How do you get more out of Walmart’s Retail Link data?

Walmart’s Retail Link portal is central to any brand with product on Walmart shelves. This is especially true if you sit on or manage a dedicated Walmart team. For that reason, it’s critical to ensure you’re getting the most out of the tool. At Alloy, we have dozens of customers across every part of the consumer goods industry and of every company size selling into Walmart. In this article, we’ll explore the four ways we see successful consumer brands getting the most out of Retail Link.

What is Retail Link?

Retail Link is the portal through which Walmart gives its suppliers access to sales data, reports and forecasts.

You may be a sales or supply chain leader who makes decisions based on the data Walmart provides. Or maybe you’re one of the many sales analysts who spend their days in the tool and have built a career out of turning its raw data into the kinds of insights necessary for business decisions.

In Retail Link you can easily query over 100 metrics and build reports by SKU, by day, by location, and so on. For the most part, it’s great at spitting out a simple report about a lot of the core metrics you need to know to run your business.

Now that you understand precisely what Retail Link is. Next comes understanding where it falls short and how you can work around this.

Where does Retail Link fall short?

What it’s not is a stand-alone reporting tool. If you’re pulling reports out of Retail Link, you’re likely analyzing them in Excel (or maybe in Alloy.ai if you’re a customer of ours). No one who spends any significant time in Retail Link would ever call it intuitive, either. It can be a finicky tool, with a lot of best practices and workarounds left to the institutional knowledge of experienced individuals — many of whom have spent countless hours learning Retail Link the hard way.

Why is Excel not the best tool for analyzing your Retail Link data?

When most of Alloy’s customers come to us, they’re analyzing Retail Link data in Excel. Analyzing Walmart Retail Link data in Excel is unnecessarily tedious and, as we’ll touch on soon, highly inefficient. Because of some limitations of Retail Link, it doesn’t give you a complete view of the metrics you’re running your business and making inventory, planning and supply chain decisions on.

Our customers seeing the most success with Retail Link have followed the four-step trajectory below.

Four tips to get the most out of your Retail Link data analysis

#1: Automate routine tasks and Retail Link best practices

As Alison Jones, VP of Operations at Edgewell said in a recent webinar: “There’s no value in you pulling the data. The value is in what you’re going to do with it.”

Walmart suppliers working in Retail Link spend massive amounts of time — we estimate up to 20% of their workweek — doing routine, mundane tasks that they could have accomplished in seconds or minutes through automation.

Alison Jones, VP of Operations at Edgewell

Here’s how automating retail link data-pulling and analysis helps you improve efficiency:

1.1: Unlock time savings

Forget having to log in, run a report manually and wait thirty minutes to an hour to have your data ready to analyze in Excel. Automate that part of it, so that the freshest data is always available when you walk in at 8 AM.

Once you’ve pulled the data, you can get onto the analysis part. Face it, Retail Link isn’t intuitive, and the core analysis you need to understand the health of your business requires a lot of analyst time.

For example, let’s say you want to understand the performance of a product and share that with your Walmart buyer. Perhaps you are only in 50% of stores and want to make the case that you should be in additional locations.

Calculating something as simple as weeks of supply requires you to have data about locations, sales and inventory, all of which live in different places within Retail Link. All of these data points have to be manually compiled by an analyst with expertise in Retail Link. This kind of labor intensive ad-hoc analysis can take hours in Excel.

This sort of analysis takes two clicks in Alloy.ai. See how for yourself.

1.2: Get rid of the “weirdness” of Retail Link

Retail Link is a great portal in many ways, but it can also be finicky. Even now, as Retail Link turns 31 years old, numerous user groups are discussing how to find this metric or how to work around that hiccup in the tool.

One well-known example is how Retail Link reports Walmart.com ecommerce data. When you run a query on sales you have three different options: POS including dot-com, POS for all stores, or dot-com only. Imagine the surprise of the new analyst who discovers that the “POS including dot-com” report will spit out a different number than the sum of “dot-com only” and “POS for all stores.” If you choose the wrong one, you’ll be making decisions based on the wrong numbers.

After spending years working in Retail Link across dozens of Walmart customers, the list of similar “quirks” would be too long to list here. The value of automating these into a tool rather than relying on institutional knowledge can’t be overstated.

1.3: Avoid being “single-threaded” when it comes to retail link data analysis

Let’s say you want to look at how our products performed last week at Walmart, but — whoops! — your analyst is on vacation this week. I guess you need to wait a week to get your analysis. When you’re reliant on people to perform rote tasks, your Sales organization can’t be resilient or agile.

And beyond that, think about the opportunity cost of not being able to have up-to-date metrics at your fingertips instantly any time you want them. It’s clear that the time constraints of working in Retail Link and Excel are causing you to operate in the dark.

The automation stage of analytics maturity will truly unlock the power of your analysts — allowing them to focus on surfacing the insights you need to run your business. But the impact will go far beyond just your analysts. Ending the “single-threaded” approach to data also will expand and democratize access and insights to the entire organization. Retail Link itself is likely too technical for users without some degree of data expertise — but the automation approach allows self-service access to reports for executives and everyone across the organization.

