Tag: shopping

Value of SKU Intelligence (What Are Customers Buying?)

One of the most frequent questions we are asked about Emcien’s methodology is “why hasn’t anyone done this before?” The answer seems obvious to us, but we should probably write it down once and for all so we can just point people to a definitive document.

Exploding SKU’s to attributes generates a LOT of data. It is only useful if this translates to actionable intelligence. As we all know, we don’t need more data. We need actionable items and recommendations to improve business. The simplest answer is that collecting all the attribute data for SKUs has not been done before, because the algorithms have not existed to farm intelligence from them. In this blog I am going to address –

1. Why having the data with the SKU attributes is invaluable!
2. What analytics capabilities are needed, if you have that data
3. Value of having SKU Intelligence (To drive SKU velocity)

Consider a product that has attributes, and offers lots of variety, also called configurations. Almost all products come under that class today! Think car, computer, or cell phone. The product has features, and each feature has alternative options. For example, a car has an engine (V-6 or V-8), a body type (sedan or convertible), and a color (red, green, blue, black, or white). Any specific car has many choices for each of the features. Customers make choices on the features. Do you want the cloth seats or the leather seats? Do you want the DVD player? How about the iPod connector? Make a choice for about 30 features and you are done. This applies for shampoo, toothpaste, computers, light fixtures, consumer electronics, … all products that have attributes choices.

We consider a configurable product that has a large number of features, each of which many alternative choices/options. Notice that we have finessed the hierarchy problem by allowing only two levels: feature and option.  A customer will typically call out only a few attributes during a purchase. They expect you to know how you can complete that spec to fill the order. That is the single biggest opportunity at every point of sale!

If we have data with attributes for every SKU, we can begin to talk about buying patterns! A buying pattern is groups of options that are bought together, like the red color with the convertible body style. Or the DVD player with the leather seats. Or the pattern might involve 3 options, 4 options, 5 options…. many options.

Auto detect most popular attributes in fastest sellers

This brings me to a very important point. If you have sales data with attributes, you need an analytics engine that will automatically detect and tell you what attributes are bought together and are highly popular. A reporting tool that makes you query every choice combination will NOT work! You will be very old by the time you get an answer to most popular choices, as there are millions of attribute combinations. Emcien offers an analytics engine with cluster analysis that will tell you what attribute groups are popular. This answers the questions – what are customers buying and what attributes are popular. This knowledge can also be used for planning the SKU definition and knowing what products need to be on the top landing pages of your web site/ store aisles. (SKU Definition is the list of attributes in the SKU.)

Once the SKU definition is in place, 75% of the cost structure and efficiency of your supply chain has been fixed ! Your supply chain operates under the assumption that the SKU definition is correct. What does this mean? This means that if you offer 2 SKUs with slightly different attributes, that could have been consolidated into one, the supply chain will suffer that cost and inefficiency. The SKU definition has to be optimized before you send it into the supply chain. This is a key driver to SKU inventory.

Customers buy products based on choices at the attribute level. If you cannot gather demand intelligence at the attribute level, you are out of touch with your customer. Customers DO NOT buy SKUs. They do not know the SKU numbers, and they do not care.

SKU Challenges Based on Supply Model

Your business falls into one of these categories based on your supply model (see table). Knowing what attributes customers are buying can dramatically improve your demand response. You will be able to improve -

• SKU definition -This means knowing how many SKUs you need and what needs to be in the SKU
• Demand Forecasting – You will be able to forecast demand at the attribute level, which is the level that customers are buying
• SKU Inventory - plan what to stock to have highest turns.
• Sales Productivity and Efficiency – If you know what attributes are selling together, you can implement an automated recommendation engine (EmcienMatch) and your sales rep can recommend a good choice to the customer during the ordering process. This is probably the single biggest value of knowing what attributes customers are buying. Your sales reps are a trusted advisor to your customers. If the sales person know that all customers of a certain type bough a particular configuration, he could recommend that choice to the customer. Win! Customer is happy because he done! Sales person is happy because he looks smart! You are happy because you increased your sales repeatability!

For our Nerdy Math readers, here is technical nugget that you will love:
SO – here is another reason why what Emcien does has not been done before. Statistics deals with numerically valued variables. A numerically valued random variable X has a domain that can be classified as a ratio scale, an interval scale, an ordinal scale, or a nominal scale. In a nominal scale, the numbers {0,1,2,3,…} are just labels and have no numerical significance. In an ordinal scale the numbers provide an ordering, so that 2>1 in some appropriate sense. An interval scale allows real numbers (like 3.27) and differences are significant, but ratios are not. The classic example is temperature. A difference of 10 degrees is bigger than a difference of 5 degrees, but 100 degrees is not twice as hot as 50 degrees. Finally, a ratio scale allows all of the usual numerical operations on real numbers. Most of statistics assumes a ratio scale, and almost all of it assumes at least an ordinal scale. This leads directly to the mean, variance, covariance, and correlation. It also leads to metric spaces with distance functions.

But we want to consider a random variable X that represents a choice between cloth seats and leather seats. You might argue that leather > cloth, because leather is more expensive. But what about color? Is there any sense in which green > red? We are really interested in nominal scales. We may assign 0, 1, and 2 to “none”, V-6, and V-8 respectively, but these are just labels. That has a profound effect on the statistics we can use. And beyond statistics, we will have to develop notions of proximity or closeness that do not depend on distance functions. So it hasn’t been done before because the mathematics you need is not the popular stuff that’s in the textbooks.

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