Reviews. They're everywhere these days. You can create online reviews for music, products, websites, restaurants, doctors, and just about anything else you can think of, on sites ranging from Amazon to Zappos. What used to be the domain of the "experts" has come to the masses.

Reviews, reviews, reviews...

In my first blog announcing QlikMarket, I expressed my surprise at how seriously our partners were about being among the top 5 rated solutions on the site. But as I thought and talked more about it with my friends and colleagues, it became less and less surprising. The reality is, in this age of Internet shopping, reviews become really important when you can't touch, feel, or see whatever it is you're about to buy.

I know this from personal experience. I buy a lot of things from Amazon.com (my UPS delivery man could find my house in his sleep), and for things that I'm not intimately familiar with, I rely on the reviews to guide me to a decision. Reviews are the web equivalent of answering the “What do you think about <insert product here>?” question, but typically without being asked explicitly. And more often than not, the consensus is right. The voice of the community allows us to ignore the ads and the hype and focus on how the product or service performs in real life.

The QlikMarket team had a dilemma to face when we launched the site: do we allow partners to review their own products? In the end, we decided that yes, as a seeding mechanism, it would be okay (although we do state it explicitly in the review if there’s an affiliation). But really, who is going to give their own product anything but a 5-star rating? So, the system really only works when the community gets involved; that's when the real value of reviews comes into play.

QlikView has a rich community. A plugged-in, intelligent community that knows what they're looking for and how they'll use it. As part of that community, we encourage you to tell others about your experience with a solution via QlikMarket’s review function. Did you love that solution? Tell the masses! Did you dislike it? That's okay, too, so long as you provide a brief explanation of what the shortcomings were. Not every solution is going to click with every customer; it's the way of the world. And a constructively-written review not only helps other potential customers, but also helps our partners learn how to improve their products. Even great applications can benefit from improvements.

Our hope is for QlikMarket to become the go-to site when customers need to extend their QlikView ecosystem with new applications, extensions, and connectors. Part of making that happen is ensuring that users have the ability to make informed decisions about the solutions they acquire, and I believe reviews are key to making that happen.

So what do you think? How heavily do you rely on reviews to help you make purchasing decisions? And how likely are you to leave a review for a product you love? For one you hate? Or one you're simply not happy with? Because I can’t be the only one, can I?

Henric Cronström

The Unclearly Said

Posted by Henric Cronström Nov 27, 2012

“What you cannot clearly say, you know not.
With the thought, the word is born on a man's lips:
the obscurely uttered is the obscurely thought.”


These lines were part of an epilogue read by Esaias Tegnér at the Lund University graduation in 1820. The last line is famous in Sweden and has become a proverb in its own right:


“Det dunkelt sagda är det dunkelt tänkta.”



Tegnér was professor of Greek in Lund and one of greatest writers in Swedish history. The statue on the picture below is found in central Lund, not far from the statue of Nothingness.


He was at the time known not only in Sweden but also abroad. The American poet Henry Wadsworth Longfellow translated some of Tegnér’s works to English and at Tegnér’s death Longfellow wrote Tegner’s drapa, a poem that praised the man and his Esaias_tegner_lundagard.jpgworks.


Tegnér’s words at the graduation were an attack on the Phosphorists, a neo-romantic group with metaphysical ideals. Tegnér himself was more down to earth and believed in observations of nature and a scientific approach.


So basically Tegnér was saying to the students not to trust unclear speech because it is a sign of dim thoughts or lack of facts. This message is simple enough and very relevant still today.


What has all this got to do with QlikView? At first glance, nothing.  But in fact, quite a lot.


People all over the world tend to rightfully distrust obscure speech, irrespective of whether they have heard of Tegnér or not. Further, an implication of Tegnér’s message is that you shouldn’t speak or act until you get your thoughts and facts straightened out. And when putting it this way, the analogy becomes obvious: get your facts right, get your thoughts straightened out – only then are you ready to communicate what you’re thinking. Only then can you act wisely.


