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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.
The Myth of Build-to-Order
Are You Looking At Top Selling Choice Combinations Before Stocking Inventory?
In working with manufacturers of configurable products, we have never met one that did not claim that they only build to order. “We don’t build until we have an order in hand” they all say. At first we believed them. A whole generation of companies has been transfixed by Michael Dell. No finished goods inventory; don’t assemble the parts until you know exactly what the customer wants; get the cash before you build the product.
Dell can assemble a computer in three minutes. A truck or a backhoe takes a little longer. But the same ideas should apply! Right?
Dell builds computers for final customers who come to its web site or its 1-800 phone line. Most manufacturers of expensive, complex products are once removed from their final customers. Their immediate customers are dealers. Final customers go to distributors and dealers to buy backhoes, tractors, work trucks, lighting fixtures, industrial fans, and so forth. The “customer orders” that the manufacturers so proudly wave are not final customer orders, they are actually channel/dealer orders. (Okay, this is an exaggeration, some are actual customer orders passed through by the dealers.) And how do the dealers place orders? They guess. They choose combinations of 30 or more options based on their experience. One manufacturer we worked with kept referring to these as “Christian orders”. After a while we asked them what they meant by “Christian orders”. With a big smile they said “Oh, we just take them on faith.”
So the dealers order certain product choice combinations to stock based on their intuition, and those units sit on their lots until they sell. Sure looks like finished goods inventory. There is some ambiguity about who actually owns the inventory. The manufacturer will say that the inventory belongs to the channel or dealer, so it’s not my problem any more. The dealer will say that the manufacturer finances his inventory; some cases has to take back the units and give his money back if a unit sits too long. In any case, the manufacturer knows that the dealer is not going to order any more units while his lot is full of “stale inventory” or “lot rocks”. (See Chrysler’s desperate attempt to force its dealers to accept more cars in 2008.)
So Dell computers are built to order. (And now also built-to-stock for their new retail model for stores such as Best Buy and Wal-Mart.) Jumbo jets are also built to order. But most configurable products are still a combination of build-to-order and build-to-stock, with manufacturers and dealers playing hot potato with the inventory. This means that somebody should be looking at the history of what combinations sold in the past, and trying to make sure that the stuff that they build is the stuff that sells! Looking at the sales patterns and trends is a very fast, efficient and intelligent way to determine what to carry. This is a more reliable than believing in Christian Orders or relying on dealers’ intuition. The manufacturers should be giving the dealers guidance on what to stock and order based on their global visibility into sales and customer buying trends. Customers buy combinations of features, and they do this in predictable ways. Detecting and using the patterns can make inventory turn faster, even if, technically it doesn’t exist.
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.
|
Topping |
Take Rate |
|
Pepperoni |
40% |
|
Mushrooms |
20% |
|
Pineapple |
3% |
|
Canadian Bacon |
3% |
|
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.
|
|
|
Mushrooms |
|
|
|
|
No |
Yes |
|
Pepperoni |
No |
18% |
12% |
|
Yes |
32% |
8% |
|
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.
|
|
|
Canadian Bacon |
|
|
|
|
No |
Yes |
|
Pineapple |
No |
97% |
0% |
|
|
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.
|
|
|
Green Peppers |
|
|
|
|
No |
Yes |
|
Pineapple |
No |
89% |
8% |
|
Yes |
1% |
2% |
|
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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What does the stairway to complexity tell us?
If a product is too complex, where is the complexity coming from? Which features are causing the explosion in the number of build combinations? The stairway to complexity tells us where to look.
The stairway to complexity shows how the number of unique configurations drops as features are removed. Here is another stairway for a backhoe with 30 features.
The number of build combinations drops from 934 down to 1 as we remove the features. Behind the graph is the actual list of features in the order they were removed. In the table below, the features are ranked from 1 to 30, corresponding to the steps in the graph.
If we want to simplify our product, this ranking of the features tells us where to start. The greatest contributor to complexity is the Buckets, of which there are 34 different kinds. The number of build combinations would drop from 934 to 838 if we didn’t have to worry about Buckets.
Is the ordering of the features in the stairway the same as the ordering by number of options? The first feature in the stairway is certainly the one with the most options (34). But Tran_Control has the second largest number of options (9), and doesn’t appear in the stairway until step 15. So there is more going on than just the number of options.
The amount of complexity introduced by a feature depends not just on the number of options, but on the relative popularity of the different options. Having two options that are split 50% to 50% is much worse than if they are split 90% to 10%. (See earlier post: Entropy of a coin toss.)
