How Good Is Your Demand Intelligence?

Published December 18, 2009 by John Maller

I was meeting with Martin, the CEO of a fortune 1000 Company.  He talked about sales productivity as a significant opportunity and an area of strategic focus for 2010.  “We have been selling this product for years. Yet, every day is ground hog’s day for our sales reps. What I mean by that is every sales call and quote request is treated as if we have never sold this product before!”.

Maximize Sales Productivity On Every Channel With Demand Intelligence

Maximize Sales Productivity On Every Channel With Demand Intelligence

In the B2B markets there are distinctive patterns in the product choices that customers make. It isn’t really customer intelligence as much as demand intelligence. The B2B markets are different from B2C. The average purchase value is typically larger, the frequency of the same end-customer is lower, but there are distinctive patterns in the product purchases. The purchase patterns exist by product choice combinations, customer type, vertical, usage, geographic region, price point and so on.

“I know that there are patterns in what our customers buy,” added Martin. He was previously the VP of Sales and has knowledge of what products customers buy. “We need our sales reps to have access to that intelligence so that they can be better advisors to customers and close the deal faster.  I have sat in sales meetings with a company exactly like the one we sold to 3 months ago, and watched my sales rep grill the prospect on product requirements. That hurts our sales more than anything else.”

In a recent blog, Michael Gerard from IDC wrote a very interesting article on the same topic. He mentions a story where a CIO from a $10B+ company had to continuously teach a vendor sales reps what he had purchased from them in the past. The article goes on to state that this can lead to poor credibility on the sales front lines.

There is a solution for this problem. Every company is sitting on tons of sales data. It is a wealth of data that can reveal what their customers are buying and where they are willing to spend their money. Emcien offers analytics that auto-detects the choice combinations in sales data. What are they buying? What are popular product choice combinations? What are popular choice combinations by vertical? What combinations are profitable? Which ones are not?  This is the demand intelligence that makes each day NOT be ground hog’s day!!

As a first step, it is invaluable to arm your sales reps with this intelligence so that they can be smart on every deal. In fact, every sales channel can benefit from this intelligence. Here are a few examples -

  • Sales reps can use demand intelligence to be better advisors to customers, convert requests to quotes faster and recommend good choices to close the deal. Even a simple report on what is the fastest selling product choices will empower your sales reps, and drive to the bottom line.
  • Your ecommerce site can use demand intelligence to quickly recommend the best products to help the customers to self-serve on and make better decisions.
  • Inside sales team can use demand intelligence to quickly complete quotes and close the deal.  I was talking to an inside sales rep and he told me that his biggest challenge is quickly responding to a quote with a price.  ”We have done this so many times before,” he said. “Why does it take us to long to get a price?  We lose deals because we cannot respond fast. The first to respond with a quote locks in the deal 90% of the times, even if the price is higher. We lose deals by being slow.”
  • Call centers can use demand intelligence to cross sell/up sell based on buying patterns. The turn over in call centers is high. Automating the cross/up sell with demand intelligence will dramatically improve productivity and profit.

Quoting Michael Gerard “This is only the tip of the iceberg of course.” Demand intelligence can dramatically improve your sales performance, customer satisfaction and profit margins.

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Gartner: Cloud computing, Analytics Top 2010 Strategic Tech List …

Published December 14, 2009 by John Maller

Cloud computing and analytics have jumped front and center. Gartner renamed Business Intelligence (BI) to Analytics.

On the analytics front, Gartner said in a presentation: “We have reached the point in the improvement of performance and costs that we can afford to perform analytics and simulation for each and every action taken in the business. Not only will data center systems be able to do this, but mobile devices will have access to data and enough capability to perform analytics themselves, potentially enabling use of optimization and simulation everywhere and every time. This can be viewed as a third step in supporting operational business decisions.”

Analytics Named as the #2 most important category, behind Cloud Computing

Analytics named as the #2 most important category, behind Cloud Computing

Gartner names analytics as the #2 most important category, only behind Cloud Computing. Analytics is becoming more important to all aspects of every business.

So what is analytics? It is purposeful focus on data. It requires mathematical analysis and algorithms on data, to compute Key Performance Indicators (KPI) that are valuable to measure the condition of the business.

