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True Demand Intelligence – Knowing who is buying what, where and why
Demand Intelligence – Knowing Who is Buying What, Where and Why
SKU numbers are an easy way to keep track of items that are built, stocked and sold. The SKU number itself is arbitrary and contains no ‘intelligence’.
The SKU number was invented for a very good reason. When this practice started, the number of SKU’s was small, and people and systems needed a simple way to track what they had. So ASP-678 may be the SKU number for a toothpaste tube with spearmint flavor, whitening, and tartar control. However, customers do not look for SKUs. They look for toothpaste with whitening, tartar control, flavors, sizes, etc. Customers buy attributes, and combinations of attributes. Some companies code the attributes into the SKUs, by concatenating two or three character codes (like SPWHTC). But this is at best a clumsy way of handling a few attributes. Companies want to know what attributes customers are looking for, and SKU numbers hide the attributes.
As the number of attributes starts to grow, whether you have them coded into the SKU number or not, the problems start to mount! The most important one is that companies do not know what customers are buying, or trying to buy. As the number of choices grows, the number of combinations grows much faster and companies drown in their own SKUs.
SKU intelligence is going behind the SKU numbers and ‘detecting what attributes customers are buying’. Knowing who is buying what, where and why is “True Demand Intelligence”.
Products have attributes. For example, a computer has a processor, a memory, and a hard drive. For each attribute there may be several alternative choices. This means that there are many different product configurations. Some companies make only a fixed subset of all the possible configurations and give each one a SKU number. Other companies allow customers to order exactly what they want, and if this is something new, then they create a new SKU number for it. In either case, a SKU number is supposed to represent a unique product configuration.
If you are trying to figure out what your customers want, then SKU numbers are a form of encryption. You have to look at your sales history in terms of the underlying attributes, and the choices for those attributes. Instead of looking at one SKU number you need to look at perhaps 20 separate attributes. The SKU number is a way of collapsing those 20 dimensions into a single dimension, with tremendous information loss. One of the things that is lost is proximity to other SKU’s, based on attribution. A customer who bought SKU A-1234 might have been satisfied with (or really looking for) SKU B-3728. These two SKUs have the same choices for 18 of the 20 attributes, and differ on only two. This is obvious when the unique configurations are represented as a set of attribute choices, but hidden when they are represented as SKU numbers. The first step in analyzing a sales history has to be expressing it in terms of the underlying attributes. Each SKU number has to be expanded into a list of choices. Then we can begin to find patterns in how the choices are made. The leather seats and the DVD player are usually bought together. Engine block heaters are not ordered on convertibles. Buying patterns exist at the attribute level, not at the SKU level.
“Buying patterns” are popular combinations of attribute choices. These can be pairs of attributes, triples of attributes, or even more. Popularity is measured by the share of sales that have that combination. Buying patterns are helpful in selling, because they reveal how customers can be moved to configurations (SKUs) that we have in stock, or that we would prefer to build. Experienced sales people are skilled at moving customers, but If these patterns are represented in some kind of knowledge base, then a computer can make the recommendations.
Customers also have attributes. The simplest is perhaps geographical location. There are patterns that involve both product attributes and customer attributes. Customers in Florida are more likely to buy convertibles; customers in North Dakota are more likely to buy engine block heaters. Customers may have several attributes, for example demographic attributes for individuals or industry attributes for companies. (We don’t assign SKU numbers to customers!) If your sales history contains information about the customer as well as information about the product, then we can look for buying patterns that are associated with certain kinds of customers.
As an example, for a desktop computer the list of attributes might be: Processor, Memory, Hard Drive, Keyboard, Monitor, Mouse, CD/DVD, Application. A specific SKU number like A-1234 is a code for a specific configuration, say (2GHz, 2GB, 120GB, Ergonomic, 22” flat panel, Wireless, R/W Combo, Gaming). The Application attribute is really a customer attribute, with values like Home, Small Business, or Entertainment, as well as Gaming. This would make it possible to look for typical Gaming configurations and typical Entertainment configurations.
SKU numbers are a useful shorthand for record keeping. Each SKU number represents a unique product configuration. But analyzing SKU numbers is like analyzing telephone numbers. To see the buying patterns, you have to go to the attribute level. The patterns exist among the attributes, so you have to decode the SKU numbers to see them.
Self-Service simplifies Product Offerings and increases Margins
Self service is a term we all know, such as pay-at-the-pump gas and self-checkout stations at some grocery stores, and now more obscure things like video game kiosks by GameFly, but the true tidal wave of self-service hasn’t even started, and it’s going to be good for both the consumer and the manufacturer, if done right.
Self Service Grocery Scanner
When you checkout your soda and cereal by swiping products across a scanner at the auto-checkout stations, there isn’t much complexity other than when you get a problem with the scanner reading a smudged bar code or trying to locate the button for ‘snap beans’ when you put those on the scale. The transaction is smooth, quick and you are in control, which is a good feeling as a buyer, you are not being sold, you are buying just what you want, quickly and easily.
