What uploading POS & Syndicated data unlocks in Confido — and when it's required. Point of sale (POS) data is the fuel that powers a large portion of Confido's forecasting, promotion planning, and performance tracking capabilities. Without it, forecasts are manual and static. With it, Confido can automatically surface insights, flag deviations, power recommendations, and enable salespeople to evaluate promotion ROI against real in-store results.

Uploading POS data enables:

• Automated forecast recommendations — Confido compares your current forecast assumptions (store counts and velocities) against the latest actuals and flags meaningful deviations for your team to review

• Baseline calculation — Confido uses actual in-store scan data to establish and update your baseline store counts and velocities, stripping out promotional lift to show true everyday performance

• Promotion performance reporting — actual lift observed at the register versus what the sales team forecasted

• Base and incremental splitting — when only total units and dollars are available, Confido uses price movement in the data to calculate base vs. promotional volume

• Deduction validation — POS actuals are used to cross-check whether deductions align with actual in-store performance

Understanding the two types of POS data and when to use each one.

Confido supports two types of data uploads, each serving a different part of the forecast and each living in its own dedicated upload section under Sales Actuals.

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What your data file needs to contain, and how flexible the format is.

Regardless of the data source, every upload file must contain at minimum:

More measures = more capability. The full set of measures Confido can use, and what they unlock:

Confido is flexible about date formats. The general rules are:

• Any standard date format works (MM/DD/YYYY, DD/MM/YYYY, YYYY-MM-DD, etc.)

• Two-digit or four-digit year is both fine

• Week ending dates can be Saturday, Sunday, or any day of the week — Confido apportions the data to the correct forecast weeks automatically

• If your data source provides retailer-specific period numbers (e.g., 'Week 10' in a retailer's fiscal calendar rather than an actual date), you will need to translate these to real dates before uploading. Set up a week mapping lookup table in Excel — this is a one-time setup effort that makes every subsequent upload a simple paste-and-go.

UPC is the preferred product identifier because it is standardized across data sources and Confido's product master. Product description names vary too much between syndicated providers, portals, and internal systems to be reliable.

UPC format is flexible:

• 12-digit raw format (no dashes) — most common

• 11-digit format (leading zero dropped) — supported

• Dashes in various positions — supported

When a UPC in your file cannot be matched to a product in Confido's product master, Confido will flag it during the upload. Do not ignore these flags — investigate each one to determine whether it is:

• A discontinued SKU that can be excluded from future data pulls

• A new SKU that needs to be set up in the product master before the upload will succeed

• A data quality issue (e.g., a UPC associated with two different internal item codes — see Section 5.2)

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How to upload a consumption or depletions file in Confido.

In Confido, go to Sales Actuals. You will see two tabs: Consumption and Depletions. Select the appropriate tab for the data you are uploading. Each tab shows all previously uploaded files with the date uploaded, the planning groups included, and the products included. You can download any past upload from this screen.

Before selecting your file, Confido asks one question: does your file already include base and incremental units separately?

Drag and drop your Excel or CSV file into the upload area, or click to browse and select it. Confido reads the header row and displays the columns in your file alongside Confido's field schema.

This is the core of the upload process. You need to tell Confido which column in your file corresponds to which Confido field. The mapping screen has four sections:

Map each retailer or distributor name from your file to the corresponding Confido planning group. If your file uses raw syndicated RMA names (e.g., 'TOTAL TARGET') or banner-level names (e.g., multiple Loblaw banners mapping to one planning group), you can select multiple source names and map them all to the same Confido planning group.

Confido remembers every mapping you create after the first upload. The next time you upload a file from the same data source, all previously mapped planning groups are pre-populated automatically.

Map the date column in your file to the Week Ending field. If your dates include extra formatting noise (e.g., 'WE 4/5/2026' instead of '4/5/2026'), you can clean this up in the upload tool or pre-clean it in Excel before uploading.

Map the UPC or product description column to the Product field. Confido will automatically match UPCs to products in the product master. Any UPCs that cannot be matched are flagged. Any that were successfully matched in a previous upload are pre-populated.

Map your data columns to Confido's metric fields (units, dollars, base units, incremental units, stores selling, etc.). If your file follows the Confido standard template for a major syndicated platform (Nielsen, Circana/IRI, SPINS), the mapping will auto-populate perfectly. Extra columns in your file that don't map to a Confido field can simply be left unmapped — they will be ignored.

