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Increase Sales With Analytics on Sales Receipts
Increase Sales with Purposeful Analytics On Sales Receipts
The sales receipt is a neatly itemized list of purchases. Every purchase comes with a specific need, and hence the sales receipt is the true voice of the customer. As demand patterns change, the sales receipt data can reveal tremendous intelligence on what customers are buying, the changing trends and what the future purchases will be. “Stores Face New Kind of Shopper” is a very interesting article by Ann Zimmerman and Rachel Dodes in The Wall Street Journal (Monday, December 28th 2009).
The financial crisis has dramatically impacted sales in all markets. Over the last two years sales have plummeted, consumers have disappeared and profits have evaporated. The financial crisis has caught us in a time of tremendous over capacity. In the B2B markets, companies have been dramatically shrinking capacity to match the new level of demand. In B2C markets, retail experts generally believe that the US now has more stores than consumer demand can support.
Customer buying patterns are dramatically changing as capacity adjusts to the new level of demand. The financial downturn further impacts this change, as customers look for new ways to stretch their money. To complicate things further, customers today have many choices of products, channels and price point. The internet has become a primary source for browsing and comparison shopping. This extends the reach of the customers, and puts pressure on companies to cater to wider product choice selection. As these shifts continue to change buying behavior, companies must have the capabilities to stay ahead of the changes. With the speed of change in products, companies need to adapt fast and stay in tune with changing demand.
The good news is that the sales receipts reveal these changing trends and buying patterns. However, this requires purposeful analytics designed to convert sales data into actionable tasks. I would also like to mention that sales data has a unique structure and characteristics. The purpose of the analytics is to reverse engineer the sales data to determine what is selling. If your product has a lot of feature choices, you can get insight into the popular choice combinations. If you sell lots of individual items (i.e. large number of SKU’s), you can get insight into what are items that are commonly grouped together. Emcien offers analytics designed for sales data. Emcien’s advanced analytics cal also give you intelligence into what choices cause the selection of other choices. Armed with this insight, you can manage your product offering to always stay ahead of the trends.
With purposeful analytics designed for sales data, you can get insight into -
- What product choice combinations are popular?
- How do the choice combinations vary by channel?
- What choice combinations are profitable?
- What are the changing trends and what choices will sell in the future?
As the market shift continues, this level of demand intelligence is mandatory to stay profitable!
Gartner: Cloud computing, Analytics Top 2010 Strategic Tech List …
Cloud computing and analytics have jumped front and center. Gartner renamed Business Intelligence (BI) to Analytics.
On the analytics front, Gartner said in a presentation: “We have reached the point in the improvement of performance and costs that we can afford to perform analytics and simulation for each and every action taken in the business. Not only will data center systems be able to do this, but mobile devices will have access to data and enough capability to perform analytics themselves, potentially enabling use of optimization and simulation everywhere and every time. This can be viewed as a third step in supporting operational business decisions.”
Analytics named as the #2 most important category, behind Cloud Computing
Gartner names analytics as the #2 most important category, only behind Cloud Computing. Analytics is becoming more important to all aspects of every business.
So what is analytics? It is purposeful focus on data. It requires mathematical analysis and algorithms on data, to compute Key Performance Indicators (KPI) that are valuable to measure the condition of the business.
Emcien offers analytics to detect product-buying patters with a focus to improve product mix and profitability. As product choices and market segments have grown to dizzying levels, companies struggle to have a finger on the pulse of their product mix, markets and profitability. What products are making money? What are customers buying? What are popular choice combinations? What is common across the market segments? What are the trends by market segment? What product choices should we offer?
Emcien’s analytics answers these questions with patented mathematical algorithms applied to sales data. Emcien’s analytics auto-detects what choices combinations customers are buying, the trends and which choice combinations are profitable. The product offering is the lifeblood of a company. At Emcien we equate product mix and choice mix to profit. If your product mix is not aligned with what customers are buying, you will not make profits. It is as simple as that!
