Tag: basket analysis
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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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Increasing Order Size With Basket Analysis
I came in to buy milk and I am walking out with 10 things in my basket. The man behind me had only one item in his basket. “How do you do that?” I asked. “It depends on what you come in to buy,” he responded.
There are a few “seed items” in the store that drive additional sales because of key concept ‘this is often bought with that’. These items are often found together in customer baskets and orders. Smart retailers will put these items as far away as possible, so that you have to walk through more aisles to get from one item to the other, in hope that you will buy more along the way. Bread and milk is a good example of that. The reverse is also true. For items that are often bought together, if the store does not carry both, they will lose the customer.
Every retailer knows that it is very profitable when a customer comes in to buy one item, but ends up with many more in his basket. Understanding the product relationships in the market basket is key to driving up the order size or basket size.
Understanding the Customer basket make-up
A retailer typically carries thousands of items. A small convenience store may carry 1,500 items. A grocery store typically carries 15,000. And the super stores like Wal-Mart and Targets carry well over 25,000 SKUs in each store.
Insight Into Customer Baskets and Product Relationships Based on Buying Behavior
The SKU management is a tremendous challenge because the buying pattern is truly a long tail. Retailers know their top sellers; these are easy to identify, but the frequency of buying falls of very sharply. The chart shows an example of one retail store operation over a 3-month period. The store carries 25,000 SKUs, has 100,000 transactions per month. The analysis covers a 3-month period, and shows the distribution and popularity of SKUs based on the frequency of purchase.
Here are some quick stats for insight into the baskets and buying behavior – The most popular SKU has a frequency of 3,435. That means is has been bought in 3,435 baskets. The frequency of the 100th most popular item drops off to 225. That means it is only in 225 baskets over the 3-month period. There are 4,000 SKUs that are bought only once. But the really interesting fact is that 1,800 SKUs are bought together 98% of the times. None of these 1,800 SKUs are top sellers! But when they are purchased, they are very often paired with other items. This intelligence is key to increasing basket size and ensuring the store is carrying the right items. SKU rationalization analyses that view each SKU as an independent item, that is bought in isolation, will result in incorrect merchandising and lost sales.
There basket analysis also showed the low-frequency/high-correlation SKUs. Every retailer knows the challenge with these items. These items sell rarely, they sit on the shelf for along time, and when it is placed in a basket it will only sell if the paired item is available! These are problem SKUs because they are capital hogs and always show up in inventory issues.
Insight into the basket make-up and the product affinities based on buying behavior is key to merchandising and increasing order size. Merchandizing, up selling, cross selling and add-ons based on buying behavior results in increased sales and enhanced customer experience. On the other hand, suggestive selling based on tribal knowledge and ‘he said/she said anecdotes’ will result in poor results and loss of customer good will.
Adding one more item to 10% of the baskets can increase sales by 5%
Sales Impact Of Increasing order size
The basket size or order size analysis shows the revenue potential of increasing the order size. The chart shows a typical basket size analysis and the upside opportunity of increasing order size. The results from this case study showed that adding one more item to 10% of the baskets can increase sales by 5%.
Manufacturers, distributors and retailers offer thousands of products. There is a significant opportunity to increase sales across all channels with knowledge of product relationships (what items sell together), when and where. It is commonly agreed that B2B purchase behavior is “need based” while a large percentage of B2C sales is emotion based. Hence, in B2B commerce, the product relationships have to be highly accurate to be relevant.
Quick review of definitions:
Frequency – Number of orders that contain this item
Volume – Number of items sold.
The volume of an item may be high because one customer bought a lot. However, frequency is better measure of popularity and is not skewed by a one-time large volume sale. In fact, SKU analyses will often remove large volume buyers to reduce this bias.