Shoppers expect that when they make a purchase, they do it in groups. It is the job of the retailer to figure out what kind of stuff we buy together, so that they can optimize their assortment planning. Shoppers rely on relevant recommendations and even look for them at the checkout. Simple example — If Susie cannot buy both bagels and cream cheese at the same time, she will instead seek out a store where she can get everything she needs.
In SKU (Stock Keeping Unit) rationalization, a retailer examines the profitability of items and vendors as a whole. When done in a linear fashion it results in lost sales and bringing back the SKUs.
SKU rationalization projects look for “What items are bought together” so that retailers and distributors can improve assortment planning. SKU analysis for assortment planning is based on two key metrics:
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- The frequency of buys. This is a metric that measures true popularity of an item based on how often customers buy this product. For measuring popularity, it is a better metric than volume as it is not skewed by one-time large volume purchases by a few customers.
- How often this item is bought with other items. This metric is a measure of how strongly correlated this item is with other items that you sell. If an item is always purchased with another item (like bagels and cream-cheese), it is very important to know the “often bought with” items, and ensure that they are stocked together and in the right proportions. Not having one item from a basket of high affinity products will result in loss of the customer.
These two metrics also apply for Amazon-esque suggestive selling for online sales. Items that have high correlation with other items are candidates for suggestive selling, up-selling, cross-selling and add-ons. For example, this would be a way to detect that cables, cartridges and paper that are bought with a particular printer. So when that printer is bought, you can automatically suggest the other items as add-ons. (Not to get too technical here, but the suggestions are not symmetrical. So – you cannot suggest a printer when a customer buys paper!)
The implications of these product relationships cannot be emphasized enough on your merchandising strategy and your supply chain planning. Manufacturers, distributors and retailers struggle to manage thousands of SKUs. This SKU classification presents a methodical approach for assortment planning to maintain the most profitable portfolio.
I. Items that have low-frequency/ high correlation are important to detect. These are trouble-maker SKUs. As companies goes though SKU rationalization projects, these items often end up on the chopping block, only to brought back again because they caused lost sales. These items are difficult to identify and there is a need for sophisticated analytics to easily identify these items.
II. Items that are bought in high quantities, but always with other items are great candidates for merchandising and bundling. They are a natural for creating sales lift and revenue lift. It is often counter-intuitive, but your #1 top seller may not be in the #1 pair of top selling items. That is why linear analysis of the SKUs based on volume or frequency results in incorrect merchandising.
III. The low frequency/ low correlation items are the targets for SKU rationalization projects. However, these items are very difficult to identify. Hence SKU projects typically end up cutting the wrong SKUs. We call these items Low-Loners. If you are a distributor, you do not want to carry these items. They are perfect candidates for drop-ship.
IV. Items that sell in high frequency, but usually on their own, require high service levels. We call these Hi-Loners. Examples of these items are cigarettes and gas at a convenience store. And by the way, beer also falls in this category. And please do not believe the beer and diapers myth! It is a myth!
The challenge with SKU management is that companies make decisions based on product relationships from hear-say, industry veterans or tribal knowledge. I think that’s how the beer-diapers myth was started! Across thousands of SKUS, and with fast changing demand patterns, this results in errors, and not a sustainable process for assortment planning and SKU management. There is too much at stake to base a company’s sales and revenue on hearsay.
As SKU management is getting a lot of attention, there is need for robust solutions based on real customer buying behavior, to help companies maintain their SKUs on an continuous basis. The value is high sales, higher margins and improved customer service.
Retail Case Study: Boosting Sales by Automating Up-Sell/ Cross-Sell Capabilities
S.P. Richards is one of North America’s leading business products wholesalers, distributes over 30,000 business products to a network of over 7,000 resellers in the United States and Canada from a network of 44 Distribution Centers. SPR works hard to make their dealers successful. Lacking insight into customer buying patterns, independent office products dealers were losing their competitive edge, resulting in lost profit opportunities due to:
- Proliferation in product-related data across multiple sales channels
- Inability to convert customer-buying patterns into cross-selling opportunities
SPR empowers independent dealers to boost sales and regain a competitive advantage through the automatic detection of customer-buying patterns for cross-selling opportunities, leading to:
- Automated cross-selling opportunities across 50,000 office products and multiple sales channels
- Realized 9% boost in sales
- Enhanced customer satisfaction and ease-of-use
Their latest value-add is automated up-sell/ cross sell capabilities on their ecommerce web site. This capability is very powerful and could not come soon enough for their dealers such as GiveSomethingBack, ReStockit, and Village Office Supply, who have shared:
- If we can sell one more item to a customer, who is already buying from us, the impact on sales is dramatic. (Quoting verbatim – “it’s a no-brainer!”)
- My customers want me to suggest relevant items that they can buy, while I have them on the phone! I want every sales rep to have that capability.
- It is so much easier to sell more to existing customers than to try to get new ones. So we embrace all the help we can get to service our existing customers better.
Emcien CEO Radhika Subramanian is a seasoned entrepreneur with decades of experience helping large organizations utilize the insight buried within their data. Before co-founding Emcien, Radhika was President and CEO of Idmon Corporation, which was funded by Cordova Ventures and Imlay Investments and ultimately sold to Swissair Group in 2001. Numerous associations have recognized her as an innovator and pioneer in analytics, with a proven track record with global giants such as Porsche, John Deere, NCR, Dell and more. She is recognized as a Leading Woman in Technology by WIT, was a finalist for the Franz Edelman Prize in Excellence in Management Science, and more. A sought-after presenter and speaker, she is a regular contributor to trade and national publications, including Forbes.
