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“This Sells with that” for Aftermarket Parts
The SKUs (stock keeping units) in an aftermarket business is nothing but the various components of an automobile, tractor or backhoe. Every new model that comes into the market sees an addition of 2,500-3,000 parts to the existing master list, while the addition of every variant adds another 500-1,500 parts. The last forty years has seen a phenomenal growth in the number of aftermarket parts. This phenomenal growth has been fuelled by the liberalization of global players resulting in thousands of models of cars, tractors and other machines.
Frequent launch of new models has reduced product life cycle and has negative implication on the aftermarket supply chain. The implication of shorter life cycle of automobiles on the aftermarket business is faster transition of parts from runners to repeaters and soon fading into obsolescence. The revenue from parts of a new model are typically low in the first two years after launch, increasing in the third and fourth years. The sales only stabilize if the model continues in the market. Unlike other businesses where a product is discontinued if it does not yield revenue, in an aftermarket parts business the part needs to be supplied even if the model has failed. This tremendous volatility impacts the number of parts the aftermarket business has to support.
For example, if an OEM introduces a new model and two variants each year – this results in about 3,000 parts. In six years time the parts proliferation in the aftermarket business of this OEM will be least 30,000 parts. This explains the relevance of having a SKU management for the aftermarket business in order to track the movement of parts till it reaches obsolescence.
Levering “this sells with that” Intelligence from Invoices
The challenges due to inventory and assortment planning for the aftermarket parts are significant. Traditional forecasting methods that treat each part as an independent entity are outdated. For aftermarket parts, the performance of traditional forecasting methods is dismal, and hence not all the parts can be forecasted. In fact, only five percent of the master list can be forecasted with an accuracy of 75 percent on a monthly basis. This results in bloated inventory, incorrect parts assortment and lost sales.
OEMs, distributors and retailer have realized the importance and complexities of the spares business and have initiated measures for assortment planning and SKU management. This requires sustainable methods as the market grows and competition gets fiercer. Every OEM has mentioned how the downturn has reflected in increased sales for the aftermarket/service parts. A part of the business that was typically ‘a nice to have’ has become a focus for improved customer service and profit.
"This Sells With That" For Aftermarket Parts
The aftermarket business deals with SKUs in tens of thousands. Unlike soft goods, in this business customers buy parts for projects and jobs. The invoices reflect that, and are comprised of parts bought by maintenance and repair shops to get a job done. Hence, the affinities in the sales transactions reveal what parts that are bought together for repairs/jobs.
The sales transaction data for the aftermarket businesses is very rich with parts affinities and trends. That’s because the data reflects the buying patterns of repair shops and mechanics. This represents a significant opportunity to leverage the transaction patterns for assortment planning, inventory management and of course, ‘suggestive selling’.
Emcien offers analytics that reveals patterns in sales transactions, producing a complete data map of the item affinities for ALL parts. This intelligence can be input into traditional warehouse planning and retail assortment planning systems. The affinities data can be used to make traditional systems smarter as the sales patterns change, parts change and market shifts.
The current sales patterns and item affinities in planning systems are input manually using manufacturer recommendations and gut feel. These recommendations get stale very quickly and are rarely based on actual sales invoices. The value of analytics driven affinities is that the relationships are always up-to-date, backed by actual sales transactions. Emcien’s analytics produces affinities data for all parts in the supply chain eliminating the need for ‘hard coded’ rules that quickly become obsolete and are a nightmare to maintain. This enables aligning parts inventory with with sales/model changes/parts utilization based on actual invoices. The affinities data can also be use for suggestive selling to increase customer service and order size.
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Revealing Patterns of Change
This is Fun, But Not When You Are Under the Gun!!!
Gartner has launched a new focus area called “Pattern Based Strategy”, based on the need of businesses to capitalize on large amounts of data and the new rules for business process adaptation.
Here is a great verbatim quote from the Gartner web page.
The depth of the recent recession blindsided most businesses. As the economy starts to recover, many business leaders are thinking, “If I had seen this coming sooner, I could have acted faster, decreased my risk and enhanced my opportunities for growth.” There is a way to see things coming. It’s a framework for proactively seeking and acting on the early and often-termed “weak” signals forming patterns in the marketplace. It’s also about the ability to model the impact of patterns on your organization and identify the disciplines and technologies that help you consistently adapt. It’s called Pattern-Based Strategy.
The key to Pattern Based Strategy is automatically revealing intelligence that is hidden in the data/information. Companies today are running more lean than ever before. Employees across all organizations are inundated with work and overloaded with data. . There is a great need for technology that will make our jobs easier and make us more productive. At Gartner, the idea that emerged, led by Yvonne Genovese, is called Pattern-based Strategy (PBS).
We are victims of too much information, missed opportunities and ‘@#$% I wish I could have seen that!‘ moments. Connecting this to a rather timely/charged topic – Think about a recent attempted terrorist attack by the Nigerian traveler who bought a one-way ticket, paid in cash, checked no bags, boarded an international plane. There were a very large number of ‘red flags’ in the sequence of events, and there was a large volume of data hiding all this intelligence. A Hope Strategy is to hire tons of people and make them search the data for red flags, more importantly sequences of red flags. This may work sometimes. But it is a poor and expensive strategy, and rarely does it produce the desired results on time! (making it quite useless, actually!)