Emcien’s analytics is built on sales data. Unlike web analytics, where is data is a bear, getting sales data is a relatively easy. This is usually the best quality data compared to anything else. The sales data is a true capture of the voice of the customer. It allows companies to see what customers are spending their money on. The value of Emcien’s analytics is visibility into customer choices, and more importantly, recommendations to increase profitability.
Analytics are very powerful as the data reveals Key Performance Indicators that offer continuous insight into the business. You can dispel a lot of beliefs that the company has based on gut feel and cooler talk. When you provide actual sales data driven insight, it has a profound positive impact on the company and the decisions. To convert analytics to action select KPI’s that are a part of your business process and budgeting process. This is key to showing improvement that is meaningful to the organization. As analytics gets more visibility in the C-suite, there is demand to produce KPIs that executives are measured on. This will help gain buy-in at executive levels as adoption of analytics grows.
Follow the Money!
Buying patterns and the economy are constantly changing. Some products and categories that were popular are not anymore. You cannot control your customers’ tastes or the economy. But if you follow how the money is being spent, you can make a lot more! Unlike clicks and page views, buying patterns are very reliable as they are based on actual sales. Money changed hands. An economic transaction occurred!
Track sales transactions to understand your customer’s buying patterns, establish a more relevant product mix, satisfy more people and sell more.
Your customers speak to you when they buy. If you can listen to what your customer wants you can manage the buying process and you can influence and even control it. “Why would I want to do that?” you may ask. By better understanding your customer buying patterns you can establish a more relevant product mix that will satisfy more people. You can also guide them to more profitable choices at point of sale based on product availability or close substitution. You will satisfy more people and sell more. You will also make it easy for them to buy your products and services.
The Analytics of Buying Patterns
First, take the guessing out of the equation. You need to know what your customers are purchasing and what they want to buy from you in the future. This intelligence is available in your sales transaction data. Customers buy your products and services in distinct patterns.
Products and services have become more complex and companies offer a dizzying array of choices. However, with analytics the sales data will reveal popular combinations of choices. These popular combinations are guides on how you can make your products and services easier to buy. How you can make is easier for customers to do business with you.
There is also the issue of product profitability. Some of the choice combinations are more profitable than other. Again the analytics will reveal which combinations are moneymakers, and which ones not! Once again – if you have access to this intelligence, you can stock the right product mix and guide customer to better choices. If you stock inventory in your store you can leverage this intelligence to plan an optimal inventory mix. That means making the most money from the least amount of inventory investment while satisfying your customers’ needs.
Whether you are running an online store or a brick ‘n mortar store – this is a key principle to selling more and maximizing your capital utilization.
Demand Sensing And Demand Shaping
Forecasting and planning is a challenge in the best of times. The times we are in make this a herculean task. Market demand shifts continually as economic conditions change, products change, prices fluctuate, competitors act, new products are introduced, marketing creates promotions,……. The list is quite endless. Current planning and forecasting methods are reactive and struggle to keep up with these shifts.
The solution is “Demand sensing and Demand Shaping” – active methods to predict what demand will arise and keep ahead of the market. Demand sensing is the ability to detect what choices customers are buying patterns and the trends associated with these choices. Demand sensing can help you to quickly see market shifts to plan your product mix and offering.
Customer Buying Patterns "Customers who bought this SKU also bought this other SKU"
Demand shaping is the ability to guide customers to the best choices at point-of-sale. This is the key to increase revenue and supply chain efficiency. However, demand shaping needs product intelligence at point-of-sale to guide customers to the best choices. Some of the ways to demand shape are –
- If you offer many products or SKUs, there are typically strong buying patterns in the demand. For example – This printer is often bought with this unbleached paper, this ink cartridge and cable. Then, when a customer selects the printer at point of sale, you want to automatically show him the other items that have strong buying patterns. The customer will thank you for this recommendation because usually they need this additional stuff, and you just saved him a ton of effort thinking about it, and a ton of time searching for it. And you made more money in this sale!