#2: Get the right visualizations with the latest data

Beautiful charts and graphs are nice to look at, but how you visualize your data — and the frequency with which those visualizations are updated is far more critical than that.

Data visualization in Alloy

When you run a report in Retail Link, it will spit out a text file or CSV with columns. Retail Link alone won’t show you a graph or a trend.

The big problem with creating data visualizations in Excel is that those visualizations are static. They need to be manually updated, and with the pace of business today, they’ll likely be out of date by the time they work their way through your organization. As a result, Sales leaders, Supply Chain leaders and Executives will be making important decisions based on outdated data.

Because Alloy’s data platform pulls data from every retailer portal automatically, you get full real-time visibility into your demand and inventory positions from shelf to warehouse — all alongside predictive simulations on what to expect in the near future. This is necessary for brands to act quickly to changing conditions, and so they can make the right decisions to reduce stockouts and minimize excess inventory costs.

#3: Find insights by drilling into the right metrics

We save customers about 20% of the time they’d normally spend pulling and visualizing data, so they can spend their time here — finding insights and doing something about them.

The advanced retail metrics that help drive your business

We see that our category-leading customers are keeping a close eye on some of the following key metrics and are using them to make inventory and replenishment decisions daily:

All of these retail metrics require hours of manual work to find if you’re working in Retail Link and Excel. If you’re looking for insights in Retail Link data using Excel, think about how long it would take for you to find out the weeks of supply in a specific distribution center right now.

You may have a few of the pieces at your disposal, but it will take you a while to find the answer. (Again, this answer is available with a couple of clicks in Alloy.ai).

Let’s deep-dive into just a few of these metrics:

Forecast accuracy in Retail Link

In terms of forecasts, all you get in Retail Link is current-state and forward-looking for the next 13 weeks. Retail Link doesn’t keep historical data about changes in the forecasts, so if you want to know how accurate your forecast was over time, there isn’t a way to do it. In Alloy we store all of your forecasts, so you can do backward-looking analysis. You’re finally able to get forecast versioning history to easily see the delta between older forecasts and newer ones. This helps give you a better sense of accuracy and important changes.

A single change in Walmart’s forecast can be both business-critical and easy to miss. Imagine you have 100 SKUs and one shifts 30% up or down week-to-week. If you’re working in Excel you may never catch that change — but you need to be alerted when Walmart changes your forecast that drastically, so you can understand the underlying causes and make decisions about your available inventory.

OTIF fines

Disputing and preventing OTIF fines is also critical. These fines can quickly add up to millions per year for large companies. All you get out of Retail Link is your OTIF score, how much was received and when. If you want to dispute these charges, you need to figure out who was at fault. That will require deeper analysis that you’ll be hard-pressed to do in Excel — especially at scale. How do you find out if the truck made it to Walmart on time, but was unloaded a day late? Our customers easily combine Walmart’s Retail Link data with their ERP data to understand where their shipments live (or lived) at all times — letting them successfully dispute these fines.

Alloy.ai customers seeing real results

Valvoline

Reduced OTIF fines by $150K by using retail data in Alloy.ai to dispute fines.

Ferrero

Increased total sales by $2.7M with better replenishment and collaborative inventory management in Alloy.ai

“We have had a lot of positive changes from Walmart, especially now that we have Alloy.ai. This is the main reason they are letting us run their orders now.”

-Valvoline analyst

#4: Focus only on the areas where you need to take action

With all the data-points you have at your fingertips in Retail Link, it’s easy to get lost in the noise. One of the most important things you need if you’re selling through Walmart (or any retailer for that matter) is to utilize alerts and exception-based reports.

Managing by exception helps you quickly spot problem areas that need your attention. Say your OOS rate is normally 1% but hits 7% on a certain SKU. In Alloy.ai, you can configure a trigger to email you with a report of high OOS rates so you’d know right away where to focus your attention.

Doing this in Excel will mean combing through hundreds (or thousands) of good SKUs or locations to find a few bad apples — not a great use of anyone’s time.

Check out this video below to see how easy it is to build out exception reports in Alloy.ai — whether it’s your in-stock rate, phantom inventory, excess inventory, performance vs. plan or anything else you need to keep a close eye on:

See it in action: Out of stock detection and resolution in Alloy.ai

What if you’re selling through multiple retailers?

All of the complexity we’ve talked about above applies if you’re on a dedicated Walmart team, only selling into a single retailer.

Imagine the explosion of complexity once you throw dozens or even hundreds of retailers into the mix. If you’re working directly in each siloed portal you’re wasting massive amounts of analyst time that could be spent looking for the insights that will drive more sales.

Being able to compare performance metrics across retailers without needing to manually build out dedicated reports is a game-changer for many brands.

Our customers have found that analyzing data across retailers provides insights they couldn’t get otherwise. Perhaps you’re seeing success with a SKU at Walmart, but not at Target. What’s the root cause? Is it demographic? Is Walmart doing something to promote the product that you can apply to other retailers to boost sales there?

These are the kinds of questions our customers are asking of their data every day, and they lead to insights that are incredibly difficult to arrive at if you’re stuck in portals and spreadsheets.

To learn more about how to uncover deeper insights with Retail Analytics read our new Retail Analytics buyer’s guide.

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