As companies start to incorporate intelligence from data into their operations, one of the primary issues is the ability to have the intelligence automatically come to you. ‘Digging for insight’ is a poor, time consuming, expensive strategy. We need the technology to work for us. Second, it is also important to start focusing the insight with a particular business function/strategy in mind. Sales, Marketing, Operations, etc.
Connecting this back to what we do, Emcien provides analytics that automatically reveal customer buying patterns in sales data. The analytics reveals the popular choice combinations, key differences by region, key trends and new emerging segments. This is an example of technology working for you, bringing insights back so that you can act on it.
Quoting a Regional Practice Manager and the Senior Architect for Siebel -
Emcien offers rigorous and repeatable detection of buying patterns, enabling your customers to act on them, while supporting your product objectives (margin, inventory, velocity, …)
Quoting a former Oracle Practice Manager and Senior Siebel Architect -
Emcien offers rigorous and repeatable detection of buying patterns, enabling your customers to act on them, while supporting your product objectives (margin, inventory, velocity, …). Emcien’s offerings readily integrate with Siebel, enabling immediate improvements to revenues. Few projects offer such potential for improving the customer experience and increasing revenues, with so relatively little development or integration efforts.
Automatically revealing patterns is required today as we all drown in data, and do not have time to hope that someone may find the intelligence that the organization needs to act on. Thanks to Gartner for launching this focus area!
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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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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.
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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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.
Part I: Reporting, Business Intelligence, Data Mining, Analytics: Actionable Tasks!
Software vendors use so many big words and confuse customers. Our customers have often asked us to clarify – so here I go. The definitions in this article are based on research of these terms, and the collective opinion of many of our customers and prospects. Over numerous conversations with our customers and the discussions of the terminology, the clarifications always go back to the origin of the terms and then move on to change in usage. Hence this article folows that flow. I would love your feedback as it is important to help buyers understand this.
Business Reporting
Business Reporting, as the term suggests presents the data from the database in an easy to read format. This originated when business users were frustrated that all the data was locked up in databases. There was a lot of data, but no one could get access to it without calling on IT folks. Hence Business Reporting was born.
Business Intelligence
This is a fancy name for business reporting. Business intelligence (BI) is a broad category of technologies that allows for gathering, storing, accessing and analyzing data to help business users make better decisions. In a 1958 article, IBM researcher Hans Peter Luhn used the term business intelligence. He defined intelligence as: “the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal.”
In 1989 Howard Dresner (later a Gartner Group analyst) proposed Business Intelligence as an umbrella term to describe “concepts and methods to improve business decision-making by using fact-based support systems.” Then in the late 1990s the usage became widespread (Remember the Bubble!). Then. everything with any data reporting was called Business Intelligence. So today, Business Intelligence is a glorified term for “Business Reporting”.
Data mining
Simply put, Data mining is hitting the data with all mathematical methods available to a mathematician! The data source can be almost anything – news papers articles, financial reports, sales data, medical data, … . This means that the data can have structure or can be un-structured. And the mathematical methods that can be applied can include neural networks, genetic algorithms, statistics on steroid and anything else they can think of.
One may ask – why are they doing this? What are they mining? Well, the simple answer is that they are mining the data looking for patterns; any patterns that can reveal relationships. So the methods used are varied and the kinds of data that are mined can come from a myriad of sources.
The results of data mining are lots of data! In fact – the result of Business Reporting and BI has been data overload. Now that’s the bad news. In a world of information overload, the last thing that we need is more data. We have less time today than we have ever had before. Business users do not need more data. They need quick conclusions on what the data is saying, converted into actionable tasks. Simply put – “Please tell me what to do”.
… More on the discussion of analytics to action in the next blog.
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Take Rates – What are the most popular product choices?
I want to apply the discussion of entropy to the features of a configurable product. But first we have to introduce the important concept of a “take rate”. In different industries this is called an “attach rate”, or a “penetration rate”. The idea is very simple: the take rate of an option is the fraction of units sold that include that option.
The take rate of option x is the number of units sold with option x, divided by the total number of units sold. So if 70% of our cars are sold with cloth seats and 30% with leather seats, then cloth has a take rate of 0.7 and leather has a take rate of 0.3.
In the case of a feature with two options, like cloth and leather, this looks just like a coin toss with two options, tails and heads. Recall that coins may not be fair. If I send you a message about a customer’s choice of seat, the entropy of that message is the same as for the outcome of one toss of a suitably biased (.3 to .7) coin. So take rates can be interpreted as probabilities.
Some features have more than two options. For example a backhoe feature called Feet has four different options: none, Flip, Flip Guard, and Street Guard. Each of these options has a take rate, and as long as we include the “none” option, these take rates have to add up to 1.0. So perhaps 30% of customers do not order Feet, 40% order Flip, 20% order Flip Guard, and 10% order Street Guard. The take rates are 0.4, 0.3, 0.2, and 0.1, respectively, which add up to 1.0.
With four options we lose the connection to coin tosses. We could use a loaded die to talk about features with six options, but an all purpose metaphor is the roulette wheel. Think of a spinning roulette wheel, or a stationary wheel with a spinning arrow as in many children’s games.
The wheel represents a feature, and there is a pie-slice for each option. The size of the pie-slice is proportional to the take rate. An example is shown above for the Feet feature of our backhoe. We can simulate a customer’s choice by spinning this wheel (or spinning an arrow). With this metaphor we can have any number of options, with any take rates. The “none” choice must be included to get a full pie (or there may not be a “none” choice).
To summarize, a product is a collection of features. Each feature has some mutually exclusive options, each of which has a take rate. These take rates add to one.
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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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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