- If you offer a product with many attributes, every sale will begin with the customer calling out a few attributes. The opportunity to demand shape is to recommend a good choice based on the partial list of attributes the customer has called out. Demand Shaping requires the ability to complete the order with the right attributes. The best way to complete the order is to have sales intelligence these attributes are bought with these other attributes. It is the Amazon-esque way to look at products with many attributes.
- The biggest opportunity of Demand Shaping is guiding customers to close-enough SKUs. Most customers describe the products they want to buy with a ‘kinda-sorta’ attribute description. As the number of product features grow, there are a large number of SKUs that are similar or close-enough that they can satisfy the customer. So there is a significant opportunity to guide a customer to a similar or close-enough SKU at the point of sale. The recommended SKU may differ in attributes that the customer did not “call out” or specify. If you can offer up this SKU it is a win-win. You have served the customer. You have won the sale. You have moved your inventory. And your competitor did not get this customer.
As product choices and the number of SKUs grow, these techniques are mandatory for an efficient supply chain and for a good customer experience in this customer-centric world.
I just read an article by Mark Pearson, Six secrets of Supply Chain Planning Masters.
Quoting Mark Pearson’s article – Think of demand sensing as predicting what demand will arise, as opposed to simply reacting to incoming orders. Shaping demand, on the other hand, is all about steering customers toward available products and services. Compared to laggards, more than four times as many masters said they can predict demand with greater than 80 percent accuracy levels. And nearly twice as many masters said their ability to shape demand was “good” or “excellent.
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Does Your Inventory Look Like Jurrasic Park?
Does Your Inventory Look Like Jurrasic Park?
I was working with a industrial manufacturer who made construction equipment, such as diggers and backhoes. They had a beautiful park-like setting around their factory. Artfully placed on manicured lawns around the ponds and fountains were quite a few backhoes . It reminded me of Jurassic Park. We discovered later that these backhoes were a lot like the dinosaurs – they had failed to adapt.
The backhoes in the park, though attractively displayed, were really just inventory. Those particular backhoes were there because they hadn’t sold. We soon understood why. The backhoe had about 40 features that the customer could choose. One of these features was a cup holder for the driver’s cab. There were two options: a stationary cup holder and a rotating cup holder that could be stowed under the dashboard. If a customer order matched a unit on the lot EXACTLY, except for the cup holder, then their inventory control system treated the two configurations as different. A whole new backhoe was scheduled and built, while the one with the wrong cup holder continued to sit on the lot, exposed to wind and rain and interest charges. Now, probably they should not have had two different cup holders in the first place. But if they did, the cost of giving away a rotating cup holder instead of a stationary one (about $20) was much smaller than all of the costs involved in building a whole new backhoe. And if the customer wanted a rotating one, he could probably have been persuaded to accept a stationary one today instead of waiting 3 weeks to get exactly what he wanted.
Two different configurations of a complex product might be “almost the same”, or “very similar”, or “quite different”, or “far apart”. Can we quantify these common sense terms? Surely two configurations that differ only on a cup holder, out of 40 features, are “almost the same”. If we can measure closeness, then we can see when a configuration in stock is “close enough” to what a customer wants.
For two configurations to be close to each other, there shouldn’t be too many features with different choices. Furthermore, some features are more important than others. In a backhoe, the engine, hydraulics, and buckets are very important. The cab is quite important. The cup holder is not so important. So two configurations are close to each other if they differ on a small number of features that are not very important.
The criteria for what is important can be based on knowledge about the product and what it is used for, and also on the cost in relation to other features. So “close enough” depends on how many features are different and how important those features are,. There might also be a probability of acceptance that depends on how many features are different. This can all be formalized, and we can compute a number that represents the closeness. If we can measure closeness, then we can automate the matching of orders and inventory in a much more nuanced way than “the same” or “not the same”. For complex products that are built to stock as well as built to order, this can substantially reduce inventory holding costs by keeping the inventory moving. This is the key to inventory optimization.
