If you’ve ever wrestled with getting historical sales data into a forecasting tool, you know the headaches: messy spreadsheets, mismatched columns, and cryptic errors that don’t tell you what went wrong. This guide is for planners, analysts, and anyone stuck with the not-so-glamorous job of making sure Avercast has the right data for real forecasting accuracy. We’ll skip the vague advice and get into the specifics—what works, what trips people up, and how to avoid wasting your time.
Why Getting Data Into Avercast Matters
Let’s get this out of the way: Avercast is only as good as the data you feed it. If your sales history is incomplete, mapped wrong, or full of holes, your forecasts will be useless. Garbage in, garbage out.
The good news? Avercast is flexible about data formats and sources. The bad news: that flexibility means it’s easy to make mistakes or get lost in a sea of options. The trick is to keep it simple, start with a clean dataset, and map it carefully.
Step 1: Get Your Historical Sales Data Ready
Don’t start uploading files yet. The #1 pitfall is sending Avercast messy or inconsistent data. Here’s how to prep:
- Pick your source: ERP export, sales system, or a glorified Excel file—it doesn’t matter, as long as it matches what’s actually been sold.
- Decide on the time frame: Most folks use at least 24 months to catch trends and seasonality. If you only have 6 months, be realistic about what Avercast can do with it.
- Standardize columns: You’ll need columns for:
- Item Number or SKU
- Location (if you forecast by location)
- Sales Quantity (not revenue—Avercast wants units)
- Date (daily, weekly, or monthly buckets; just be consistent)
- Check for missing data: Run a quick pivot or filter in Excel. Zeroes are fine; blanks or random text aren’t.
- Clean up weird characters and formats: Watch out for commas in numbers, date formats that flip month/day, and extra spaces.
Pro Tip: Don’t try to import every field from your ERP. Start with the basics. You can always add more detail later.
Step 2: Choose the Right Import Method
Avercast supports several ways to bring in data, but not all methods are worth your time:
- Manual File Import (CSV or Excel): Easiest for first-timers and smaller datasets. You upload files through the Avercast interface.
- Database Connection: IT-heavy, but good if you’ve got regular updates or lots of data.
- API Integration: Powerful, but only if you’ve got dev resources and a real need for automation.
What works for most? Manual CSV/XLSX import. It’s fast and lets you catch mapping errors before they become a bigger mess.
Step 3: Format Your Data File
Avercast isn’t super picky, but it does expect a few basics:
- File type: CSV usually works best. XLSX is fine unless you have complex formulas or multiple sheets.
- Column headers: Make them clear. “SKU” or “ItemNumber,” not “Thingy1.”
- Date format: Stick to YYYY-MM-DD if you can. If your system exports as MM/DD/YYYY, double-check that Avercast reads it right.
- No merged cells, formulas, or subtotals: Just raw data.
Sample CSV:
ItemNumber,Location,SalesQty,Date 12345,WarehouseA,10,2023-01-01 12346,WarehouseA,7,2023-01-01 12345,WarehouseB,2,2023-01-01 ...
Step 4: Import the File Into Avercast
Here’s the typical flow for manual import:
- Log in to Avercast.
- Navigate to the data import section: This is usually under “Data Management” or “Administration.” If you don’t see it, you might need extra permissions.
- Select ‘Import Sales Data’ (or similar).
- Upload your CSV/XLSX file.
- Map the columns: Avercast will ask you to match your file’s columns to its required fields.
- Double-check: Your “SalesQty” goes to “Sales Quantity,” your “ItemNumber” to “SKU,” and so on.
- If you use locations, make sure these match exactly what’s set up in Avercast. Typos here mean data gets ignored or duplicated.
- Set the date range: Tell Avercast what period your file covers. If you’re importing a big file, it might take a few minutes.
- Review the preview: Avercast usually shows a few rows so you can sanity-check the mapping. If it looks weird here, stop and fix the file.
Common headaches: - “Date not recognized” errors mean your date format is off. - “Unknown SKU” errors mean your items in the file don’t exist in the master item list in Avercast. You might need to import or update those first. - Silent failures (nothing happens): Usually a mapping mismatch or a permissions issue.
Step 5: Handle Mapping Issues & Data Validation
This is where most first-timers get stuck. Avercast is only as smart as its mapping screen—and it won’t always catch subtle mismatches.
- Double-check that every required field is mapped. If you miss one, Avercast will drop the row or import it wrong.
- Watch for duplicate records. If your file has multiple entries for the same item/location/date, Avercast might sum them, overwrite, or throw an error—depends on your settings.
- Validate totals: After import, run a quick report in Avercast and compare totals (by item, by month) to your source file. If they don’t match, something’s off.
Pro Tip: Don’t assume Avercast will warn you about everything. Sometimes it’ll just skip bad rows without a peep.
Step 6: Check Your Forecasts and Fine-Tune
Once your data’s in, the real test is what the forecasts look like. Don’t expect miracles if your sales history is patchy or inconsistent.
- Spot-check a few SKUs: See if the forecast makes sense given recent trends. If you see wild spikes or flatlines, you probably have data gaps.
- Look for missing items or locations: If something’s missing, check your mapping and item master list.
- Iterate: It’s normal to import, spot an issue, fix your file, and re-import. Don’t try to be perfect on the first go.
What to ignore: Fancy fields like “promotions” or “adjustments” aren’t needed at first. Focus on getting a reliable base forecast; you can add more complexity once you’re confident in the process.
Pro Tips and Real-World Gotchas
- Column name mismatches are the #1 source of pain. Make your headers obvious and double-check them.
- Blank rows or weird symbols will break the import. Don’t trust your eyes—run a quick “Remove Duplicates” and “Trim Spaces” in Excel first.
- Too much data at once can slow things down. If you’ve got more than a few hundred thousand rows, import by year or by major product group.
- Documentation can be vague. If you get stuck, Avercast support is helpful, but be ready to send them your file and screenshots.
- Don’t expect Avercast to “fix” bad data. It won’t. Clean your file before you upload.
Keep It Simple, Iterate, and Don’t Overthink It
Importing and mapping historical sales data into Avercast isn’t rocket science, but it does require patience and attention to detail. Start simple: get your core items and locations in, double-check your mappings, and look at the forecasts before moving on. It’s tempting to fuss over every field, but the fastest way to get value is to keep things straightforward and build up from there. If you hit a wall, take a breath, fix the basics, and try again. That’s how you actually get accurate forecasting—one clean import at a time.