Each week, download two Business Reports CSVs (sales and traffic by date, and by child item) for the latest week and the one before, paste or upload them, and ask ChatGPT or Claude to compare sessions, unit session percentage and Buy Box percentage by product. Recalculate the totals yourself before acting on what it says.
The review is about change, not description. A chatbot that tells you "sessions were 3,100" has told you nothing. A review that says "sessions rose 6%, units fell 10%, and the whole drop sits in two products, one of which lost the Buy Box" has told you where to look this afternoon. Everything below is built to get you to that second sentence safely, in about half an hour.
Which reports to download, and how
These are Seller Central's own Business Reports, not Amazon's company reports to investors. In Seller Central, open Reports, then Business Reports. Forum and seller-guide descriptions of the layout agree on the main items:
- Sales and Traffic by Date: daily figures across all products, useful for the shape of the week.
- Detail Page Sales and Traffic by Child Item (there are by-parent and other versions too): one row per product, with sessions, page views, units ordered, sales and the offer percentages. This is the report the review is built on.
Pick the date range, include the metrics you need if your view offers column settings, and download the report as a CSV file. Seller guides say most Business Reports update daily, and the most recent day can still be incomplete when you download it. Treat that as a reason to compare complete weeks rather than to read any one number as exact.
Column labels change over time. The ones that matter here usually appear as sessions, page views, unit session percentage (units ordered divided by sessions), units ordered, ordered product sales, and the Buy Box percentage, which Amazon's Business Reports glossary calls the Featured Offer percentage: the share of page views on which the Featured Offer, the Add to Cart spot, appeared for your offer. Open your downloaded file and read the header row before you write a prompt. Use the names you see there.
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Buy nowThe seller in this walk-through
A small seller of roofing fixings and sealants lists five products and fulfils some orders itself. The owner does a review every Monday, using the previous full week (Monday to Sunday) and the week before it. The figures below are illustrative but internally consistent, so the arithmetic can be followed.
| Product | Sessions (now / before) | Units (now / before) | Unit session % (now / before) | Buy Box % (now / before) |
|---|---|---|---|---|
| Hex bolts, 100 pack | 1,240 / 1,310 | 112 / 131 | 9.0% / 10.0% | 97 / 98 |
| Roof sealant | 860 / 640 | 41 / 38 | 4.8% / 5.9% | 88 / 96 |
| Flashing tape | 540 / 520 | 54 / 49 | 10.0% / 9.4% | 99 / 99 |
| Staples for roofing tacker | 310 / 300 | 9 / 21 | 2.9% / 7.0% | 41 / 92 |
| Roof vent | 150 / 160 | 6 / 7 | 4.0% / 4.4% | 100 / 100 |
Totals: 3,100 sessions against 2,930, units 222 against 246, overall unit session percentage 7.2% against 8.4%. Sessions rose about 6% and units fell about 10%. Ordered product sales fell from $4,320 to $3,873, a drop of $447. Nobody reading only the sessions line would suspect trouble. The percentages and the Buy Box column show it.
The prompt
Paste the two weeks as plain text or upload both CSVs, then use this prompt. It fixes the maths rules, asks for evidence and forbids the model from inventing causes.
You are helping a small Amazon seller review weekly Business Reports. Below are two CSV extracts: THIS WEEK and LAST WEEK, one row per product, with sessions, units ordered, unit session percentage, Buy Box percentage and ordered product sales.
Rules:
- Calculate totals yourself and show them. Unit session percentage overall must be total units divided by total sessions, never an average of the row percentages.
- For each product, show the change in sessions, units, unit session percentage, Buy Box percentage and sales, with the dollar effect on sales.
- Rank products by dollar change in sales, largest loss first.
- For each of the top three movers, state what the numbers show and list 2 to 3 possible causes as questions I should check in Seller Central (stock, price, Buy Box, listing changes, ads, reviews). Do not state a cause as a fact.
- End with the three checks I should do first today. Do not recommend changing a listing before checking the offer and stock.
