Retail Link ran the Walmart supplier world for over twenty years. It was clunky, slow, and you probably needed a dedicated analyst just to pull a decent report. But everyone built their processes around it. Buyer meetings, line reviews, QBRs — all grounded in Retail Link data.
Then Walmart killed it and replaced it with Scintilla.
If you're a supplier still adjusting to Scintilla — or if you've never known anything else — this guide breaks down what actually changed, what the data looks like, and how to stop leaving insight on the table.
What Was Retail Link?
Retail Link was Walmart's proprietary supplier portal. Point-of-sale data at the store and item level. Inventory across stores and DCs. Forecast data from replenishment systems. Scorecard metrics — instock, OTIF, sales comparisons.
The interface felt like it was built in the early 2000s, because it was. Pulling reports meant clicking through multiple screens, setting date ranges by hand, and exporting to Excel before you could do anything useful. Some suppliers learned the query builder, but the learning curve was brutal and the data limits were strict.
None of that mattered. Retail Link was the source of truth.
Enter Scintilla
Scintilla is the replacement. But it's not an upgraded portal. It's a completely different model.
Retail Link was a query tool. Scintilla is a data delivery platform.
You don't log in and build queries anymore. Scintilla pushes a standard set of datasets to you every week — structured Excel workbooks with consistent columns. Same data, same format, every supplier.
The 14 Core Scintilla Datasets
Every week, you get up to 14 datasets:
| Dataset | What It Contains |
|---|---|
| Item Table | Your full item catalog with UPCs, descriptions, departments, categories |
| Sales & Inventory | Store-level sales, units, on-hand inventory, weeks of supply |
| eComm Sales | Online sales data (walmart.com, pickup, delivery) |
| eComm Inventory | Online fulfillment center inventory positions |
| eComm Instock | Online instock percentages by item |
| Vendor Scorecard | Walmart's official supplier scorecard metrics |
| Tender Analysis | Purchase orders issued by Walmart, quantities, amounts |
| Store MUMD | Mark-Up / Mark-Down data — pricing changes and margin impacts |
| Store Demand Forecast | Walmart's demand forecast for your items at the store level |
| Order Forecast | Expected upcoming orders from Walmart's replenishment system |
| DC Metrics | Distribution center inventory, receipts, and shipment data |
| Future Valid Item Store | Which items are authorized at which stores going forward |
| Modular Plan Metrics | Planogram and modular data — where your items sit on the shelf |
Each file follows a naming convention: {supplierID}_{supplierName}_{datatype}_weekly_{weekcode}.xlsx
For example: 104874_FOREVER GREEN_salesandinventory_weekly_021526.xlsx
What Changed From Retail Link
1. Data Delivery vs. Data Pull
The biggest change. You don't build queries anymore. Data shows up in a fixed format on a fixed schedule. No more wrestling with query builders — but you also can't do quick ad-hoc lookups for a single store or a single day. That flexibility is gone.
2. Weekly Granularity
Scintilla data is weekly, aligned to Walmart's fiscal calendar. Retail Link supported daily queries. If you relied on daily store-level data for promotions or rapid response, that's a real loss.
3. Standardized Columns
Every supplier gets the same column structure within each dataset. This matters more than it sounds. In Retail Link, the fields you got depended on which report you ran and how you configured it. With Scintilla, column names are consistent — all_links_item_nbr, store_number, pos_sales_dollars, on_hand_quantity.
But watch out: column names use underscores and abbreviations, and they don't always match what you'd expect. Always verify the actual column headers in your files before building any automation. This is a constant source of bugs.
4. Fiscal Week Alignment
All Scintilla data is organized by Walmart's fiscal week. The fiscal year starts in late January or early February, so Week 1 is typically the first full week of February. Year-over-year comparisons need to align fiscal weeks, not calendar weeks. Get this wrong and your numbers won't make sense.
5. No Built-In Visualization
Retail Link had basic charts and dashboards. Scintilla gives you raw Excel files. That's it. You want charts, trends, or dashboards? Build them yourself or find a platform that does it.
How to Adapt Your Process
Build a Weekly Data Pipeline
Scintilla runs on a fixed weekly schedule. Your process should too:
- Sunday/Monday: New data drops. Download your 14 datasets.
- Monday/Tuesday: Load the data into your analysis tool — Excel, a database, an analytics platform.
- Tuesday/Wednesday: Review key metrics. Sales trends, instock changes, ordering gaps, scorecard movements.
- Thursday/Friday: Act on findings. Contact your buyer about gaps, adjust forecasts, prep for next week.
Track Week-Over-Week Trends
A single week's numbers are noisy and mostly useless on their own. Four-week, 13-week, and 52-week trends tell you whether your business is actually growing, shrinking, or flat.
Track these:
- Sales dollars and units (store + eComm separately)
- Instock percentage (both store and online)
- DC ordering volume (are DCs ordering consistently?)
- AUR (Average Unit Retail) (pricing shifts ripple through everything)
Compare TY vs. LY
Scintilla's scorecard data includes This Year and Last Year comparisons for most metrics. Use them. A 15% sales dip looks alarming until you see the same dip happened last year. Seasonal patterns explain a lot — don't panic until you've checked LY.
Cross-Reference Datasets
No single file tells the full story. The real value comes from combining them:
- Sales & Inventory + DC Metrics — Are stores running low because the DC isn't shipping?
- Order Forecast + Tender Analysis — Is Walmart ordering what the forecast predicted?
- Future Valid + Demand Forecast — Are your authorized stores aligned with forecasted demand?
- Modular + Sales — Are shelf positions translating to actual sales?
Automate What You Can
If you're still manually opening Excel files, copying into a master spreadsheet, and building pivot tables every Monday — you're burning hours on work that should take minutes. Scintilla's structured format makes it straightforward to automate. Python scripts, a database, a purpose-built platform — pick one. The time savings compound every single week.
The Opportunity in the Transition
Most suppliers are still figuring this out. They download the files, glance at the scorecard, and move on.
The ones who win treat Scintilla data as a strategic asset. They load it consistently, track trends over time, combine datasets, and walk into buyer meetings with specific, data-backed recommendations. Thirteen datasets covering sales, inventory, supply chain, forecasting, and store-level authorization — the data is there. The question is whether you're actually using it.