Tag: sales history
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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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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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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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Stairway to (Product) Complexity (a.k.a. Why Do I have SO Much Stuff!!!)
In the last post I introduced two ideas about the sales history of any product. First, the number of unique configurations, or build combinations, depends on which features are included in its description. Second, the number of unique combinations drops whenever a feature is removed. A natural question to ask at this point is: Which feature, if it were removed, would lead to the greatest decrease in the number of unique configurations?
This is an easy question to answer, if we have a way of counting the number of unique configurations in any input set of configurations. This is really just a matter of sorting, so suppose we have such a counting algorithm. We try removing the features, one at a time. In each case we apply our counting algorithm to get a score. The score is the number of surviving unique configurations. The feature with the lowest score is the winner (like golf).
Now suppose that we permanently remove the winner, and repeat the contest again. This will determine a second winner, which can also be removed. We keep repeating until there are no features left. Now imagine a graph with the number of features removed on the x-axis and the number of surviving unique configurations on the y-axis. This is the stairway to complexity.
The stairway shown above is for a product (commercial stoves) with 23 features. The number of unique configurations starts at 1223 and drops to just 1 as the features are removed. The features are removed by looking for the biggest drop at each step.
Abstract:
Product complexity is driven by large number of options. Companies struggle to determine which feature choices are driving complexity. They typically “randomly cut choices” to streamline and rationalize SKUs. The cost of product complexity is tremendous on engineering. The current PLM systems do not have a method to measure this and provide intelligent feedback to engineers on how to standardize platforms to reduce engineering and maintenance costs. This article clearly details the metrics around product complexity and how to solve this issue.
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The Number of Choice Combinations Depend…
The number of choice combinations depend on which product features are included. The build combinations is the product mix or the marketing mix.
Let’s consider the sales history of our product. There are two very important numbers: the number of units sold and the number of unique configurations. The number of units is well defined, but the number of unique configurations is ambiguous. The ambiguity comes from the fact that there will be more unique configurations if we use more features, especially soft features, to describe our product.
One very special soft feature is a Serial Number, or VIN (Vehicle Identification Number). The whole purpose of the Serial Number is to make each instance of the product unique. So if we look at our sales history and include Serial Number, we will see that the number of unique configurations is exactly the same as the number of units of the product (instances).
If we want to begin to understand the demand for our product we have to see which instances are actually the same. That means we have to get rid of the Serial Numbers. When we do, the instances collapse into groups of now unique configurations; that is, unique without Serial Number.
If we are interested in the tangible features of the product, then we may want to take out other soft features as well. Geographic region is important for some purposes, but may be a distraction when we are interested in the physical product. Taking out the geographic region feature will cause another reduction in the number of unique configurations. The red, V8, convertible in Florida will get combined with the red, V8, convertible in New York.
Sometimes we are interested in the variants of our product ignoring color. We know that every real variant is going to come in several colors, but we want to look at the product without the distraction of color. This is sometimes called the “body in white”. So the red, V8, convertible and the green, V8, convertible collapse into the V8, convertible.
The point I am making is that the number of unique configurations depends on which features are included, and this number drops whenever a feature is taken away. Mathematically, this is called “projecting out the feature”.
The number of unique configurations is at most the number of units sold, and at a minimum it is just one. If we take away all of the features, then every unit looks the same, which means just one configuration. There is a path from one extreme to the other that we will introduce next time.
By the way – understanding this is important as product complexity is a key driver of process complexity.
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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.
How I want to buy a car
Every five or so years, I shop for a new car. I hate car shopping. The haggling, the long trips to dealerships way outside of town, the hours and hours of waiting, punctuated by furtive whispers to my husband, “Don’t give in! Stick to our budget! But don’t tell them our budget!” and similar. But that’s toward the end of the process. There’s a lot of work leading up to it.
First I hit the Consumer Reports site to research cars. A subscription is just $5.95 a month, but it auto-renews so you have to remember to unsubscribe or it quietly chips away at your wallet forever.
I find the five safest vehicles according to my car type and year. When I say new car, I just mean it’s new to me. I like to benefit from someone else’s new-car depreciation, which is something like 25% the minute you drive off the lot.
Anyway, I get on several different car sites like CarsDirect.com and AutoTrader.com to look for my next set of wheels. First I have to pick make and model, then enter my ZIP Code, then there’s a long list of cars. If I want to, I can see the list from lowest price to highest. The trouble is, I want to compare five different models and several different years. I’ve got to select the same filters over and over for all five and then compare the info. continue reading »
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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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The typical tail graph
In a previous post, I discussed two types of sales history: raw and collapsed. The collapsed sales history can be displayed in a table or spreadsheet, with a special column for volume. If this table is sorted on decreasing volume, then the most popular configurations (popcons) will be at the top. The graph with the volumes displayed in decreasing order (popcons on the left) is called the tail graph
We have drawn tail graphs for cars, computers, washing machines, lighting fixtures, trucks and tractors, and they all look basically the same. The first tail graph shown below is small but typical. It represents 2,884 tractors, with 1,997 unique configurations, or build combinations. On average, there are 1.44 units per unique configuration. The most popular configuration was ordered 23 times. The graph quickly drops to two of a kind and finally one of a kind (our technical terms are “twosies” and “onesies”). Combined, the onesies and twosies account for 2,000 tractors, or 69% of total volume. Rather high, though this number is usually at least 40%.
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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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