Tag: product management
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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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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.
Q&A with Mark Gottfredson, Bain & Company
In today’s post, we talk to Mark Gottfredson about product complexity and customer choice.
Emcien: It’s natural for companies to add products and features to keep customers happy. What are the downfalls?
MG: The challenge of adding complexity is it’s the most natural thing in the world. Marketing comes up with new ideas for products or configurations to get the next bit of market share or a little bit more share of wallet. But most companies aren’t so good at retiring products; they don’t have a similarly robust process for taking things out of the catalog that no longer sell, or sell only small amounts. They don’t do a good job of balancing.
Most decisions we make are based on incremental economics. Each decision makes sense in its own right, but the costs of complexity tend to grow systemically. You can’t tie them to a single product decision. Take tinted windshields, for example, that you can sell as an option for $120 and 40% of customers will buy. Assuming the costs of tinting the windshield including inventory impacts, etc., are $9, it will always make sense to add the option. By itself, it is a rational decision, but when coupled with hundreds of other decisions, we end up with dozens of options like power windows, 13 exterior colors, 10 interior colors, 7 different radio and speaker combinations, etc. Eventually, the vehicle can be made in 10 billion different ways, and you don’t know what the next order will be. Since you can’t effectively forecast anymore, you get frustrated and buy a $50 million forecasting module to try to manage all the complexity. You have difficulty balancing your lines, build inventory and increase supply chain costs. Unfortunately, when most companies finally decide to reduce complexity, they “cut off the tail” of low-running options or SKUs. But they don’t remove the systemic costs, and they don’t see any benefits.
Emcien: Companies often overestimate the value buyers place on having many choices. What are the downsides?
MG: Go to a banking website like Citibank or Bank of America. The site describes itself as a full-service bank that has all the items you could want. There are long lists of products like credit cards with different reward programs, as if to say, “We have a lot of products. Surely there’s one here for you. Good luck finding it.” High complexity is a priori evidence that you don’t know what your customers want.
Emcien: When do fewer choices mean higher sales?
MG: When you understand customers. Dell understands customers well. Dell’s website is Spartan; there are just a few choices. If you choose a desktop, up pops three computers: high, medium and low cost. These three configurations are what your segment – home, professional, government – wants. You can customize each one, but you’ll make it as expensive as the next higher model, so then you switch to that and you’re still buying a standard configuration. Every time I have seen complexity reduction done right, sales have increased.
Emcien: How do overoptimistic sales expectations help to spread complexity?
MG: What happens is sales looks for a gimmick that gets them the next sale. Many manufacturers think whatever’s thrown over the wall from product management and sales must be good to go. And sales thinks more is better! Engineers love to engineer; they’ll give you complexity. Most firms build complexity systematically into operations, and then they build systems to handle the complexity, and that’s high cost.
Companies should think about what business would be like with a zero-complexity baseline – how they would operate if they offered just one product or service. The purpose of zero-based thinking isn’t to eradicate complexity; it’s an exercise to reimagine the business with the optimum amount of complexity.
Mark Gottfredson is a director of Bain & Company’s office in Dallas, Texas, which he founded in 1990. Over the past 26 years, he has advised chief executives and top-level managers in a wide range of industries. Currently, he serves as the Global Head of Bain’s Performance Improvement Practice and is also a leader in the firm’s business strategy, airline, financial services, manufacturing and energy practices.
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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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Product management to fit?
I often hear clients say, “We’ve been told by management that we should only have a standard and premium model.” Then product teams are tasked to fit the line to that limitation, while no product analysis was done to support the decision.
While reduction in configurations is important, how it is achieved is just as important. Using a quantitative approach earlier in this decision-making cycle can help the management team make an informed decision. Maybe a reduction to two models is just too aggressive; adding one additional model might be the difference in having proper market coverage. Examination of customer buying trends and patterns should be the first indicator for product direction. Determining product strategy based on market needs as opposed to retrofitting the product to an idea could lead to more effective product lines for both the manufacturer and consumer.
Stop product complexity at the door
In any manufacturing company that builds configurable products, there is a lot of discussion around what product complexity is. What’s interesting is that when times are good and there are lots of sales, the discussion is usually around how to simplify or streamline with the goal to sell more product even faster, that complexity is keeping sales from going even higher. In bad times, the discussion typically moves to how complexity is causing undue stress on the supply chain, creating problems with parts forecasting, quality and finished goods inventory.
Rarely do these discussions end with participants really agreeing about exactly what complexity is or how to reduce it. Solutions are attempted with internal projects like SKU reduction and part number reduction initiatives driven by Six Sigma teams that mean well and do good work, but usually are chasing the tail of the complexity dog, rather than leashing it for good and guiding it to higher profits, lower forecasting errors, even shorter sales cycles.
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