This blog post is based on a webinar done together with TR Data Strategy, a consulting firm specializing in harnessing data and analytic tools to explore, visualize and calculate relevant statistics. They provide data-driven strategies for managing, growing and transforming businesses. In retail, they specialize in shoring up underperforming categories and optimizing stores.




Have you ever wondered how the biggest retailers and their buying teams scrutinize their vendors? You’re in luck — this blog post will explore a secret method that Walmart uses to examine their vendors and identify inefficiencies.

This method was kept under lock and key so well that, despite working with Walmart, TR Data Strategy was initially unaware of its existence. A retired senior manager began working for one of their clients — a large Walmart vendor — and shared the strategy with them. In fact, the manager’s first project after leaving Walmart was to recreate this evaluation model.

At Walmart, this type of analysis is referred to as the “quadrant analysis” report, or just “quadrant” for short.

What is the Quadrant?

The purpose of the quadrant analysis is to sort product and store-level data into a visualization that lends itself to important insights, providing a number of benefits to the user. The quadrant specializes in allowing you to uncover a variety of hidden issues. These may range from invisible weaknesses that everyone misses to problems that don’t top the priority list but which still eat into margins and leave money on the table. The quadrant gives vendors knowledge so that they can come to a buyer meeting with proactive solutions, improving relationships with buyers and avoiding painful markdowns and concessions.


The quadrant works by cross-tabulating both your stores and your products into categories based on their relative performance: “A” stores and products represent your top 25% performing stores and products (relative to their own category – stores to stores and products to products), “B” represents your middle 50% of stores and products, and “C” represents your bottom 25%.

The purpose of this chart is to distill the data into one singular view so you can get an overview of your entire business at Walmart (or any given retailer, for that matter). It provides an at-a-glance view to show the strengths and weaknesses of product lines and the contribution of store quality to overall results.

How to Read the Quadrant

To help us understand how to interpret the quadrant, let’s go through an example. Here, we will illustrate where to catch issues and how we can take steps to fix the problem. Please note that this example is based off of a real situation, but the numbers have been obfuscated for confidentiality purposes.

This chart tells us a number of things at a glance:

  • One of the first things we can see is that a notable majority (60%) of sales comes from the A products.

  • We can also see that A products still have good movement in B and C stores (18% and 12%), which tells us that A products are particularly strong overall.

  • It is also useful to notice that B products did well in A and B stores, but fell off in C stores (16.5% and 9% versus 4.5%).

  • C products performed poorly in C stores, suggesting that C products are not particularly active overall (only 0.5% of total sales were C products in C stores).

With the information distilled from the quadrant, we can use various company-specific metrics to determine where hidden problems might lie. In this example, we will calculate the in-stock rates of our products, focusing on our biggest seller (A products). For this purpose, we’ll group A products together in the top row and compare this to B and C products together in the middle row. We will also place the company’s average in-stock goal in the final row.

So, what can we infer from this chart? We can see that despite having an issue with in-stock rates, A products still make up the majority of sales for this vendor. This leads us to ask ourselves: what do these percentages mean to this company’s margin? And how much money are they leaving on the table because their products are out of stock?

When we dig deeper and extrapolate these figures into real dollar terms, it’s clear that A products are significantly undercutting the company’s potential revenue due to an 11 point in-stock percentage gap versus the company goal.

TR Data saw this situation and decided to investigate:

“We discovered that the reason A products were out of stock so often was because the stores weren’t ordering far enough in advance. After asking around, we found that A products were out of stock while they were still on the water from China. This was because those in the supply chain were paid bonuses based on money “saved” when inventory was kept low – savings calculated to be only a few thousand dollars per month in interest on inventory.”

Upon leveraging the quadrant analysis to dig into this otherwise healthy business, the findings were quite profound: for a few thousand extra dollars in carrying cost, the client was able to address their recurring out of stock issues for A products and improve sales by roughly $6.1 million.

In doing so, this vendor was able to achieve 20% of their growth target for the entire company without having to create or sell any new products. Best yet, this revelation also extended to the other retailers that the manufacturer worked with, meaning they could apply this lesson to the rest of their customers and recoup even more in previously lost sales.

TR Data was also surprised at the results:

“The business that was used as the basis for this example was an established, strong manufacturer that appeared to be running at peak efficiency. And even so, we estimated they may have been suffering from this hidden problem for the better part of a decade, meaning that they unknowingly lost out on tens of millions of dollars in revenue.”

Now, you may be asking yourself, “how does this happen? How was this not caught earlier?” Quite possibly the biggest reason an analysis like the one above was not conducted earlier is because of the sheer amount of data that needs to be crunched in order to build a model like the quadrant.  

How to Build the Quadrant

Building a quadrant can take quite a lot of effort, but doing an analysis like this without a bonafide analytics platform would be too resource intensive. For instance, the example above required 1.6M lines of data per week – far beyond the limits of tools like Microsoft Excel or Microsoft Access. This is where Askuity comes in.

TR Data Strategy identified six important steps for building your own quadrant analysis. For a time period (usually per quarter or year) and relevant metric (usually units or dollars),

  1. Start with a chart with sales by SKU, by store, by week as one row. Rank or order your stores by dollars and/or units.

  2. Divide your stores into quantiles – the top 25% is A, middle 50% is B, bottom 25% is C.

  3. Repeat step 2, but for your products. Keep in mind units and dollar rankings may be different.

  4. Go back to your data and mark each row (sales, SKU, store, by week) for the quantile store and product the row falls into

  5. For each quadrant, sum all the relevant rows to calculate units and dollars sold for the relevant time period and fill in each block in the matrix. For example, every row with both A products and A stores is summed into the A/A block, and every row with C products at an A store goes into the C/A block. You can also use percentages to make comparison easier.

  6. Repeat this process for every period, retailer and metric you want to analyze.

Other Uses for the Quadrant

The quadrant has a number of other applications worth taking a look at. You can use it to:

  • Calculate other metrics. In-stock rate, average selling price, average units (or dollars) per store per week, etc.

  • Perform geographic analysis to pinpoint where and who your best customers are

  • Come prepared to planner and buyer meetings, impressing them with your knowledge, initiative, and strategies

  • Understand characteristics of your best performers for use in product development

For more ideas about how you can leverage the quadrant to your advantage, check out some tips here:


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The quadrant illustrates the potential power behind using data analytics to look at your business’s status. While it can be complicated to put together, the insights and lessons it has to offer can end up paying off in a big way. In today’s market, understanding your products and their movement is more critical than ever and the quadrant is a tool that can shed light on money you didn’t even know you were losing.

To get a more in-depth tutorial on the quadrant, check out our free webinar, led by Askuity and TR Data Strategy