In a professional situation where a database is a major source of information, you need to use the data not only to find facts and answers, but also to find the questions. You use your gut feeling to create ideas and you use the data to refine the ideas into knowledge. Or, to discard the ideas, if facts show that the ideas are wrong. You need to be able to play with the data, to turn facts around and look at them from different angles before you can say that you understand the data. And you need to understand the data before you can talk smartly about it.


This is what Business Discovery is all about: helping you to prepare before you speak, act, or make a decision.  It is the process of going from the dark to the light, from the unknown to the known, from ignorance to insight. It is the process of going all the way from a blank mind to a substantiated allegation.


Because - The Unclearly Said is The Unclearly Thought.



One of my favorite famous quotes is “Chance favors the prepared mind.” Louis Pasteur is credited with saying this about the field of observation but it applies in so many other areas of life, as well.  I was thinking about this during the Orionid meteor shower recently. I wanted to be lucky and see a “shooting star.” On a Saturday night in mid-October, forecasters predicted that skies would be clear and the meteor shower would be visible in the northeastern U.S.

shooting star.jpg

There were no guarantees but there were things I could to do set myself up for the possibility of seeing some meteors—like going outside at night. And once outside, there was still no guarantee I would see a shooting star but at least I could make it possible by laying back and looking skyward. Sure enough, that night I saw six or seven meteors, some of them huge and bright.

As I was watching the meteor shower I thought about chance favoring the prepared mind, and about Frans Johansson’s book, The Click Moment: Seizing Opportunity in an Unpredictable World. In this book, the author makes the argument that “...success [whether that be a career, product or service, or entire organization] is random, far more random than we have come to believe.” Why? Because “if the rules can change at any time, then it is impossible to predict with certainty what strategies and tactics you must use to succeed” and  “If we pursue an approach that we know has worked in the past, we can be certain that competition will be fierce; others will pursue it as well.” Not comforting thoughts for those of us who pride ourselves on our logical, rational approach to life!

But Johansson recommends actions we can take to maximize our chances of success in a chaotic world. And these have great implications for Business Discovery.

  • Create opportunities for serendipitous discovery. Johansson says that interconnectedness “. . . increases the frequency of serendipitous encounters and unexpected insight and enables far greater rates of innovation.” He was talking specifically about interconnectedness among people but his insight also applies to data. Bring together data from sources that have been up until now disconnected and see what happens. “Our minds tend to keep separate concepts apart. It requires something unusual to spark the connection,” Johansson said. Seeing the data all in one place, and seeing all the relationships in this data, can be mind-bending.
  • Spend some time exploring. By definition you don’t know what—if anything—you’re going to find when you go exploring. It could be something great, it could be something small, and it could be nothing at all. But even if it’s nothing at all, perhaps while you’re heading down a path you’ll see something that triggers a thought about a new direction to try next time. Give your mind some space to wander. Explore your data, looking for outliers and unexpected relationships. “We are conditioned to search for similarities, not differences.” If everyone else is looking for similarities, and you discover an important difference, you may stumble on to something big. Click around in your data to give yourself a chance to make a discovery. “Unique insights are one of a kind; they are random, unexpected, and serendipitous.”
  • Keep an eye outside your primary area of inquiry. “When you focus on one thing exclusively,” Johansson said, “you miss everything else that’s going on around you. You become oblivious, in a sense.” What does this mean for making discoveries in your data? Look at your data in its broader context, not shutting out lines of inquiry that on the surface may seem irrelevant. If you see something in the data that looks a little different from what you might have expected, dig in deeper. As you’re exploring, pay attention to data that shows you which product categories a particular customer demographic ISN’T buying, the regions where a sales rep has NOT been assigned, or the products that HAVE NOT been returned under warranty. There just may be insight here.
  • And, my favorite: “You must actually do something.” Exploring data and discovering insights in it are all well and good—but utterly worthless if no one acts on the insights. The same would be true for a serendipitous meeting with a person; what good is it if you never follow up on that business card the person shared with you? Johansson gives an example from science: “...scientists have the best chance of writing groundbreaking papers when they publish a lot, no matter when that happens in their career.” He drives home the importance of taking action. “Any action we take in response to a click moment, for example, will open up a wide range of new ideas, possibilities, and connections. And any move we make to place a purposeful bet will set us in motion. As we move ahead to explore one bet after another, we come into contact with a different set of options, opportunities, and inflection points.”