THIS WEEK:
[paste]
LAST WEEK:
[paste]
What a good answer looks like
For the walk-through data, a correct response (illustrative) opens with the totals: sessions 3,100 vs 2,930 (+5.8%), units 222 vs 246 (-9.8%), unit session percentage 7.16% vs 8.40%, sales $3,873 vs $4,320 (-$447). It then ranks the movers: hex bolts -$342, staples -$180, roof vent -$42, with roof sealant +$72 and flashing tape +$45. For the staples it should note the Buy Box fell from 92% to 41% while sessions held steady, and ask: has another seller undercut the price, or did stock run out so the Buy Box switched? For the bolts it should note sessions fell 5% and the percentage fell a full point, and ask what changed in price, reviews and ads.
What you would fix in a weaker answer: it calls the staples problem "a listing quality issue" and suggests rewriting the bullets. That reasoning conflicts with the data, because traffic held and only the offer percentage collapsed. A listing rewrite would not touch it. Push back with "the Buy Box fell from 92% to 41%; what would explain that and what should I check first?" The better question produces the right checklist.
Sanity checks before you trust a word of it
Chatbots handle tables confidently and sometimes wrongly. Run these checks every week. They take five minutes and they are the difference between a review and a fiction.
- Row count. Count the rows in your file and the rows the model says it read. A dropped row is a dropped product.
- Totals against Seller Central. Compare the model's total sessions, units and sales with the totals row of the report, or the sums in your own spreadsheet. They should match to the unit.
- Weighted, not averaged. In the walk-through, a simple average of the five product percentages is 6.1%, but the true overall percentage is 7.2%. If the model quotes 6.1%, it averaged the rows, and every conclusion that leans on it is shaky.
- Spot-check one division. Take any product: units divided by sessions should equal the unit session percentage, within rounding. 112 divided by 1,240 is 9.0%.
- Date coverage. Check that both files cover seven full days, and that neither includes today's partial figures, which usually look like a collapse.
- Number format. Thousands separators and currency symbols can be misread. If sales look off by a factor of 1,000, look here first.
If you do the arithmetic in a spreadsheet, the same weighted rule applies: sum units, sum sessions, divide once. The tutorial on using ChatGPT with Excel covers building the sheet if you would rather have the model write the formulas than do the sums itself.
Reading the three numbers that matter
Sessions measure visits to your product pages (Amazon's glossary counts a customer's visits within a 24-hour period as one session), so they tell you about demand and visibility: search ranking, ads, seasonality, a promotion. A drop in sessions with a steady unit session percentage means fewer people arrived, not that the page got worse. Unit session percentage measures how many sessions turn into units, so it tells you about the page and the offer together: price, images, reviews, stock and the Buy Box. Buy Box percentage (check your report's column name) shows how much of the time your offer was the one a buyer could add to the cart straight away. If it falls, sales fall even if everything else stays the same.
That gives a small decision table you can paste into the chatbot as a rule:
| Sessions | Unit session % | Buy Box % | First thing to check |
|---|---|---|---|
| Down | Steady | Steady | Search visibility, ads, season |
| Steady | Down | Steady | Price, reviews, images, recent listing edits |
| Steady | Down | Down | Competitor price, stock, seller metrics |
| Up | Down | Steady | New traffic that doesn't fit the product (broad ads) |
| Up | Up | Steady | Whatever you changed last week, keep it |
A second week: the traffic that didn't buy
Take the roof sealant in the walk-through. Its sessions rose from 640 to 860 (+34%) while units went from 38 to 41 (+8%) and the percentage fell from 5.9% to 4.8%. The Buy Box also slipped from 96% to 88%. A tempting conclusion is that the listing is worse. The model's list of questions should start elsewhere: did a broader ad campaign start sending visitors who are not looking for sealant? (A look at the search term report would show it.) Did the price rise? Did the Buy Box slip because a second seller appeared? Extra sessions with a falling percentage is the classic sign of traffic that doesn't fit, and the fix is in the advertising, not the page. The review's job is to turn that pattern into the right question before you touch the listing.
Using the by-date report for the shape of the week
The by-child-item report tells you which product moved. The by-date report tells you when. Paste the seven daily rows for each week into a second, shorter prompt:
Here are daily sessions, units and sales for two weeks. Tell me: (1) which weekday pattern is normal for my account, (2) any day that breaks the pattern and by how much, (3) whether the drop or rise happened on one day or built gradually. Do not suggest causes; list what I should check for each odd day.