Each of those backhoes in the park had a story to tell. Stories like: “I could have been sold in October of 2007, but I didn’t have the rotating cup holder. Or in January of 2008, but I had flip-guard feet instead of street-guard feet.
Someday my perfect order will come, I know it will.”
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Four Times the Sales Transactions With the Same Head Count!
Products have so many choices. Customers hate the experience of having to answer 50 questions to get to selection. Asking them 50 questions translates to poor customer experience and it is actually bad for you too. Here is why. Every sale begins with a customer calling out the top 3 or 4 items they want in a product. I wish there were big blinking lights that could now say “Big Opportunity!” This moment is the biggest opportunity for you to recommend a product choice that meets his needs, while being good for you as well.
In ‘days of yore’, that is what a good salesman would do. He would look over his shoulder and see what he had in stock, and gently guide the customer to one of those products. But those were the days when products were fewer, choices were fewer, and sales guys could look over their shoulder and see what they had in stock. Gone are those days! Today – sales is outsourced. It is one of the biggest and most critical functions’ that has been outsourced. Sales are done though dealerships, channels, etc. Even when a company has internal sales resources, the sales teams tend to be big or have heavy turn over. So – you have a newbie selling your product more often than not.
As product choices have continued to skyrocket, this poses a tremendous challenge on sales reps, sales channels and outside sales people. They lack the tools and automation to quickly hone in on customers’ needs and recommend product choices. The lack of these tools is causing tremendous challenges to sales people and customer. Here is a list of some of the outcomes:
- Poor customer experience due to inability to recommend good product choices at point of sale .
- Lost sale because customers do not like to answer endless questions to get to a product choice. More often than not – this is bad for the seller as well. Long list of questions result in random choice selections. This causes one-off products and poor product selection.
- Longer sales cycle – Every sales rep will tell you that he wants to recommend a good product choice and close the sale. The faster you can recommend a product, the higher the conversion rate.
Today’s sales tools and CRM include contact management and pipeline management. We do not expect the sales reps to remember the names and contact information of their customers. But why do we expect them to memorize the product features and choices? Why do we take them through endless product training sessions, even as the products are changing?
CRM needs product selection information to enable a sales rep to close the deal. Jane Barrett, Research Director at AMR presented some of the benefits, in her presentation to an SAP user group in October 2009.
Here is a short list of the benefits of empowering sales reps with guided selling at point of sale –
- Sales cycle time drops dramatically, from days to instantaneous.
- Increase in profit (2% in one case) by continuous up selling and offering alternatives at point of sale
- Four times as many sales transactions can be handled with the same headcount. (400% sales productivity improvement!)
If you do not use product recommendation tools, your sales staff is working at 25% sales productivity. In affect, you have 4x the staff you need to do the same job
Call To Action – Please talk to your sales reps and channels on how they are currently selling your product and their current challenges. If they do not have automation and tools to recommend product selection, you have a significant opportunity for improvement. A potential opportunity to improve sales productivity by 400% !
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Do Customer Buying Patterns Exist?
Customers have to make choices in order to buy configurable products. Do they make these choices at random, or are there patterns? When we look at the sales history for a configurable product, like a car or a computer, can we tell if customers have just been flipping coins and rolling dice? Or do their choices hang together and make sense? To answer this question, we would have to look at how they buy combinations of options. In the previous post, I took a pizza as a simple configurable product, and looked at how customers ordered pairs of toppings. Just by looking at the sales numbers we could detect that the selection of pineapple and Canadian bacon are not independent. Even if we had never heard of a Hawaiian Pizza, we could discover it in the data.
Even more information is hidden in combinations of three toppings at a time, or four toppings at a time. Any combination of toppings will have appeared on some of the pizzas that have been sold (or maybe none). The relative popularities of all the different combinations has a clear message: customers are not flipping coins. Some toppings naturally go together, and others do not. Pepperoni, broccoli, and anchovies is just unlikely. If a particular pizza restaurant has a few “house specials”, like the Meat Lovers and the Veggie Delight, we can see them in the data, even if we don’t know their names.