According to Frans Johansson, “If we can create an environment where we can increase the number of unexpected insights, then we set ourselves up for success.” If you are creating opportunities for serendipitous discovery, setting aside some time to go exploring, keeping an eye outside your primary area of inquiry, and then taking some action, it’s highly unlikely that your competitors will reach the exact conclusion you do. And there, hidden in plain view, is your competitive advantage.

Check out Johansson’s book – or, if you only have an hour, check out the on-demand webinar, “Capitalizing on the Click Moment” with Johansson and QlikTech’s VP Product Management Donald Farmer. It will be time well spent.

My previous blog about the human mind and Big Data (see “Your Split Brain (Part 2 in the ‘Body of Big Data’ Series)”) ended with the realization that Big Data has two fundamentally different use cases. If managers are to get first-hand value out of Big Data, it might be useful to understand how the conscious, or sensory-somatic, part of our brain makes decisions.


Photo by: Johns Hopkins University Applied Physics Laboratory

If you pick up something with your hand (try it right now – pick up an object in front of you) you’ll notice that you first use your fingertips to touch the object, your curl your fingers to grasp the object, you move your hand to orient the object, and then you move your arm to raise the object. Of the millions of sensory receptors from your fingertips to your arm, you actually need very few of them at any one time to perform this action. Consider people with prosthetic limbs which enable them to do the same thing you just did. This proves that it doesn’t take much data to perform an action. However, out of the millions of receptors, you never know which ones you will need for a particular decision. For example, if a mosquito bites your arm, a very small number of receptors fire furiously, causing you to swat the mosquito.

This brings us to my second insight about making decisions when we have Big Data: Use data relevant to the current problem.

While the algorithms data scientists write may need most of the data most of the time, managers almost always need just a relatively small amount of relevant information to make their decisions. This means that whichever BI tool you use to analyze data must be able to present information that’s relevant to your particular decision at hand. Just as it doesn’t make sense to kill a mosquito with a sledgehammer (and you’ll probably miss), it doesn’t make sense for managers to use the same tools as data scientists to make business decisions.

What are the best ways to make sure we have relevant information at our fingertips, then? Here are a few ideas:

  • Aggregate data to the level of the decision to be made. For example, it doesn’t make sense to look at time-series data at a one-second granularity level when you’re making a decision that is at the granularity of a daily level.
  • Organize your decisions into categories based on the task or app concept. Just as picking up objects and swatting mosquitoes are two entirely different decisions requiring different data, consider creating apps with data models that contain just the relevant data. Don’t throw everything you could possibly need into one monolithic app or dashboard; you’ll drown in the data.

Hopefully these insights have been helpful.  In my next blog post I’ll explore the mystery of how our minds make decisions based on both conscious and subconscious information.

Whether you’re a patient or a doctor or someone who works in a medical office, one of the frustrations in life is getting answer to your questions about insurance payments, drugs, treatments, etc. It makes me proud that QlikView customers in healthcare and pharmaceuticals are using QlikView to empower front-line professionals to answer their customers’ questions quickly and accurately, thereby improving the customer and patient experience. Here are a couple of great examples.

doctor on phone.jpg

DAKOTACARE, the health care plan of the South Dakota Medical Association (United States)

Prior to using QlikView, the customer support staff at DAKOTACARE had to rely on IT for custom reports to answer questions from health care providers and clients. The IT group placed report requests in a queue and prioritized them and the requestor would get the requested report days or even sometimes weeks later – by which time the information was often no longer needed. Sometimes the support staff would not even submit data requests, knowing that their information needs would not be met prior to a decision deadline. Some of them would try to extrapolate answers on their own, with varying results.

Now, with QlikView, support representatives can answer providers’ and customers’ questions instantly, all on their own. For example, if a provider calls the support line to inquire about when a claim will be paid, the rep can answer the question immediately– and send the provider an Excel spreadsheet showing all claims, receipts, and payments going back to 2001. DAKOTACARE is seeing increased customer satisfaction because customers are getting complete answers quickly.