Emcien offers analytics to detect product-buying patters with a focus to improve product mix and profitability. As product choices and market segments have grown to dizzying levels, companies struggle to have a finger on the pulse of their product mix, markets and profitability. What products are making money? What are customers buying? What are popular choice combinations? What is common across the market segments? What are the trends by market segment? What product choices should we offer?

Emcien’s analytics answers these questions with patented mathematical algorithms applied to sales data. Emcien’s analytics auto-detects what choices combinations customers are buying, the trends and which choice combinations are profitable. The product offering is the lifeblood of a company. At Emcien we equate product mix and choice mix to profit.  If your product mix is not aligned with what customers are buying, you will not make profits.  It is as simple as that!

Emcien’s analytics is built on sales data. Unlike web analytics, where is data is a bear, getting sales data is a relatively easy. This is usually the best quality data compared to anything else. The sales data is a true capture of the voice of the customer. It allows companies to see what customers are spending their money on. The value of Emcien’s analytics is visibility into customer choices, and more importantly, recommendations to increase profitability.

Analytics are very powerful as the data reveals Key Performance Indicators that offer continuous insight into the business. You can dispel a lot of beliefs that the company has based on gut feel and cooler talk. When you provide actual sales data driven insight, it has a profound positive impact on the company and the decisions. To convert analytics to action select KPI’s that are a part of your business process and budgeting process. This is key to showing improvement that is meaningful to the organization. As analytics gets more visibility in the C-suite, there is demand to produce KPIs that executives are measured on. This will help gain buy-in at executive levels as adoption of analytics grows.

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Follow the Money!

Published December 1, 2009 by John Maller

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!

Follow The Money

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.

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Demand Sensing And Demand Shaping

Published November 3, 2009 by John Maller

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

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 –

  1. 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!
  2. 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.
  3. 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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The Myth of Build-to-Order

Published October 26, 2009 by John Maller
inventory

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.

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Does Your Inventory Look Like Jurrasic Park?

Published October 25, 2009 by John Maller
dinosaur-park

Does Your Inventory Look Like Jurrasic Park?

I was working with a industrial manufacturer who made construction equipment, such as diggers and backhoes. They had a beautiful park-like setting around their factory. Artfully placed on manicured lawns around the ponds and fountains were quite a few backhoes . It reminded me of Jurassic Park. We discovered later that these backhoes were a lot like the dinosaurs – they had failed to adapt.

The backhoes in the park, though attractively displayed, were really just inventory. Those particular backhoes were there because they hadn’t sold. We soon understood why. The backhoe had about 40 features that the customer could choose. One of these features was a cup holder for the driver’s cab. There were two options: a stationary cup holder and a rotating cup holder that could be stowed under the dashboard. If a customer order matched a unit on the lot EXACTLY, except for the cup holder, then their inventory control system treated the two configurations as different. A whole new backhoe was scheduled and built, while the one with the wrong cup holder continued to sit on the lot, exposed to wind and rain and interest charges. Now, probably they should not have had two different cup holders in the first place. But if they did, the cost of giving away a rotating cup holder instead of a stationary one (about $20) was much smaller than all of the costs involved in building a whole new backhoe. And if the customer wanted a rotating one, he could probably have been persuaded to accept a stationary one today instead of waiting 3 weeks to get exactly what he wanted.

Two different configurations of a complex product might be “almost the same”, or “very similar”, or “quite different”, or “far apart”. Can we quantify these common sense terms? Surely two configurations that differ only on a cup holder, out of 40 features, are “almost the same”. If we can measure closeness, then we can see when a configuration in stock is “close enough” to what a customer wants.

For two configurations to be close to each other, there shouldn’t be too many features with different choices. Furthermore, some features are more important than others. In a backhoe, the engine, hydraulics, and buckets are very important. The cab is quite important. The cup holder is not so important. So two configurations are close to each other if they differ on a small number of features that are not very important.

