Compare sales last year to this year, with AI reporting

To compare sales last year to this year, line up the same weeks (or the same month, with the same days of the week) and compare net sales, gross margin, and units by department. In a POS with a built-in AI assistant, you can ask for that comparison in a plain sentence and get the answer in seconds, instead of building it in a report writer or exporting to a spreadsheet. The rest of this guide covers how to line the periods up fairly, how to read what the comparison tells you in a seasonal store, and which reports supply store owners ask for most.

Why same-period comparisons are the honest read for a seasonal store

A garden center's April against its March means nothing, because March is seed packets and pruning shears while April is the start of the year's busiest weeks. April against last April is the comparison that tells you something, and the trade has a name for it: comparable sales, meaning the same store, open in both periods, measured against itself. If you run more than one location, comparable sales only counts the stores that were open in both periods, so a newly opened second location doesn't make the whole company look like it grew when the original store shrank.

The trap in same-period comparisons is calendar shift. Easter can land in March one year and April the next, and if your store sells seed, fertilizer, and garden supplies, that holiday can move a noticeable chunk of spring revenue between months. A rainy Saturday in the middle of peak season can cost a garden center a week of normal traffic, and some years a month has five Saturdays while the same month last year had four, which matters in a business where Saturday is the biggest day of the week. The fix is to compare the same weekdays, not the same dates: take the four weeks ending on a Saturday this year and compare them against the matching four weeks ending on a Saturday last year. Most POS reporting lets you pick exact start and end dates, and when you ask an AI assistant for the comparison, say the dates out loud so it lines the weeks up the way you mean.

A worked example: fall stain season at a paint store

Here's an illustrative example with small, realistic numbers. Say you run a paint store and you want to know how exterior stain did this fall against last fall, comparing September and October both years:

The averages are the first thing to check, and the math is worth doing by hand once so you trust it: $18,450 divided by 410 gallons is $45.00 a gallon last year, and $17,484 divided by 372 gallons is $47.00 this year. Now the picture gets interesting. Units are down 9.3% (38 fewer gallons out of 410), but dollars are only down 5.2% ($966 out of $18,450), because you're collecting two dollars more per gallon. Fewer gallons at a higher price is a different story than fewer gallons at the same price, and it points you toward the follow-up questions: did you take a vendor price increase and pass it through, did a wet September push deck projects into next spring, or did a contractor account that bought stain by the five-gallon pail last fall take their business somewhere else? The comparison doesn't answer those questions by itself, but it tells you which ones to ask, and that's most of the job. If margin is part of what you're reading here, the gross margin formula guide walks through that math the same way.

A second quick one, also illustrative: a garden center sells 2,400 annual flats last April at $4.50 each for $10,800, and this April sells 2,050 flats for $9,225. That's 350 fewer flats, down 14.6%, at the same price, so price explains none of it. Before concluding that demand fell off, check whether Easter landed in March this year and pulled some of that traffic forward, and whether the last Saturday of April got rained out. Same-period comparisons are the honest read, but they're the starting point of the conversation, not the end of it.

The reports a supply store asks for

Most of the reporting an independent supply store wants comes down to a handful of questions asked over and over, and it's worth listing them because they're a good test of any POS you're evaluating:

A note on the salesperson report: it only works if the clerk is recorded on each ticket, so if that's a report you care about, make sure the system captures it at checkout and confirm the report exists in a demo rather than assuming it does. In some systems sales-by-clerk is a built-in report, and in others it isn't on the list at all. That last point applies broadly, because on many older systems, any report that isn't on the built-in list means a custom report, and custom reports mean Crystal Reports or SQL knowledge, which in practice means calling a consultant and waiting. That's the gap AI reporting closes: the ad hoc question you think of on a Tuesday ("how did sundries do against last year, but only the pro brands?") shouldn't cost you a consulting invoice.

Asking for these reports in plain English

When a POS has an AI assistant, the difference between a useful answer and a muddled one is how specific the question is, and the trick is to name three things every time: the period, the measure, and the grouping. Compare these two asks:

• Weak: "How are sales compared to last year?"
• Strong: "Compare net sales and gross margin by department for the four weeks ending last Saturday against the same four weeks last year."