Genzyme (a Sanofi company), a pharmaceutical company

Answering questions and informing customers is an important part of maintaining Genzyme’s customer relationships. When customers have questions about the use of Genzyme products, it is important that the questions are answered in a timely and thorough manner. Prior to using QlikView, medical information personnel spent many hours manually compiling reports from an inquiry database. Reports used to take days to generate and were only available monthly.

Now, with QlikView, all of the data that can be shared with the business is available in one area and the compilation is automated. This means a reduced number of hours spent on compiling reports. Information is now available to Genzyme employees on a daily basis and they have insight into the questions that their customers are asking nearly as fast as customers are asking them.

To learn more about DAKOTACARE’s use of QlikView, download the case study, “DAKOTACARE secures healthy business analysis with QlikView.” For more information about the Genzyme story download the customer success story, “Genzyme Uses QlikView to Support Its ‘Power of Intelligence’ Initiative.”

Our customers frequently ask us how we use QlikView here at QlikTech. The answer is, “In every way you can imagine.” I just came across another use case: management of our product quality process. I spoke with Annette Steinrücken, quality manager at QlikTech, and thought I would share a couple of highlights with you.

black belt.jpg

Annette, a Six Sigma Black Belt, is in the second wave of her tenure with QlikTech. During her first stint in the late 90s she was a software tester and today she oversees the entire production process QlikTech. In her role as quality manager, Annette supports and coaches process owners to develop, document, and implement their processes in the best possible way.

At QlikTech we use the Software Engineering Institute’s CMMI (Capability Maturity Model Integration) version 1.3 quality model for software development and we use QlikView to measure and improve our efforts. We use QlikView apps to measure many factors that affect product quality. We track KPIs for new development as well as product maintenance in areas such as project progress, delivery precision, response time, number of development bugs, and bug statistics based on product area.

“QlikView enabled us to identify bottlenecks in our processes – areas where we could make improvements,” Annette said. “Another benefit of using QlikView is that we test our product design with our own real business cases and contribute our own feedback to an improved product. With QlikView apps and stronger adherence to the CMMI quality model, we have seen an increase in the stability of each new product release.”

Want to learn more about how we use QlikView internally? See these related blog posts: “How We Used QlikView to Transform QlikTech Customer Support” and “QlikView on Salesforce Yields Higher ‘Return on Energy.’

No, I’m not referring to the left and right lobes of your brain or implying that you have a personality disorder. In my previous blog post, “Who, Me? I’m Big Data? (Part 1 in the ‘Body of Big Data’ Series)”, I described how our nervous system is like Big Data, dealing with volume, variety, and velocity continuously.

nervous system.jpgMy further research into how our nervous system works revealed that there are two primary subsystems: the autonomic and the sensory-somatic. Before I lose you with the big words, just remember that:

  • “Autonomic” is automatic. It’s about things you don’t control that happen automatically, such as your heart rate, the size of your iris, your digestive process. You might notice them when they aren’t working properly, but most of the time they are complex background processes that optimize themselves to your body’s current condition. For example, when you are exercising your heart rate goes up and when you are resting your heart rate goes down. Somehow your autonomic nervous system takes into account all that Big Data delivered by your senses and optimizes your body’s functions.
  • “Sensory” means you feel it and “somatic” means you’re awake. So the sensory-somatic nervous system represents things you are conscious of such as sights, sounds, and movement. In this realm of the mind we make conscious decisions based on sensory input. When you hear the roar of a lion you start to run, but when you see that the lion is in a cage you decide to stay put.

So how does the way our mind process Big Data relate to how organizations process Big Data? The corporate analog to the autonomic nervous system is any use of Big Data that is automated, repetitive, and usually involved with process optimization. For example, online retailers use Big Data algorithms to optimize their recommendation engines to drive incremental sales, or hedge funds use automated stock trading algorithms to optimize their return on assets. These are uses of Big Data that are the domain of data scientists – people who are skilled in mathematics, statistics, and algorithms and create batch processes that run in the background continuously.

The corporate analog of the sensory-somatic nervous system is the managers who make conscious decisions informed by data. The decisions they make each day are different and cannot be described programmatically. One question often leads to another, and one decision is usually part of a series of decisions.