What is true of the pizza is also true of other configurable products: computers, trucks, tractors, lighting fixtures, industrial fans, and so on. All products that have variety. Customers make choices, but not by rolling dice. There are combinations that go together and combinations that do not. A pizza maker can juggle the preferences of his customers in his head. But when a product has 30 or more features, intuition is overwhelmed. The number of combinations explodes so fast that the unaided human mind can’t see the patterns. At this point, mathematical models and intense number crunching can reveal the patterns and let the product manager for a line of trucks be as confident as a pizza maker.
Do Customer Buying Patterns Exist?
Buying patterns are real, and they manifest themselves in how customers buy combinations of options. With the computing power we have available today we can detect and capture them. These patterns can then be used to design “house specials”, forecast future sales, and guide customers to what we want to sell them.
So, who else is talking about customer buying patterns?
Intel Talks about Changing technology buying patterns
Customer Buying Patterns have Changed. What’s Your Plan?
An entire report that summarizes the results of a consumer usage and purchasing pattern survey conducted in March of 2007. The survey was conducted with In-Stat’s Technology Adoption Panel (TAP) — a dynamic, online panel of more than 19,000 technology users and decision makers. Over 1,400 technology users responded to this focused survey.
Findings in this report include consumers’ time spent on PCs, when they last purchased a personal-use PC, the PC’s features/form factor/usage, the desired features of future PC purchases, changes in usage patterns, and consumers’ thoughts about new technologies.
The changing patterns include -
- When consumers are likely to make their next PC purchase.
- The features consumers state they want
- The features consumers state they really want, based on changes in their usage/buying patterns.
- How consumers view new technologies
However – buying patterns are constantly changing. As social networking grows, we are watching new markets emerge every day. There is gold for companies who can continually detect these patterns and offer the right products and feature mix.
Customer Buying Patterns – What you can learn From Pizza Sales
There are 1,140 ways of ordering a pizza with 3-toppings, if the pizza offers 20 choices
First order take rates tell us about the relative popularity of different options. For example, consider a small set of possible pizza toppings.
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Topping |
Take Rate |
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Pepperoni |
40% |
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Mushrooms |
20% |
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Pineapple |
3% |
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Canadian Bacon |
3% |
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Green Peppers |
10% |
Customer buying patterns really start with second order take rates, which tell us about pairs of options, or toppings. Second order take rates tell us about relative popularity, but they also reveal something deeper: dependence. If you know that a pizza has pineapple on it, there is a very good chance that it also has Canadian bacon. This is dependence. In this case the reason is that there is a widely known “Hawaiian Pizza” that has both of these toppings. In general, customers don’t flip coins or roll dice. They select options that “hang together” in some way. The patterns can be seen in the combined take rates. Let me illustrate with three examples that are contrived to illustrate some important points. First consider pepperoni and mushrooms together.
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Mushrooms |
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No |
Yes |
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Pepperoni |
No |
18% |
12% |
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Yes |
32% |
8% |
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In this table you can see that pepperoni has a 40% take rate, since 32% of pizzas have pepperoni without mushrooms, and 8% have pepperoni with mushrooms. In the same way, 20% have mushrooms, because 12% have mushrooms without pepperoni and 8% have mushrooms with pepperoni. This illustrates the first law of second order take rates: the first order take rates must be preserved. But this table contains no new information. Customers are apparently ordering pepperoni and mushrooms independently. This is revealed by the fact that 8% is exactly 20% of 40%. Knowing that a pizza has pepperoni does not give us any clue about whether or not it has mushrooms. Similarly, knowing that it has mushrooms is useless in guessing if it has pepperoni.
As a second example, consider the two toppings that are on every Hawaiian pizza: pineapple and Canadian bacon.
