If you have attended an eMetrics event, you know how awesome they are.  After spending several months fighting to get people to care about data, you get to spend a few days with peers, talking shop and having a pint or two. They are honestly the most enjoyable shows I do all year.

Full disclosure: I try and get to as many eMetrics shows as I can, and have had the privilege to have spoken at several of them. So I have a bit of a bias.

In an attempt to help foster Ottawa’s fast growing analyst community, and also drive some awareness of eMetrics and the DAA, there will be a half-day event taking place January 17th in the Byward Market. Unlike the summits, which do have attendance fees, the Ottawa event will be free of charge, which is my favourite price.

Now, normally when a major organization puts on a free event, they send in the B team. Except for one speaker (last one on the list), eMetrics has put up some of the best minds in the business.

Jim Sterne: Founder of the DAA and eMetrics, and best selling author on eMarketing, Mr. Sterne is flying in from Santa Barbara to speak personally.  Jim is one of the best speakers on measurement in the world, and is worth the price of admission.  (Given that it’s a free event it doesn’t come out like a compliment, but trust me – he’s amazing).

Stephane Hamel: Stephane is one of the best known digital analysts in the country, and is a pioneer in both analytics maturity modelling and analytics training.  He is coming down from Quebec City to share some of the insights garnered in his work around the world.

Kelly Kubrick: While Kelly is known around North America for her analyst chops, she also happens to be one of the most sought after analysts in Ottawa (she’s local!).  True story, it was after a lunch with Kelly years ago that I decided to change careers and become an analyst.  If you are an analyst in Ottawa, odds are good that, at some point, someone paid Kelly to train you.

Alan Wille: Alan is the CEO of Klipfolio, both an Ottawa based company and pioneer in dashboard technology. Alan and his team were light-years ahead of everyone when they realized the need for solid internal dashboards, which they are now powering inside major enterprises globally.

Me: Someone else couldn’t make it.

Whether you are a member of Ottawa’s fast growing measurement community, or a digital/marketing/sales executive who wants to hear some of the best minds in the business (and me) talk about best practices for winning with data, you need to check out this show.  Once it’s over, you’ll have a ton of great new ideas – and probably plans to check out the eMetrics summit in Toronto later this year.

Cheers,

Jim

PS.  I know this is a fanboy style post, and I am normally more of a snarky writer.  That’s how good I think this event is.

Business analysts, especially ones who specialize in digital, are a rare breed. They have a strong understanding of technology and data, but also have the ability to see the big picture of an organization–so that they can focus on telling relevant stories around signal data. This is why good analysts are so hard to find, and so expensive to acquire.

When you talk to a firm that already has analysts in place however, you find out what they are tasked to do tends to be the following:

• Tag management for existing web technologies
• Basic ad hoc reporting management for business staff (e.g., “How many people came from email last week?”)

No wonder analysts never stick around longer than a year at one job; by the time the recruiters start calling with more money the analyst is so bored they would work for less.

Now, there are good reasons for why analysts aren’t normally used very effectively, and I have spoken about some of them in past blogs (too much separation from business users, newness of the discipline, etc.).  Moreover, getting to a place where you are effectively using your analyst can be very rocky, it’s not an overnight decision and it will require cultural change in your business.

Here’s a tactic that you can use to start to get some real business impact from your analysts: give them interesting challenges that they will enjoy, and start to get your business moving from intuition to insight-driven.

The next time you are considering a new vendor (“Our mobile email retargeting system creates 5x ROI!”) or a major new digital initiative (“Let’s move a bunch of our ad budget to LinkedIn”), bring your analyst in and ask them to build a model for you that answers the following:

• What are the specific numbers in our business that this vendor/initiative should affect?
• How are these specific numbers connected to our primary KPIs?
• Given historical data for these numbers, what is the opportunity for us if the vendor/initiative increases performance by 1%?  What does this do to the primary KPI?
• Given the numbers we know already (average sale, margin, cost of sale, etc.) coupled with the cost of the vendor/initiative, what is the bare minimum impact needed for it to pay for itself?

Trust me, an analyst who hasn’t gotten a meaty question in a while is going to love this.

If you have a capable person, you are going to get some real insight and really start down the road of tying analysis into decision-making. Just be prepared to see a lot of ideas get shot down by the data. That idea someone had to dedicate three full-time people to Twitter and Facebook to grow sales might get shot down.

It’s up to you to let the information support your decision (and maybe trump your intuition).

Cheers,
Jim

Tweet Buttons

When you generate a Twitter button for your website, you’re given code that looks something like this:

This is basically a simple link which will be replaced dynamically using JavaScript. The above example is for a button that tweets the current page. If you’re specifying the URL to tweet, your code will be different. You’ll also have your own twitter name in the place of YOUR_TWITTER_NAME, of course.

You also get some JavaScript that is meant to load a widgets.js file. Twitter actually has two versions of this code, one synchronous and one asynchronous. Asynchronous is generally better for your page load times, so we’ll be using that one in this example. So, somewhere on your page you should have the following code as well:

As was the case with Facebook, Twitter was nice enough to give us a callback function that is executed whenever a Tweet is successfully sent from such a button. In order to add Google Analytics social media tracking to this action, you just need the following code added to the page:

This code will first wait for the asynchronous JavaScript to load, then track a social engagement event called ‘Tweet’ in the network ‘Twitter’, as well as sending the title of the page being tweeted. The URL of the page the action occurs on is also sent automatically and appears in the reports. Again, if you are specifying a URL to tweet, you will probably need to replace document.title with the name of the page to tweet, in quotes.