The weak version leaves the assistant to guess which days you mean, whether you want dollars or units, and whether you want one big number or a breakdown, and you'll spend the time you saved arguing with the answer. The strong version reads like what you'd tell a sharp office manager, and it gets you a table you can act on. A few more prompts in that same shape that supply stores lean on:

• "Which customers who bought from me last spring haven't bought anything this spring?"
• "Show net sales by vendor for March this year versus March last year, sorted by the biggest dollar drop."
• "List everything that sold below cost this month, with the margin on each ticket."

Two habits make this work well. First, the first time you ask a new question, check the answer against a report you already trust, like a department sales summary you know is right, because once you've confirmed the assistant reads your data the way you expect, you can stop double-checking every ask. Second, save the questions that work, because a good prompt asked every Monday morning becomes a report you no longer have to build at all. If you want a wider set of numbers worth watching week to week, the retail KPIs guide for supply stores is a good companion.

How forecasting weighs last year against the last few weeks

The same this-year-versus-last-year logic shows up again when it's time to order, and it's worth understanding the method because more POS systems now build it into purchase order suggestions. The simplest version is a year-over-year growth factor, and an illustrative example makes it clear. Say you're looking at a fast-moving item: over the last four weeks this year you sold 120 gallons of it, and over the same four weeks last year you sold 100. The growth factor is 120 divided by 100, or 1.2, meaning you're running 20% ahead of last year on that item. Now look at what sold in the next four weeks of last year, say 90 gallons, and multiply by the factor: 90 times 1.2 suggests you'll want about 108 gallons for the coming four weeks. Last year supplies the seasonality, the recent weeks supply the trend, and the factor stitches them together.

Two limitations are worth knowing before you trust any suggestion like this. The first is that the method needs history in all three windows, so a brand-new item with no year behind it gets skipped until it has one, and you'll be setting its reorder point by hand in the meantime. The second is that the factor carries forward whatever happened in the last few weeks, including the flukes: if one big job or one freak warm weekend drove a sharp three-week swing, the factor treats that spike like a trend and inflates the suggestion, so review the numbers before the purchase order goes out rather than after the truck day. For the items without clean history, the reorder point and safety stock guide covers the formula-based approach that pairs with this.

A ten-minute daily habit

Comparisons against last year are the monthly and seasonal read, but the daily read is where small problems get caught while they're still small. A good morning habit takes about ten minutes with coffee before the doors open: read a summary of yesterday's activity, then scan for three things. Look for anything that sold below cost, because that's usually a pricing error or a cost increase that never made it into the sell price. Look for margin exceptions, tickets where the margin is far off the department norm, which often means the wrong item got picked or the wrong price tier got applied. And look for quantity errors, the ticket that shows 400 pounds of grass seed where 40 was meant, because units of measure mistakes quietly wreck both your inventory counts and your cost data. Catching one of these the next morning is a two-minute fix, while catching it at the inventory count in January is a shrink problem and a bad mood. The habit pairs well with a clean end-of-day closeout, so the money side of yesterday is settled before you read the margin side.

How Rundoo handles it

Rundoo is the AI-first POS for independent supply stores, and Dooey, the AI built into every Rundoo screen, reads your store's own sales, inventory, and charge accounts and answers plain-English questions in seconds. Rundoo comes with a library of built-in reports and dashboards for the standard asks, and anything not on the list you can ask for in plain English instead of opening a report writer. The example asks on the product page show the range: "Pull yesterday's sales in seconds," "Which of my best customers have gone quiet?," "Where is cash tied up in dead stock?," "Compare this item's cost across my vendors," "Flag anything selling below cost," and "What margins look off this week?" One owner asked Dooey which of his top spenders in 2022 were no longer buying from him in 2025 and got a list of 20 high-value accounts in seconds, which is the "who stopped buying?" report from the table above, minus the report writer. One paint dealer told us: "Tony and I have been using Rundoo extensively for our weekly sales meetings, and the AI-generated reports have become one of the most valuable tools in helping us manage the business."

For the daily habit, Rundoo's End of day screen is a daily dashboard with every tender method, deposits, and payments by department, and the Daily AI Summary is a scheduled overnight review of the previous day's activity, sent as a text before the store opens, flagging pricing errors, margin exceptions, and odd quantities. For ordering, the purchase order builder offers Replenishment, Min & Max, and Predictive methods per vendor, and Predictive works the way the growth factor example above describes: it compares the last N weeks this year with the same weeks last year to get a growth factor, applies it to what sold in the next N weeks last year, and skips brand-new items until they have a year of history, with every suggestion reviewed in a grid before anything goes to the vendor.