But what happens if you try to buy a “configurable product“? In the grocery store, the only thing configurable is the weight of produce, but other than that, the costs and configurations are set in stone and are detected by reading the bar codes. Easy to understand as the buyer and relatively easy to deal with as the seller. Configurable products are those where you have to make many choices before you can order the one product. Products like computers, cars and thousands of others where the buyer has to describe their preferences or choices so the product can be created and delivered. It’s even more complex in a B2B environment than it is in B2C, where the products available and choices are astronomical. Products like Lighting, Valves, Agriculture and Construction Equipment, Lifts, Electrical equipment, cooking equipment and conveyors have more choices and variants than you can imagine and that variety makes it hard to order, build and deliver efficiently.
Usually a large direct sales force is sent out with complex price books (sometimes online in PDF form) to sit with customers and prospects and help them combine choices in hopefully valid ways. The choices a customer have to make are quite extensive, ranging from tens to hundreds of choices. Most of these choices the customer doesn’t care about, but they are required by the manufacturer just so they can build a valid product. Customers care about the few things that matter to them but after that, they will just choose things that “seem to make sense” just to complete the order. Sometimes they don’t even do that, they get so frustrated with 60 more questions about features and options on the product (many of which they don’t understand) that they walk away.
In some cases companies believe that putting in a configurator is the solution to their problem. Configurator’s automate the order process by ensuring that the order is VALID. The engineering and marketing rules that drive what can be built and offered are setup in a configurator such that the user ordering the product is led through valid questions and end up with a build-able product. Now this product may be build-able but it also may be a one-off low-margin brand new SKU that manufacturing hasn’t built before and requires some parts they aren’t carrying at this time. All this for something that was only 2 choices from a very popular configuration. And those 2 differences only happened because the customer was asked 20 more questions after they entered the 5 things they cared about. They chose as best they could, but without any guidance or suggestions, ended up on a new SKU which will ultimately explode into huge numbers of parts and processes to support the new SKU.
Now if the customer only had to enter the 5 things they cared about and the system recommended the combination of other choices such that the customer’s price limit was met and the configuration wasn’t a new SKU and the SKU had a good margin, then it would have been a win-win for everyone. And the whole process could be complete quickly and easily. The customer wouldn’t have to answer any other questions and would feel that same feeling that you do when you swipe your can of soup across the scanner at the market. The manufacturer wins as well because the customer was guided toward an existing configuration so the cost of creating and supporting a new SKU was avoided. It’s happening now with recommendation engines that leverage buying patterns to suggest full configurations based on the few attributes a customer gives it. Just like Amazon can recommend other books you might want to read based on the current “fly fishing” book you are looking at now, suggestion engines can be utilized to provide this convenience for much more complex products.
That’s the self-service tidal wave that’s coming, when all products, not matter how complicated can be ordered by simply asking for the attributes that YOU care about, what your price limit is and then Voila! it’s done. Customers will order more from companies that offer this convenience. Just think about how often you walk into the gas station to pay as opposed to pay at the pump. And if you had two stations to fill up at, one was pay at the pump and the other required you stand in line after pumping the gas, which do you think you would most often go to? Simplification is good for everyone, and profitable too.
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How many choice combinations does your product have? That depends.
Possible combinations
This is a question with several answers. The easiest answer is the least useful. The number of possible build combinations, or unique configurations, is easily computed by multiplying the number of options for each feature. For example, if your product has feature A with 3 options, feature B with 2 options and feature C with 4 options, then there are 24 (3 x 2 x 4) possible build combinations.
These numbers grow very rapidly. If you have 5 features, each with 4 options, there are about 1,000 build combinations (exactly 1,024). With 10 such features, the number of combinations is about 1 million (1,048,576), and with 15 features it is over 1 billion (1,073,741,824).
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Key Concepts To understanding Product Variety
1. Product
A product is something offered for sale to customers. This is deliberately vague, because we want to encompass services as well as tangible products. Most of our discussion and examples involve manufactured products, but our framework also applies to services with many variants like insurance policies and cell phone calling plans.
2. Instance
An instance of a product is a specific unit of the product: the car that Joe buys, which has a specific VIN (Vehicle Identification Number).
3. Configurable Product
A configurable product is a product where the instances are not all identical. No. 2 pencils are not configurable. Computers, cars, tractors, refrigerators and cell phones are configurable.
The Root Cause of Product Complexity!
Emcien defines product complexity as simply the ability to predict what the next order coming into the company will be.
Think about it: If you only made product configuration A, you have 100% confidence in knowing that the next order in the door will be configuration A (assuming you get an order in the door at all, not a total given in this economy). But if you have configurations A and B, it’s harder to know and with A, B and C, it’s even harder, and so on. When you have thousands of configurations, predicting the next one is very difficult.
It’s not just the number of configurations that’s important but also how they’re distributed. If I have 10 configurations but 90% of my orders are for config A, then it’s still safe to predict that the next order is config A. But having 10 configs that have each been ordered 10% of the time is extremely complex!
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