Analyze Seasonal Sales Data from Your Export Files
Retail and ecommerce sales exports contain multiple years of transaction data, but spotting seasonal patterns in thousands of rows is slow work. This analyzer reads your export file and shows you which months drive revenue, how this year compares to last, and when to expect your next peak. Upload your file and see results in seconds—no formulas, no pivot tables.
Drop your file in and ask a question — no account needed.
Upload your spreadsheet.xlsx .xls .csv — free to try, no accountOr start with
What this export contains
| Order Date |
| Order ID |
| Product Name |
| Quantity |
| Unit Price |
| Total Amount |
| Customer ID |
| Payment Status |
| Fulfillment Status |
| Sales Channel |
| Product Category |
| Discount Amount |
Common questions
How do I analyze seasonal sales data across multiple years?
Upload your sales export with an Order Date column and total amount. The analyzer groups transactions by month and year, calculates monthly totals, and charts them so you can see recurring peaks and compare the same month across different years without building formulas.
What file formats work for seasonal sales analysis?
CSV and Excel exports from Shopify, Amazon Seller Central, Square, Stripe, WooCommerce, and most POS systems. The file needs a date column and a revenue or total column. Product name and category columns enable product-level seasonality if present.
Can I compare this year to last year if the current year is incomplete?
Yes. The analyzer shows year-over-year comparison up to the same date in both years, so if today is November 15, it compares January 1 to November 15 for both years. This keeps the comparison fair when the current period is still running.
How does it identify peak sales periods?
It calculates the average monthly revenue across all years in your data, then highlights months that exceed that average by a threshold you set. You see which months consistently outperform and whether peaks are shifting earlier or later over time.
Does it show seasonality for individual products or categories?
If your export includes a Product Name or Category column, you can filter the seasonality view to one product or category. This reveals that winter items peak in October while gift items peak in December, patterns that disappear in a store-wide total.
Try it with your own file
DataMimi reads the file you actually have — merged cells, headers below row one, totals pasted at the bottom — and shows which rows and columns every number came from.
Ask about your fileWhat DataMimi costs
Every plan does everything — slides, written reports, cleaned exports, dashboards. They differ in how much work they cover.
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Free
40 credits a month
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$24 /month
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$59 /month
5,000 credits a month