You can’t afford to rely on gut decisions if you work in retail or merchandising. You need access to granular data to make informed decisions about stock-keeping unit (SKU) rationalization.
Fortunately, AI-Powered Analytics provides those detailed insights, helping your team quickly identify low-performing SKUs and take action. In this article, we’ll break down:
What a solid SKU rationalization process looks like, and why you urgently need one
How to go from best-guessing to making data-driven decisions about SKU optimization
The six steps to building a reliable, resilient SKU rationalization process—no matter what the market does next
Table of contents:
For most retailers, there’s an ongoing tension between operations and sales.
Sales wants to add more SKUs to offer a greater variety of products, compete more effectively, and capture market share. Operations wants to sell existing stock, keep SKUs lean to reduce storage costs, and avoid obsolescence.
Both are right—and good SKU rationalization strikes an ideal balance between the two positions.
SKU rationalization is the process of evaluating the SKUs in your product portfolio and deciding which ones to keep. The goal is to optimize your product offerings by eliminating underperforming or redundant product lines.
SKU rationalization is both an art and a science. A thoughtful SKU rationalization process combines row-level data analysis with the unique retail expertise and insight of an experienced team. When you strike the right balance between product variety and operational efficiency, you yield a stronger brand, a more aligned organization, and higher profits.
Now’s not the time to make intuition-driven SKU decisions. As prices go up, the pressure is on to stay lean—and carrying inventory that doesn’t move is a liability. A sound SKU rationalization process makes inventory optimization a snap.
With detailed SKU data analytics at your fingertips, your team can:
If you’re methodically reviewing what’s selling well and what’s not, you can proactively eliminate underperformers and duplicates to avoid overstocking. This allows you to spend more of your budget on high-demand items.
When you eliminate low-demand items, you free up valuable warehouse space, reduce the need for excess stock, and minimize the associated carrying costs (storage, insurance, handling expenses, and so on).
Suppose you’re not wasting time and space on low-demand products. In that case, you prioritize more profitable stock and have the SKUs flying off the shelf ready.
SKU rationalization isn’t just about cutting dead weight; it’s also about making data-guided decisions when adding new product lines. Using this methodology, you build a reputation for carrying the diverse product mix your customers want while keeping your inventory lean.
Newark-based cabinet manufacturer Fabuwood knows all about the inventory planning demands facing modern retailers. David Samet, the company’s Director of Technology, has seen a shift in how their team makes decisions.
“In the past, [inventory planning] used to be more of a gut feeling. Whereas now, everything is a data request.”
Analyzing data is clearly better than following a hunch. The only problem: Most retail data teams struggle to keep up with the demand for reports. At Fabuwood, that meant people made decisions based on a gut feeling and then found data to support them.
So Samet’s team implemented ThoughtSpot to let their business users get hands-on access to SKU data. And when working with a team member to validate a sales forecast, Samet realized the value of data accessibility.
“I was easily able to drill into each item from month to month—down to the door style and color. Two days after seeing me do it, that same team member came back to me and told me that he was able to do it himself. That’s really the best feedback I can receive.”
SKU rationalization isn’t an easy process to start or maintain. Still, it’s well within reach for companies with access to a modern data stack—you just have to know how to use it. Here’s how to build an SKU rationalization process to eliminate waste and maximize profits:
This boils down to the simple question, “What SKUs are sold in our stores daily?” But finding an answer takes some digging if your data is stuck in siloed warehouses.
Too many retailers fall back on company-wide reports that only give insight into high-level patterns—overall SKU sales per quarter, for instance, instead of per store per day. But this information doesn’t help you make restocking decisions.
Instead, you need a retail analytics tool that shows actual SKU numbers down to product color and size. This is where ThoughtSpot comes in. With our AI-Powered Analytics, your merchants become their own data analysts, surfacing insights about SKU performance at the most granular level.
What you define as “low-performing” depends on your retail strategy. In most cases, this revolves around sales numbers. However, you might opt to keep an SKU with high customer satisfaction ratings but low sales.
Again, you need in-the-weeds data to make these decisions. For instance, a SKU might look like a low performer—but when you dig into per-store data, you see that it sells incredibly well in specific regions or markets. The ability to limitlessly drill down and perform ad-hoc analysis makes it easier for responsible parties to make informed decisions.
This is where the art comes in. Experienced retailers often spot early market trends using their knowledge of consumer behavior and a sixth-sense kind of instinct. They need data to refine their guesswork into a reliable business strategy. Big data analytics in retail can help.
Aside from access to the data, decision-makers also need access to easy-to-understand data visualizations. This empowers teams to share and collaborate around insights—a vital function when proposing changes to SKUs and merchandising.
For instance, Canadian Tire improved sales by 20% by forecasting demand with ThoughtSpot—despite 40% of their stores closing during the pandemic. Check out this video to see exactly how they used data to refine their SKU strategy—including using AI-Powered Analytics to surface the questions they didn’t even know to ask.
At this point, you need to evaluate the inventory holding costs associated with each of your SKUs. Consider issues like:
How much it costs to store
The risk of it becoming obsolescent before sale
The impact on your working capital versus profitability
The cost and speed of the supply chain
Don’t rely on estimates from suppliers here. Instead, use your data analytics tool to either review actual sales data on existing SKUs or analyze the behavior of comparable SKUs if you’re adding a new product to your portfolio.
At this point, consider the SKU holistically. Instead of just reviewing the performance of a plastic cutting board, examine how each variant performs. Does the red board perform better than the blue board? You may need 20% more red boards.
Or maybe you’ll sell more boards if you bundle them into a 3-pack? Look at historical trends to see how similar products have sold for you and spot opportunities for bundling SKUs.
Again, making these decisions requires precise data grouped by SKU and product attribute—the more attributes, the richer your analysis. The richer your analysis, the better you can use your expertise to make SKU decisions.
Once you’ve conducted your analysis and made your decisions, it’s time for the actual rationalization. Even if you’re cutting a product line, there’s still a cost associated with that change—whether it’s the damage to your relationship with the supplier or the loss you incur selling those remaining SKUs. But if you have evidence to support your decisions, you have ample information to justify those costs.
When you communicate the proposed changes to your product portfolio, make sure you explain the why behind them. For example, you may have discovered low-profit or slow-to-move products, a market trend, new customer preferences, or inefficiencies in the SKU supply chain.
SKU rationalization changes often make people nervous. When you communicate with your team, keep these tips in mind:
Anticipate potential concerns or objections and proactively address them in emails and meetings.
Let your data visualizations tell the story, emphasizing the predicted positive outcomes of these changes.
Take a phased approach—staggered changes make for calmer business operations.
Having granular SKU data at your fingertips is a game-changer for rationalizing your product portfolio. Instead of relying on top-level reports—and a bit of guesswork—your team gains instant access to sales and warehouse inventory levels across thousands of SKUs.
You can drill down into the attributes of each SKU and make informed decisions based on a holistic picture of how each SKU performs.
As Iaro Boutorine, previously the Manager of Merchandise Analytics at Canadian Tire, said, "Timely access to information is extremely important so we're not starting to make wrong day-to-day decisions—just because everything is changing so fast. And that's where ThoughtSpot is so, so crucial to me."
Set up a free trial to see how ThoughtSpot can transform your SKU rationalization and inventory planning.