The criteria for what is important can be based on knowledge about the product and what it is used for, and also on the cost in relation to other features. So “close enough” depends on how many features are different and how important those features are,. There might also be a probability of acceptance that depends on how many features are different. This can all be formalized, and we can compute a number that represents the closeness. If we can measure closeness, then we can automate the matching of orders and inventory in a much more nuanced way than “the same” or “not the same”. For complex products that are built to stock as well as built to order, this can substantially reduce inventory holding costs by keeping the inventory moving. This is the key to inventory optimization.

Each of those backhoes in the park had a story to tell. Stories like: “I could have been sold in October of 2007, but I didn’t have the rotating cup holder. Or in January of 2008, but I had flip-guard feet instead of street-guard feet.
Someday my perfect order will come, I know it will.”

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Four Times the Sales Transactions With the Same Head Count!

Published October 19, 2009 by John Maller

4x-sales

Products have so many choices. Customers hate the experience of having to answer 50 questions to get to selection. Asking them 50 questions translates to poor customer experience and it is actually bad for you too. Here is why. Every sale begins with a customer calling out the top 3 or 4 items they want in a product. I wish there were big blinking lights that could now say “Big Opportunity!” This moment is the biggest opportunity for you to recommend a product choice that meets his needs, while being good for you as well.

In ‘days of yore’, that is what a good salesman would do. He would look over his shoulder and see what he had in stock, and gently guide the customer to one of those products. But those were the days when products were fewer, choices were fewer, and sales guys could look over their shoulder and see what they had in stock. Gone are those days!  Today – sales is outsourced. It is one of the biggest and most critical functions’ that has been outsourced. Sales are done though dealerships, channels, etc. Even when a company has internal sales resources, the sales teams tend to be big or have heavy turn over. So - you have a newbie selling your product more often than not.

As product choices have continued to skyrocket, this poses a tremendous challenge on sales reps, sales channels and outside sales people. They lack the tools and automation to quickly hone in on customers’ needs and recommend product choices. The lack of these tools is causing tremendous challenges to sales people and customer. Here is a list of some of the outcomes:

  • Poor customer experience due to inability to recommend good product choices at point of sale .
  • Lost sale because customers do not like to answer endless questions to get to a product choice. More often than not – this is bad for the seller as well. Long list of questions result in random choice selections. This causes one-off products and poor product selection.
  • Longer sales cycle - Every sales rep will tell you that he wants to recommend a good product choice and close the sale. The faster you can recommend a product, the higher the conversion rate.

Today’s sales tools and CRM include contact management and pipeline management. We do not expect the sales reps to remember the names and contact information of their customers. But why do we expect them to memorize the product features and choices? Why do we take them through endless product training sessions, even as the products are changing?
CRM needs product selection information to enable a sales rep to close the deal. Jane Barrett, Research Director at AMR presented some of the benefits, in her presentation to an SAP user group in October 2009.

Here is a short list of the benefits of empowering sales reps with guided selling at point of sale –

  1. Sales cycle time drops dramatically, from days to instantaneous.
  2. Increase in profit (2% in one case) by continuous up selling and offering alternatives at point of sale
  3. Four times as many sales transactions can be handled with the same headcount. (400% sales productivity improvement!)

If you do not use product recommendation tools, your sales staff is working at 25% sales productivity. In affect, you have 4x the staff you need to do the same job

Call To Action – Please talk to your sales reps and channels on how they are currently selling your product and their current challenges. If they do not have automation and tools to recommend product selection, you have a significant opportunity for improvement. A potential opportunity to improve sales productivity by  400% !

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Do Customer Buying Patterns Exist?

Published October 15, 2009 by John Maller

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.

buying-3

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

As buying patterns change, Intel’s GCC GM Samir Al-Schamma talks about Intel’s growth markets and looks at its latest business processor and explains the changes introduced. With the new platform requiring a major upgrade, Rob Jones asks if companies really have the appetite to spend the money up-front in these difficult market conditions.

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.

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Customer Buying Patterns - What you can learn From Pizza Sales

Published October 12, 2009 by Roy Marsten

There are 1,140 ways of ordering a pizza with 3-toppings, if the pizza offers 20 choices

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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Is your sales history self-encrypting?

Published September 30, 2009 by Roy Marsten

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.

  1. 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.
  2. 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.
  3. 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?
  4. 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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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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