Introducing a new feature only increases product complexity if it splits existing configurations that would otherwise be the same. One manufacturer insisted that his product was so complex because it was produced for many different countries. But the number of unique build combinations was exactly the same whether the Country feature was included or not.
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Q&A with John Sloan, former director, Jeep Brand Global Product Marketing
In today’s post, John Sloan talks about challenges dealers face in ordering inventory that best matches customer demand.
Emcien: Describe the Chrysler-Emcien initiative that examined dealers’ struggles with complexity in the ordering process.
JS: In a soft “push” market where volume is driven by heavy incentives versus the merits of the brand / model, managing cost is paramount. A key piece to focus on is product inventory. Dealers get roughly 60 days of no-interest floor plan. In a soft market, vehicles can easily sit for longer than two months before being sold, so it’s critical that vehicles be easy to order, stock and sell. Simple is better.
Emcien worked on a model to simplify the Chrysler PT Cruiser product mix. There were thousands of possible build configurations for the PT Cruiser, creating significant complexity for engineering and the assembly plant, as well as the supplier extended enterprise. Emcien’s ability to accurately forecast demand is invaluable for a complicated product line because it can assist with reducing the build configurations to those that best match demand. The PT Cruiser initiative validated the power of the Emcien inventory model.
Variation is valuable
Advances in interconnection technologies are driving an increasingly demand-driven market. Customers are learning to expect to get what they want, when they want it, how they want it. And they tell you in each and every interaction they have with your company, or not. In a demand-driven world, increasing product variation and complexity in your business model is inevitable. Left untended, your business can become a tangled web of counterproductive business strategies with a dense portfolio of product families comprising thousands, even millions, of variants.
However, make no mistake, variation is valuable. To deny complexity or view the long tail of product variation as a management failure is to deny diversity of the world in which we make our living. Eliminate complexity in your product offer and you will find yourself competing with boatloads of product from China, India or any of a number of low-wage production markets.
The “keep it simple” principle is the root of good management. However, as Oliver Wendell Holmes, Jr. has observed, “I would not give a fig for the simplicity this side of complexity, but I would give my life for the simplicity on the other side of complexity,” it matters which form of simplicity you choose. The wrong simple answer is to try to focus on the 20% of product variants that make up 80% of your revenue, the head of the ubiquitous Pareto distribution, and find ways to minimize or eliminate the so-called unprofitable remaining 80% of product variants that lurk in the tail. Hello commodity, goodbye margins. The right simple answer is to deliver Intelligent Variation based on the voice of the customer shouting through the many interactions they have with you each and every day.
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What is sales history, exactly?
We often talk about the sales history of a product, so let’s explain exactly what it means. There is a raw sales history and a collapsed sales history. The sales history, raw or collapsed, is the starting point for all the analytics we will be introducing later.
Raw sales history
A product is a collection of features, where each feature has a set of mutually exclusive options (one of which may be “no,” “none” or “none of the above”). A sales history consists of a record for each unit of the product that has been sold, with a list of the options that were included. Since each record is for a specific unit, there may be a serial number feature. So imagine a table with a row for each unit sold and a column for each feature. The entries in a column are the different option choices for the corresponding feature. Blank cells indicate a “none” choice.
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Help the sales team help the customer
This morning I was talking to the VP of business process improvement for a company that sells industrial machinery. Their products are highly configurable. She told me that every year they have 50% new configurations they have never seen before. The number of choices on their products has grown over time. ”A salesperson can’t know everything about the product,” she said. “Customers want a few choices, and before you know it, the quote has crept into a configuration that’s bad for the customer and bad for us. “
As the VP explained, the biggest opportunity for complexity management is at the point of taking an order. A customer wants to be guided to complete their order. This concept is called Demand Shaping. There are myriad ways a configurable product can be ordered. However, each customer cares only about a few features that are of high importance to him or her.
Extending the product configuration to gain insight
One of the most important components in choice complexity is the product configuration itself, the mixture of product options that give a product its unique signature. Obviously the typical product orderable options are needed to analyze the complexity of a product, but other more abstract options can offer surprising insights into product and customer behaviors.
A typical car configuration has options such as sedan, V6 engine, automatic, blue, cloth, AM/FM/CD, sunroof. But more abstract items can be recorded along with these to offer more insight. Sales type can be recorded to analyze what types of product configurations sell better in promotional sales events as opposed to normal sales transactions. An attribute to record an extended factory warranty option may provide new ideas for packaging options together with additional warranty services that customers are moving towards.
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