t to prove that someone looked at the numbers.

It’s to improve the next decision.

At the end of every monthly report, management should understand:

What happened?

Why does it matter?

What needs investigation?

Where is the opportunity?

What are we doing next?

AI can dramatically reduce the time required to find those answers.

But the value doesn’t come from AI writing the report.

The value comes from turning scattered business data into better decisions.

Not Sure Where to Start?

If your e-commerce data is scattered across Shopify, marketplaces, advertising platforms and spreadsheets, and you’re not sure how to turn it into a useful management report? NorthPilot Digital Agency can help.

We can help you identify the metrics that actually matter, structure your reporting workflow, incorporate profitability and channel performance, and build an AI-assisted reporting system designed around the decisions your business needs to make.

Contact NorthPilot and let’s turn your e-commerce data into an action plan for growth.