Skus are endlessly fascinating. Not only do they enable some really compelling marketing activities, but they’ve become so ubiquitous to the modern consumer they have even found their way into gamer parlance (Hey, anybody played 076527034974 yet?).
In this series, we first laid out the general concepts of SKUs, then looked at the rationalization of optimal SKUs. In this article, we’ll dig into analyzing SKUs that are considered the “tail,” which are the low margin or negative margin (loss) items.
Analyzing beyond the tail.
When analyzing SKUs, it’s important to gather multiple points of data. After having sorted and Pareto'ed SKU data to find the tail items, it’s a good idea to conduct a deeper analysis before deciding which to keep and which to discontinue. Sales dollar volume alone does not tell the full story. Let’s take a look at a few different scenarios.
Selling at different prices.
Most often, the same SKU is sold at different price points to different customers. The price you sell a specific SKU to a mom-and-pop store will be different than the price for a national Big Box store. Correspondingly, the profit margin will be different. In this example, total volume, dollar sales, and gross margin should be reviewed by SKU, and then add an additional layer of data information by customer. Then, isolate this data to focus on margin by customer. Most likely, the margins for the manufacturer selling to the Big Box stores will be lower than that of the mom-and-pop stores.
Bring in other departments for input on this analysis. Find out from Operations how much overhead absorption is occurring at a per-plant basis for the Big Box stores. Ascertain if that absorption is worth the reduced margins. Talk to Customer Service to see if the mom-and-pop stores require a high degree of interaction, which drives up overall customer cost. In other words, analyze the information.
“A voice of the customer gap analysis is also useful in further calibrating a data story by integrating qualitative feedback among internal and external stakeholders,” says Nicole Lev Ross, Orchid Black operating partner. “By understanding both seller and buyer motivations and preferences, management is armed with greater insight into why people make decisions.”
Takeaway: Average margin by product doesn’t tell the whole story.
Become Sherlock Holmes.
Getting the right data can give you a critical edge, but thinking about that data in the right way can be at least as important, which is why an observational data analysis is extremely useful.
Let’s assume we work for a national wood pallet manufacturing and refurbishing company. Wood pallets can be reused an average of 20 times. Pallets are needed by other product manufacturers and distributors to move, store and ship products, which are located in disparate areas of the country. However, major shipping, train and truck distribution hubs are located in just a few centralized cities (Los Angeles, New York, New Orleans, etc.).
Our pallet company will need locations at the distribution points of our customers as well as areas close to the OEM manufacturers and distributors. When a pallet comes into our pickup location at a major distribution point, it will be given one SKU. We will likely categorize the pallet and repair or scrap it at that location. Once it has been repaired, it will take on a different SKU number. The repaired pallet will be transferred to regional locations throughout the country. The “cost” of the pallet will be some variation of an internal transfer price. So, your new SKU for the repaired and transferred pallet has some nominal price that is not reflective of the market price. You then resell that pallet to a local customer.
How do you do SKU analysis for this item?
Begin with data. Review the internal cost for the pallet. Then compare that to the cost to buy in the same category and quality pallet locally (build versus buy). If the internal transfer cost includes a markup and subsequent profit for the division that received, repaired and shipped the pallet to us, and that profit is appropriate for that unit, then we can keep that as our analysis price. If it does not, then we should use the local “buy” price for the pallet. Then, taking the appropriate cost, we should run an analysis by customer and margin for that SKU.
Takeaway: At the end of the day, you may want to buy a long stem pipe and deerstalker cap to look the part of Sherlock Holmes in this SKU analysis investigation.
SKUs nationalized, regionalized and localized.
When performing SKU analysis, consider slicing the data beyond the aggregated information. If you sell throughout the U.S., then your aggregated data will be nationalized. Reviewing SKUs at a national level will show how the SKU is doing on average across the country. But most certainly, when looked at in smaller segments, the SKU will have different sales dollar volumes.
Let’s use a national, wholesale food distributor that runs its SKU analysis data at the aggregated (national) level. Bread is a great seller, peanut butter is a great seller and jelly is a great seller. However, the analysis reveals that grits are not a great seller. In fact, they’re way down in the tail. Stopping the SKU review at this point would mean the delicious corn mill concoction would no longer be gracing the tables for many consumers. Breaking it out by region and location (state) reveals a different story. Suddenly, those grits are in the top quartile of the SKUs for the Southern region. And reviewing the SKU data locally shows it ranks even higher in Mississippi, Alabama and Georgia. What is the targeted action? Reduce or perhaps eliminate grits from poor-selling regions, while keeping the high-volume SKU in the southern region.
“Companies with a national or even global presence can lose sight of the local or regional needs and preferences,” says Lev Ross. “A deep customer and stakeholder study can reveal the reason behind a buying decision—potentially opening the door to future opportunities and a more targeted sales and marketing approach.”
Takeaway: Never assume that SKUs perform equally well across a broad area. Purchasing varies from region to region and even by location. A good analysis will reveal discrepancies.
Analyzing, going beyond rationalizing.
Rationalizing your SKUs is a great first step in the process of deciding which SKUs to keep and which to eliminate, but a deeper analysis is an important part of your growth strategy and will further improve gross margins.
Stay tuned for part 4, SKU Strategy, where we look behind the curtain to reveal the magic of SKUs.
Robert Nix has more than 25 years of experience in accounting, financial and operational leadership roles. He is an expert in cost accounting and a lean six sigma certified. He believes in utilizing processes, data and technology to drive revenue improvement, cost savings and scalable efficiencies. He also believes in servant leadership; putting the needs of the team first and driving a strong group performance towards helping our clients.
For more information on ways to optimize your SKUs and add value to your business, contact Robert Nix at: email@example.com.