This thinking led me to an insight:  Big Data has two fundamentally different use cases. What works for the data scientist is probably going to be different from what works for management. In my next blog post in this series, I will explore the thought process of managers more deeply, to understand what is important to aid them in better decision-making.

Original logo.jpgIn the early days of QlikView, there was a big challenge in describing what QlikView is. A word processor is a word processor and a spreadsheet is a spreadsheet. But what is QlikView? How do you describe QlikView to a person that hasn’t experienced QlikView?


Technically, it is a logical inference engine with a visual user interface that utilizes color coding and group theory to display the possible combinations of field values. But that description doesn’t help a lot – does it?

Our first attempt to describe what the product does is in fact found in the original product logo. It was the shape of an aperture that symbolized that the user could focus in on specific pieces of information.



We soon started to describe QlikView in terms of “associative” and “works like you think.” We recognized that the human train of thought is not linear and predictable and we started to market QlikView as a tool that supports the way the brain works. The brain associates and contemplates. It turns questions around and looks for the opposite, the excluded.


We felt that there was a gap between man and machine and that QlikView was the tool to bridge this gap.



Info Mart 5.jpg


At one time there was much buzz about data warehouses and data marts. We of course wanted to position QlikView in this discussion, so for QlikView 3 we started to call it “the associative info mart program.” “Mart” because it positioned QlikView as a tool with a subset of the data (compared to the data warehouse) dedicated for one specific purpose. Today we would instead use the term “app.” “Info” because QlikView was more than just a data mart.


AQL.jpgIn 1996 at CeBIT, the big yearly IT exhibition in Hannover, I and a couple of colleagues found ourselves in the Münchner Halle late one evening after a few drinks discussing with potential partners ways we could license our technology to other software vendors. At the same time, Alfredo – a local opera singer – was performing on the stage in his red tuxedo singing “Granada” and “O, sole mio.” He did this every year.To make a long story short: We brainstormed possible trademarks for the technology and came up with AQL – Associative Query Logic. This abbreviation survived many years and is still used by some partners and users.


Intuitive data exploration.jpg


Since then, we have used many other descriptions of what QlikView is: intuitive data exploration, general purpose multi-dimensional query tool, a revolution in business intelligence, in-memory BI, and now Business Discovery. All of these descriptions are correct. They describe different aspects of QlikView. But Business Discovery is the best description so far.


To me, the main difference between QlikView and more old-fashioned query tools is that QlikView supports the entire process – the process of coming from a blank mind, not knowing what you are looking for, all the way to attaining knowledge and taking action.


It involves exploring the data. It involves discovering new facts. It involves playing with data, turning them around, scrutinizing the facts and formulating a relevant question. It involves conducting analysis to get an answer to the question.Revolution.jpg And, finally, it involves presenting the answer to the question to other people as a basis for a decision or an action. It supports the entire process of going from ignorance to insight.


And how do you describe that in just a couple of words? Business Discovery is in fact quite good.




Further reading on the Qlik history:

A Historical Odyssey: Quality - Learning - Interaction - Knowledge

A Historical Odyssey: QlikView 1

100,000 Questions

Posted by Erica Driver Nov 2, 2012

What happens when you get a hold of a traditional report or standalone data visualization and start to look at the numbers? You think of questions. No matter what your original question was, seeing the data always brings more questions to mind.

Ask and answer limitless questions with QlikView.png

What makes a QlikView app different from a traditional BI report or standalone data visualization? With QlikView you can ask and answer streams of questions on your own or in teams and groups – without having to go back to IT or a BI specialist to get a new report or a new visualization. With QlikView, the answers to most questions are just a few clicks away. You can ask questions in many ways such as lassoing data in charts and graphs and maps, clicking on field values in list boxes, manipulating sliders, and selecting dates in calendars.

One person’s line of questioning may spark questions in others and as people explore the data they move toward insight and decision. Using QlikView’s social Business Discovery capabilities like annotations and collaborative sessions you can engage others in the questioning process, in real time or asynchronously.