In the walk-through, the by-date rows (illustrative) show units of 38, 35, 33, 36, 31, 24 and 25 across Monday to Sunday this week, against 32, 36, 34, 37, 35, 36 and 36 last week. The model should notice that the first four days are normal and the last three weaken, with the drop starting on Friday. That timing matches the Buy Box slipping late in the week, and it narrows the search: look at what changed in the offer on Thursday or Friday, not at anything the week began with. A gradual slide would point elsewhere, towards ranking, reviews or a competitor's gradual price cuts.
Which products deserve the attention
With dozens of products, a weekly review that discusses every row is unreadable. Add a filter before the analysis: keep products that make up the top 80% of sales over the last four weeks, plus anything whose sales moved by more than 25% either way. In the walk-through that would keep all five, but a seller with forty products might keep eight. Ask the chatbot to apply the rule and name what it dropped, so you can see nothing important was excluded. A line such as "Excluded: 31 products, 6% of sales, none moved more than 25%" is the sort of statement you can check in a minute.
A four-week view for trends
Once a month, ask for a longer comparison. Keep the weekly files and paste four weeks of totals by product, then ask for the trend in each of the three metrics. Four points on a line show direction in a way two do not. For example, if the hex bolts' unit session percentage reads 10.4%, 10.0%, 9.6% and 9.0% across four weeks, the slide is steady rather than sudden, and a steady slide often means a competitor improving, reviews ageing or a price creeping out of line, not a one-off event. Ask the model to separate steady drifts from jumps, and to name the products in each group. Then check the three biggest drifts by hand against reviews and competitor prices.
Writing the weekly note the chatbot can learn from
Here is a filled-in note, in the form that makes the next review quicker (illustrative):
Week ending 15 March
What moved: sales -$447 (-10%). Sessions +6%. Unit session % 8.4 to 7.2.
Biggest losses: hex bolts -$342 (sessions -5%, % 10.0 to 9.0); staples -$180 (Buy Box 92 to 41).
Checked: staples offer, second seller at 12% lower, my stock low. Hex bolts: price unchanged, two new 3-star reviews on Thursday.
Decided: restock staples, hold price one week. Read the new reviews on the bolts, check for a pattern.
Next Monday: look at staples Buy Box first; bolts % against 9.0.
When the next review starts, paste the previous note above the new data and add one line to the prompt: "Say whether my last note's checks explain this week's movement." The model then reports on progress, not just on numbers, and you close loops you would otherwise forget.
The thirty-minute Monday routine
- 5 minutes: download both reports for the last full week and the one before. Name the files with the week-ending date.
- 2 minutes: open them, check the header names, the date ranges and the row count.
- 5 minutes: run the prompt. Read the totals first.
- 5 minutes: run the sanity checks. If any fails, fix the data and run again.
- 10 minutes: check the top three questions inside Seller Central: the offer and Buy Box for each flagged product, stock levels, price against competitors, recent edits.
- 3 minutes: write the weekly note: what moved, what you checked, what you will change, and what you will look at next Monday.
The note is the part people skip, and the part that makes the routine compound. After a quarter you hold twelve short notes that show which changes helped. Keep them in one document, newest on top, and paste the last four into the chatbot occasionally to ask what patterns repeat.
Turning a finding into an action
For the staples, the owner checks the offer page and finds a second seller at a price 12% lower, and the owner's own stock running low. Two actions follow: restock before the next sale, and decide whether to match the price or hold, based on margin. The chatbot can model that decision if you give it the numbers. At a $15 price, if matching the competitor costs $1.80 a unit and the margin is $5.20, a 12-unit week at the lower price earns $40.80 against 9 units at the higher price earning $46.80, so matching only pays if it lifts sales to about 14 units a week (46.80 divided by 3.40), and 12 is not enough. Keep decision maths as simple as that and check it by hand. The restock planning tutorial picks up where the stock question begins.
When the chatbot explained a drop that was only a delay
A realistic failure, framed as an illustration: an owner pastes a report that includes Sunday's partial figures. Units look 30% lower than the week before, and the chatbot produces a confident paragraph about a conversion collapse and suggests dropping prices by 10%. Nothing was wrong. The latest day was incomplete, because it had not finished filling in when the file was downloaded, and the owner nearly cut prices on the basis of an artefact.