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Canadian Bacon |
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No |
Yes |
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Pineapple |
No |
97% |
0% |
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Yes |
0% |
3% |
For simplicity, I have made this an example of complete dependence: a pizza has pineapple if and only if it has Canadian bacon. Notice that the first order take rates (3% for each) are preserved.
The third example is the really important one: partial dependence. This is illustrated here by pineapple and green peppers.
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Green Peppers |
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No |
Yes |
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Pineapple |
No |
89% |
8% |
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Yes |
1% |
2% |
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In this case, the first order take rates are also preserved: 3% for pineapple and 10% for green peppers. But these two choices are not independent. The take rate for pineapple and green peppers together is 2%, which is much greater than 10% of 3%, which would be only 0.3%.
Exercise for the reader: show that if we know that the pizza has green peppers, then there is a 20% chance that it has pineapple. (Much greater than its 3% first order take rate.) And if we know that it has pineapple, then there is a whopping 67% chance that it has green peppers!
So second order take rates capture information about how customers are combining toppings, and we can use that information to make predictions.
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Is your sales history self-encrypting?
Emcien’s mission is to find the actionable intelligence that is hidden in the sales history of configurable products. We call this SKU intelligence. Many companies, however, save their sales history in a way that keeps any patterns hidden forever. I call this self-encryption. Many of these worst practices began as a way of saving space at a time when storage space was expensive.
A configurable product is one where the customer has to make choices to customize the product to his own particular needs or preferences. The valuable patterns are in the way these choices are made. The sales history should be at the right level of abstraction: in terms of the choices that the customer made. Here are four ways you may be encrypting your data.
- SKU Numbers. SKU numbers identify unique product configurations. They are a great shorthand for keeping track of what has been built and what is sitting in inventory. But if the sales history is kept in terms of SKU numbers, and the definitions of those SKU numbers are stored in a different place, then you may not be able to decipher your own history. By “different place” I mean a different database, different computer system, or anywhere that is not part of the history itself.
- Part Numbers. Customer orders get translated into Bills-of-Material (BOM) so that the requested item can be built and delivered. But what happens to the order afterwards? Often it is saved in terms of the part numbers. The customer ordered “2GB of RAM”, which became part 123-XYZ-645A. This was the right part number for 2GB of RAM from a certain supplier during a certain period of time. Remembering 123-XYZ-645A may be important for some warranty issues, but it is the wrong level of abstraction for understanding the customer. Many customers ordered “2GB of RAM”, but they got many different part numbers (different suppliers at different times). Part numbers change constantly, and unless a complete trail of part number changes and equivalences is maintained, a history in terms of part numbers is irretrievably fragmented.
- Standard Options. Most manufacturers make different models of their products, and the different models come with different “standard options”. The sales history doesn’t mention these options because there would be so much repetition (let’s save space!). The problem is that the set of standard options changes over time, even though the model names stay the same. Which options were standard on Model ABC in September 2007? Who remembers?
- Product Packages and Option Bundles. This is similar to the standard option problem. Some set of options is bundled together and sold as the “Sports Package” for some period of time. So the sales history says “Sports Package”. What was in the Sports Package in September 2007? Who remembers?
The sales history should be self-contained, with a record of each unit sold, expressed in terms of the options bought by the customer. If some options were implied by others, but could have been different, then they should be spelled out. If the data is saved in the right way, then the patterns in how customers buy the product can be revealed.
The difference can be dramatic. The message below appears to be gibberish.
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But suppose we use the key that Dan Brown uses in “The Lost Symbol”: the magic square discovered by Benjamin Franklin.
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14 |
3 |
62 |
51 |
46 |
35 |
30 |
19 |
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52 |
61 |
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1 |
64 |
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48 |
33 |
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17 |
Then we see the hidden message: “Emcien can easily find the hidden treasure in your sales history”.
The value is that customers are speaking to you when they buy your products. This is the true Voice of the Customer (VOC). But due to the data encryption issue, companies are blind to this intelligence. Unleash this intelligence, and you can drive higher sales and margin by serving the customer with the right choices.
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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.