Tag: affinity
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Assortment Planning and Up-selling based on “This Sells With That”
Walmart’s (formerly Wal-Mart) announcement of a SKU rationalization project contained in this year’s 10-K filing with the Securities and Exchange Commission confirms the importance of this initiative for all retailers. 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. As shoppers, we all know that we buy items 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. Simple example – If I cannot buy both bagels and cream-cheese at the same time, I will go to a store where I can find it!
SKU Classification Based on Frequency of buys and Product Relationships
SKU analysis for assortment planning is based on two key metrics:
- 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 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.
SKU Categorization For Merchandising, Up selling and Cross selling
The second chart presents a more detailed discussion of the SKUs based on frequency of buys and affinity with other products. (Affinity simply means “this items sells with that”. )
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 companies sales and revenue on hear-say.
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.
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Making Analytics Actionable
I just read an article titled “Making Analytics Actionable” by Michael Vizard. He makes two good points – predictive analytics is not new and analytics needs to be actionable. I could not agree more.
On the first – predictive analytics has been around for a long time. We used to call it forecasting. It was difficult then, and it is difficult now. Forecasting gurus, or should I say Predictive Analytics gurus have thrown every mathematical trick at the data to predict the future. It reminds of a quote by the CEO of a Fortune 500 company, who said “No one knows how to forecast. If they did, they would be in a different business.” I think by different business he meant forecasting money on Wall Street. But we all know that has gone! And now may be the quants on Wall Street would agree with him as well.
On the second point – yes, analytics should give you actionable information. As I hear from our customers, time and time again, they do not need more data. Companies are drowning downing in data. (maybe “downing” is the right word!!! We are downing in data! It is a downer!!!) In the name of Business Intelligence, they now have the capability to slice and dice this data, creating more data! The purpose of analytics is to convert all that data into something meaningful and actionable. If the analytics does not accomplish that, it is just another BI tool.
As you investigate analytics for your business, here are a few best practices:
- The analytics need to be focused by business function.
- The analytics needs to answer the question “What do I do with this?” and “what is the business value”.
- The analytics should make your job easier, and the recommendations should want you coming back to it over and over again.
The answer to the question “What do I do with this?” should be actionable tasks that a business should be able to run with. That is called Business Analytics. The reason the third point exists is because you, the user, would only come back to it over and over again if it made your job easier. That is the key to business analytics. “Take all that data and convert it into actionable tasks and make your job easier”. How is that for a tag line!
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