Two habits prevent it. First, end every comparison on a full day that the report has finished, and when in doubt drop the most recent day from both weeks. Second, add one line to the prompt: "If any day appears incomplete or lower than the rest for no reason, flag it before analysing." The model does not know your data is partial unless the numbers look odd to it, and even then it may not say so.
Keeping the files tidy and the data private
Save each pair of CSVs in a folder named by week-ending date, and never overwrite the old ones, because your four-week comparisons depend on the files you kept. Business Reports hold figures by product, not buyers' names or addresses, so they are low risk to paste, but they are still commercially sensitive. Before using a chatbot with them, check your plan's data settings, delete columns you do not use such as cost notes you added yourself, and keep order-level downloads, which can contain customer details, out of this routine altogether.
Making the review part of a bigger picture
Business Reports show what happened to demand on Amazon. They don't show your margins, your ads or your returns. Once the weekly routine works, add one more input at a time, and only when the previous one is trusted: the advertising report, then the returns report. The question of what the page itself does for conversion is covered in the tutorial on A+ Content with AI and the checks before you publish, and the unit session percentage is the number to compare before and after.
Finally, fix the definitions once. Write down what "a good week" means for your account, a number for each of the three metrics by product, based on your last eight weeks, and give those targets to the chatbot at the top of the prompt. A model told that the staples normally convert at 6% to 8% will notice 2.9% without being asked, and a model told nothing will treat it as one more number.
Questions sellers ask about reviewing Business Reports with AI
Which Business Reports should I download each week?
Two are enough to start: the sales and traffic report by date for the whole account, and the detail page sales and traffic report by child item (ASIN) for each product. Use the same date ranges every week so the comparison is fair, and download them as CSV files.
Is it safe to upload Business Reports to a chatbot?
These reports hold your sales and traffic figures, not buyers' personal details, so the main concern is commercial confidentiality. Check your chatbot plan's data settings, remove columns you do not need, and never add order reports that contain buyer names or addresses.
Why do the chatbot's totals differ from Seller Central?
Usually because it averaged percentages instead of weighting them, dropped a row, or read a thousands separator as a decimal. Recalculate totals in a spreadsheet and compare them with the report's own totals before trusting any analysis built on them.
How much data should I paste?
Two weeks at ASIN level for a catalogue of up to a few dozen products is manageable. For more, upload the CSV file rather than pasting, or filter to your top products by sales first. Always include the previous week alongside the latest so the model compares like with like.
Does a low unit session percentage mean a bad listing?
Not by itself. It can reflect price, the Buy Box, stock, reviews, images or the traffic source. A change in the percentage after a change in the Buy Box percentage points at the offer, not the page. Check the offer and the stock before you rewrite anything.
Further reads
- Amazon A-to-z Guarantee Claims: Reply With AI-Drafted Evidence — Handle the claims that dent your seller metrics.
- Amazon IPI Score: Find What Drags It Down With an AI Review — Check inventory health when stock drives the Buy Box.
- Amazon FBA Reimbursement: Spot Lost and Damaged Stock Using AI — Check whether lost stock explains a sales dip.
- Use AI to Sell Products Online: One First Job and a 30-Day Test — Choose a first AI job for an online seller.
- Best AI for Ecommerce Operations: Orders, Stock, Returns, Suppliers — See the wider set of store operations AI can take.
- Amazon FBA vs FBM Cost: Model Your Own Products With AI — Check margins behind the sales figures.
- Amazon Buyer-Seller Messaging: AI Replies Within the Rules — A draft-then-screen routine for Amazon buyer messages: what AI may write, what Amazon blocks, and a rules checklist to run on every reply.
- Amazon Review Analysis With AI: Find What Buyers Want — Use Voice of the Customer or hand-copied public reviews, have AI group the themes, then turn them into bullet rewrites and product fixes.
- ChatGPT for Startup Analytics: Read Your Metrics Without an Analyst — A founder's Monday routine: export the numbers, strip customer data, have ChatGPT explain what moved, and re-check every figure in a spreadsheet.
- AI Tools and AI Development: The Complete 2026 Guide — the AI hub, including every tutorial in the AI-for-business series.
Sources: Amazon's Business Reports glossary (as republished by a reporting partner), published seller guides and Seller Central forum threads on Business Reports, checked November 2026; column names and report names change, so check the headers in your own CSV downloads.