The 100,000 Questions Jam

To demonstrate QlikView’s ability to enable users to ask a limitless number of questions, 17 of us at QlikTech got together for a short jam session we called “100,000 Questions.” We all logged into the Sales Management and Customer Analysis app on our demo site. On the count of three we all started to bang on the app, identifying and writing down questions we could ask with the app. Below is our list. (Get ready, it’s long.)

You’ll see that some questions are straightforward while others are more complex. Some questions (numbers 1-6) really exemplify the QlikView associative experience – the user is able to see all the relationships in the data. Other questions (7-14) highlight the power of gray – QlikView shows the user not only which values are associated – but which ones are not.

How many of these questions can you answer with a visualization, report or traditional dashboard? How many IT staff, power analysts, or visualization experts would you need to support the business users asking these questions? With a single QlikView app, this is the list of questions that 17 people came up with in about 20 minutes. Imagine what’s possible with dozens or hundreds of people using an app over weeks and months. It's brilliant!


  1. When I select the product subcategory "pumps" I see that we have not sold any pumps in all of 2012. This is unexpected -- what were the sales trends on pumps in 2010 and 2011?
  2. It's interesting that customers between the ages of 50 and 60 who have purchased accessories but no bikes have all purchased individually (online) vs. in a store. Should we run a marketing campaign to try to bring them into a store?
  3. We sell the largest volume of socks to customers in the 40-50 age range. What else do customers in this age range tend to buy?
  4. Auburn CA is only selling components and their margin is down. If they added clothing, etc., would their margin increase?
  5. Is there a correlation between customer age and choice of full- or half-finger gloves?
  6. Is there a correlation between length of customer's last name and how much they spend?
  7. Which products did we not sell to a store?
  8. Which product categories did we not sell to an individual?
  9. In what quarters did we not sell any bottom brackets?
  10. What categories aren’t we selling in Boston?
  11. Why are no vests sold in Wyoming?
  12. Which customers in Canada did not buy products in a store?
  13. Which products has "Aaron Products" NOT purchased?
  14. Which product subcategories are we NOT selling, in the top two customer age groups in Australia?
  15. Which products did we sell to a store?
  16. Which product categories did we sell to an individual?
  17. What are we selling in the US?
  18. What was our total revenue in 2012 across all regions?
  19. How many bikes have we sold?
  20. What was our total revenue in the US in 2012?
  21. What stores are in Boston?
  22. What kinds of bikes have we sold?
  23. Where are we selling half-finger gloves in Australia, but not full-finger gloves?
  24. Which people over 90 years old bought clothing but nothing else?
  25. Which customers have purchased bikes but not accessories?
  26. Which customers bought bikes in Q3 2011 in Australia but did not purchase any accessories?
  27. Which customers bought road bikes in Q3 2011 but did not buy components?
  28. Which customers have purchased accessories but no bikes?
  29. Which customers bought road bikes in Q3 2011 but did not buy clothing?
  30. Which customers bought road bikes in USA in Q3 2011 but did not buy clothing?
  31. Which customers between the ages of 50 and 60 have purchased accessories but no bikes?
  32. Which customers bought road bikes in 2010 and didn’t buy helmets?
  33. In the US, which customers bought road bikes in 2010 and didn’t buy helmets?
  34. How much revenue comes from Boston?
  35. How did we do in 2011?
  36. What product subcategory does "front brakes" belong to?
  37. Which categories are we selling in Boston?
  38. What are the in-store sales in North America?
  39. How many vests have we sold in the US?
  40. How many types of road bikes do we have?
  41. Do customers in Europe spend more than those from the US?
  42. Of the $12M we sold in the US, which region produced the most revenue?
  43. Did we sell any products to stores in Germany, or just to individuals?
  44. How many vests did we sell in 2011 compared to 2012 YTD?
  45. What's our most profitable product?
  46. Where are we selling the highest number of helmets?
  47. Which product produced the most revenue?
  48. Why are our profit margins so low in Canada?
  49. How many product subcategories belong to the category "components?"
  50. How many orders did we get in Q1 this year vs. last year?
  51. Which region had the best margin improvement over last year?