After completing the column mappings, Confido validates the data and flags rows with issues. Common issues include:

• Number formatting errors (e.g., units with commas: '1,234' — use 'Fix All Errors' to resolve automatically)

• Date formatting that Confido cannot parse (requires pre-cleaning in Excel)

• Rows with no valid data (e.g., footer rows, copyright lines from syndicated exports — select and delete these in the upload tool)

Use the 'Fix All Errors' button to automatically resolve formatting issues. For rows that cannot be auto-fixed, you can select them individually and delete them, or use 'Delete All Rows With Errors' to remove them all at once. Verify the count of deleted rows makes sense before proceeding.

Once all rows are clean and error-free, click Import. Confido will confirm the upload succeeded and the new file will appear at the top of the upload history list with today's date.

After uploading, verify the data came in correctly by checking the forecast for one or two of the planning groups included in the upload. You should see the actuals rows update with the new data. If something looks wrong, you can delete the upload from the upload history screen — it will be removed from the forecast immediately — and re-upload a corrected file.

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Important choices your team needs to make about how to manage ongoing uploads.

Every time you upload, you have two options:

• Recommended approach: Upload the latest weeks only (incremental): Only upload the newest data since the last upload — typically the latest 4 weeks. This is faster, produces smaller files, and prevents any historical data from being changed if a data source issues a restatement.

• Upload the full history (full reload): Upload the entire dataset from the beginning. This captures any restatements or corrections the data provider has made to historical data, but it means your historical actuals may shift when you reload — which can affect previously reviewed recommendations and forecasts.

A known edge case: some brands have the same UPC associated with multiple internal item codes in their ERP (e.g., a US retail item and an Amazon/e-commerce item sharing the same UPC, or a US item and an international item sharing the same UPC).

When this occurs, Confido cannot automatically determine which internal item to map the POS data to. Left unresolved, it will return one of the two options — potentially the wrong one.

Solutions to investigate with the Confido team:

• If the item descriptions are different between the two items, Confido may be able to use the item description as a secondary identifier to distinguish them

• Adding a lookup column to your Excel preparation template that explicitly maps the UPC to the correct item for each data source (e.g., when uploading US retail data, always map this UPC to the US retail item code)

• Working with the Confido engineering team to add source-specific logic if the issue is widespread

For data sources that do not follow a standard format — retailer portal exports, Canadian distributor reports, broker-provided files — set up an Excel preparation template for each source before your first upload. The template should:

• Map retailer-specific week numbers or period codes to real calendar dates

• Map distributor or retailer item codes to UPCs or product descriptions

• Map banner-level retailer names to Confido planning group names if needed

• Strip out any footer rows, copyright notices, or metadata that would cause upload errors

Once built, the template becomes a simple paste-and-go step before each monthly upload. The one-time setup cost is worth the ongoing time savings and error reduction.

Start with monthly uploads for all data sources. This gives the team time to build comfort with the process and establish quality checks before increasing frequency. As the team becomes more confident and if more real-time insights are needed, individual data sources can be moved to weekly uploads.

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How depletions uploads differ from consumption uploads, and what to expect from pre-configured distributors.

The depletions upload process follows the same steps as consumption uploads with a few key differences.

Most distributor depletion reports are issued monthly — you receive one total number per item per month rather than weekly data. When you upload a monthly depletions file, Confido automatically splits the monthly total evenly across the weeks in that month. This means you will see depletion actuals in weekly buckets in the forecast view, even though you only uploaded monthly data.

Unlike consumption data, depletions data typically does not include a 'stores selling' measure. This is normal. Confido derives store count information for depletions customers from the item-level data itself — the number of stores that ordered in a given period is counted from the underlying store-level depletion lines. You do not need to provide store counts separately for depletions uploads.

For the major US distributors — UNFI and KeHE — Confido has pre-configured upload templates. When you select these distributors in the depletions upload screen, Confido already knows the expected file format from the portal and has all column mappings pre-set. You simply download the depletion report from the UNFI or KeHE portal in the expected format and upload it — no mapping work required.

For other distributors (including Canadian distributors), the first upload requires the same column mapping exercise as a consumption upload. Confido remembers the mapping for all subsequent uploads from the same source.

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Common questions from teams setting up POS data uploads for the first time.