  52. What was our revenue in Q3 2012 in California?
  53. How do bike rack sales compare to bike sales?
  54. Why didn't we sell headsets, locks, pumps, tights, and wheels in California in 2012?
  55. What is the profit margin on accessories in 2012
  56. Which country purchases/sells the highest number of shorts?
  57. How many orders did we get in Q1 this year vs. last year?
  58. Who's our best customer, globally?
  59. What product line shows greatest potential for future expansion in Asia?
  60. What is the company’s profit margin?
  61. Who are the top sellers?
  62. How does profit margin overall compare to profit margin in Canada?
  63. Where are the top sellers?
  64. Which region had the highest margin movements last quarter?
  65. What is the biggest selling state in the US?
  66. Are there significant seasonal fluctuations?
  67. Which regions had the highest sales in Q3 of 2012?
  68. Why are sales of wheels concentrated in one particular area?
  69. How many products did customer A2Z Solutions buy?
  70. Why were no components ever sold to individuals?
  71. How do our Q1 sales compare YoY? What about Q2 and Q3?
  72. In which countries were margins below 20%? 15%? 10%?
  73. What's the average quantity of items purchased by customers younger than 25?
  74. What's our best quarter?
  75. Why have the sales of tights stayed constant the past 5 months when they were steadily increasing before?
  76. Based on our YTD sales and quarterly YOY growth, what are the projections for Q4?
  77. Why is Q3 our best quarter?
  78. In the "bottles and cages" product subcategory, which products had the highest margin so far in 2012?
  79. Which products do people over 40 buy?
  80. Which customer purchased the most with us?
  81. How many orders did they place?
  82. How does product category change as customers age?
  83. Which country sells the highest number of tights?
  84. Why didn’t we sell any HL headsets into Germany in Q3 and Q4 of 2011?
  85. In Canada, which customers bought products in a store?
  86. Why are road bike margins so low in Canada compared to Australia?
  87. Why didn’t we sell any bib shorts in Australia in 2011?
  88. What is the distribution of customers by age in Canada, for all of the transactions we've done in that region?
  89. Did anyone notice we sell 6x+ more wheels in North America compared to the rest of the world combined?
  90. What is the demographic age range for bike stands in each country?
  91. Which products has customer "Aaron Products" purchased?
  92. The top 3 individuals ordered with us more than 25 times in 3 years but they've never bought bikes or components from us. Why not?
  93. What is our margin in the US?
  94. Why is margin so far below goal?
  95. Why have sales dropped so much in the US?
  96. Why are sales of gloves declining?
  97. The number of orders is up but sales are low. Are sales reps giving stuff away? 
  98. Lisa Cai is between 50 and 60 and has purchased 12 items valued over $5,000 in 2012. What are the upsell opportunities?
  99. Which customers bought road bikes in Europe?
  100. Which European country sells the most helmets in the 4th quarter of the year?
  101. How can we market mountain bikes to match the revenue goals met in California in other states?
  102. Are there significant differences between sales in France and Europe?
  103. Which country made the best margins on clothing in 2011?
  104. Which product is most profitable in Germany?
  105. Which products are we selling under margin?
  106. Which country sells the least road bikes?
  107. Which country has the highest margins on bib shorts in 2012?
  108. Which country has the lowest average number of orders per month?
  109. Which country has the lowest average number of clothing orders per month?
  110. What's our projection for sales of bikes in 2013 based on sales over the last few years?
  111. What was our margin on bikes sold in the US in the first half of 2012?
  112. Why is the Road 250 Black so unprofitable?
  113. Which country has the highest average orders of accessories per month in 2011?
  114. What's our most profitable product in the UK?
  115. How close did we come to our margin goal on bikes sold in the US in the first half of 2012?
  116. Who is our top customer in Australia?
  117. Why are US sales 3X the number in Canada for clothing?
  118. Who is our top US customer?
  119. Who are the top three customers in the UK?
  120. Who are the least profitable European customers?
  121. What other regions are selling an unprofitable mix of products?
  122. What is the year on year sales growth in Australia?
  123. How many bib shorts have we sold across the world in 2012 so far?
  124. Which country sells the most Mountain Bike 400 Silver?
  125. Among the types of bikes we sold in the US in the first half of 2012, what was the profit margin on each type?
  126. Show me the products that are selling below margin.
  127. In September of 2011, what were our average orders per month?
  128. Why are there no sales of logo caps to anyone over 36?
  129. Which stores are selling low-margin items? Are they also selling high-margin items?
  130. We are selling a lot of jerseys at an average of -20% margin. Which alternative products could we sell instead that are higher margin?
  131. How many customers bought mountain bikes and didn’t buy a pump in 2011? Maybe we should run a 50% pump promotion? But before we do are we making enough margin on pumps to justify this? If not we should consider upping the margin on pumps? If we do this maybe we could put a promotion together to justify the offer in the eyes of the consumer?
  132. In which regions are we selling the majority of jerseys?
  133. Why have we not sold any components in Q3 and Q4 of 2012 when component sales were the highest in Q3/Q4 in 2011?
  134. What does Patton Enterprise sell besides a few no-margin items?
  135. Why are there no sales of logo caps worldwide to anyone over 36 not just in the United States?
  136. Do 30-40 year olds spend more than 40-50 year olds?
  137. Do 40-50 year olds tend to prefer touring bikes over road bikes?
  138. Which age group buys more accessories?
  139. When we sell jerseys in a store we are selling them at -33% profit margin. But when we sell them online to individuals the margin is +23%. Should we stop selling them in-store?
  140. Which age range buys the most mountain bikes?
  141. Which age group is the least profitable to sell clothing to?
  142. For sales in Australia, what are the top two age groups we sell to?
  143. Are we losing money off of Patton Enterprises?
  144. Have we hit our margin goal on clothing in 2012?
  145. What states are doing best in the US?
  146. what states are doing the worst in the US?
  147. What are the successful states selling?
  148. Which regions are doing the best (and worst) for in-store sales in Australia?
  149. California has good revenue but several cities are below margin. What are they selling?
  150. The margin is declining for in-store sales in Australia. Which products are lowest-margin for in-store sales in this region?
  151. Barston is costing us money. They are -10% margin. What's up with their orders?
  152. What percentages of our sales in stores in Australia come from bikes? components? clothing? accessories?
  153. A2z Solutions did 1,000 orders with us. Were these unprofitable?
  154. For online sales in Australia, they are 97.4% bikes vs. other categories. What other products should we sell online – especially the ones that are low-margin in-store in other regions, but high-margin online?
  155. Which customer has the highest sales order from the store channel?
  156. Alabama and Massachusetts are the 2 lowest revenue producers in Q4 across all product categories, in the US. What's the issue?
  157. What proportion of sales in Maine are for bikes?
  158. In the first three quarters of 2012, we've had margin issues with the stores in Brandenburg Germany (-5.7% margin). How does margin on specific products compare in this part of Germany compared to others?
  159. Nothing was sold to individuals in Maine.  Why?
  160. Which categories of products are A2z Solutions selling below cost?
  161. Did we sell these expensive/no margin bikes last year?
  162. Why is the margin for bike stands in city of commerce, CA -47.0% compared to other cities in CA?
  163. Who are we selling low-margin bikes to?
  164. On sales of accessories in Germany, cable locks are selling at only 31% margin -- below our target. Are they selling at higher margins elsewhere?
  165. What bikes are popular with the younger folks and also with middle aged people?
  166. What is our best-selling bike?
  167. We are only selling cable locks in stores. Should we start selling them online where we can get a higher margin?
  168. 40 to 50 year olds spent the most money on bikes in 2010. Is the trend still valid in 2012?
  169. Which product category does "pumps" fall into?
  170. What is the age group of customers buying bike stands in City of Commerce CA?
  171. The Road-150 is selling well and is high margin. How many stores sell this bike?
  172. How are sales of the Road-150 trending?
  173. Which bikes do customers buy in Europe?


--A special thanks to the participants in the “100,000 Questions” jam: Anders Lundkvist, Chester Liu, Ellen Gyles-Scott, Henk Jekel, Jacque Coolidge, Kevin Gould, Mike Saliter, Monica McEwen, Peter Simonsen, Ralph Neugeberger, Sandhya Patel, Spence Culpepper, Tobias Jakobza, Tom Hardcastle, Zinat LaManque, and others.

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