Follow Buttons

Twitter also provides buttons that allow visitors to quickly follow your tweets. The code for such buttons looks like this:

You’ll need the asynchronous code given above to make this button function as well. Again, Twitter’s callback functions let you recognize whenever someone performs a follow action, and you can track the social media engagement accordingly. The following code will do the trick:

This code tracks a social action called ‘Follow’ in the network ‘Twitter’, and sends the Twitter name of the user followed. Please note also that all code on this page uses the Google Analytics asynchronous tracking code. If you’re using the traditional snippet, you’ll need to make modifications.

All of this allows you to gauge the effectiveness and utility of Twitter buttons on your site. Using segmentation, you can see whether people who tweet articles/posts/content are more engaged or likely to purchase than others, or whether certain content gets you more Twitter followers.

So, that’s basically it — use the code above and start measuring the impact of Twitter buttons on your site’s traffic. The results will show up within Google Analytics under Traffic Sources > Social > Social Plugins.

Cheers,

Colin

This time, I want to discuss metaphysics, the area of philosophy focused on being itself. What is it to exist, and what are the things that exist? What is the true nature of the world? These are the questions metaphysics tackles. Of all the areas of philosophy, this probably seems the least relevant to business analysis, and I’ll admit it may be the biggest stretch. However, all belief and knowledge depends on certain metaphysical assumptions, and it’s an important philosophical activity to address them.

Photo credit: Janet Ramsden

The Metaphysics of Business Analysis

First, a note on metaphysics. Many people tend to think of metaphysics as being what its name suggests in modern terms, ‘beyond physics’. That’s not the case, nothing supernatural or mystic is intended by the name, though many have supernatural metaphysical beliefs, of course. The name ‘metaphysics’ actually comes from readers of Aristotle who referred to the writing after Aristotle’s Physics as the ‘Metaphysics’. What Aristotle does in these texts is what many philosophers have called ‘first philosophy’. And the name makes sense: before we can do any other philosophy, we have to determine what we’re talking about when we discuss being, the world, objects or anything else. We need to make our assumptions perfectly clear.

So what are the objects of business analysis? A business, the data it generates and the money it makes come to mind. But these three things seem like abstractions to me. Are they real things? In the same sense that a rock is a real thing? I’m not sure that they are.

What is a Business, Really?

Legally, a corporation is an entity in itself. But is there really such a thing as a corporation? Does it have some kind of social reality or is it just something that exists in our minds? Is it reducible to facts about human brains and what they believe? Or does it reduce to a set of people, offices, desks, chairs and other items? Maybe it’s not reducible at all, and a business is something in and of itself, separate from all the ideas, people, offices, desks and chairs, which is created in the moment that someone starts a new business.

If every person employed by a business were to vanish, would the business continue to exist? Is the answer the same whether the business is a company or not? This is an ontological question, a question about what things are counted as ‘real’. Sometimes that’s an arbitrary decision, but a philosophical system must have a consistent ontology, and a common attitude is to limit one’s ontology to the least types of entities possible.

I also brought up the issue of whether an analyst is measuring a business or simply business. That may seem like a subtle difference, but it has to do with the problem of identity over time. A business is constantly changing — how much money it has, how many people it employs, who those people are, and so forth. Since this information changes from one moment to the next, it would seem that a business is never the same business that it was yesterday, unless we do posit that a business is a separate, independent, non-physical entity.

Otherwise, if a business lost all of its money and went into debt, fired every one of its employees, and then earned money again and hired new employees, we might not be able to call it the same business. What happens if all of the money and employees changed gradually over a decade – at what point does the first business cease to exist and the second come into being? (This sort of example goes back to the Plato).

It seems to me that the business analyst is not analyzing an object called a business. Rather, he or she is analyzing business as it happens — it is activity that is measured and reported on. The payment and collection of money, the movement of inventory, the clicks on a website: the analyst’s domain of inquiry is a specific set of activities in the world, activities that meet certain conditions, such as clicks on this website, movement of inventory in this warehouse or store, and so forth. For the analyst, this does away with the ontological question of whether or not a business exists; only relevant actions are the subjects of analysis.

Data and The Elusive Number

Photo credit: Luis Argerich

I ruined my boss’ day once when I went on a rant explaining why numbers don’t exist. Jim had trouble working on tactical things after that. But it’s a real metaphysical question that doesn’t have a well agreed-upon answer yet: do numbers exist? Is there such a thing as the number eight, for example? Does it exist somewhere? When I say 8 + 8 = 16, what am I talking about? Abstract concepts? If so, would numbers exist if there weren’t anyone to think about them?

These kinds of questions have troubled philosophers for centuries. Plato believed that all universals, that is, non-instantiated beings like “horse-ness”, “chair-ness”, “beauty” and so forth, existed out in some separate realm of the forms. He held that this realm, sometimes called the Platonic Heaven, was more real than this one, since we never get anything pure, just the likeness of these universal forms when they are instantiated in everyday objects.

Few modern philosophers agree with this view, but many of them do believe that mathematics is about something — namely these numerical objects which exist, on their own, in some real sense. So, I might have eight coins in my pocket, but apart from these and every other grouping of eight, there is the object ‘eight’. When we learn that 8 is twice 4, or that 8 is a Fibonacci number, we learn something about this object. This view is called mathematical platonism, after Plato.

Others, on the other hand, believe that eight only exists when you group eight things together, and that arithmetic is simply reasoning about arbitrary groupings of real objects, not about some real numerical objects themselves. This view is called mathematical nominalism. I fall into this camp, personally, but the matter is far from settled.

University of Nottingham philosopher Jonathan Tallant is featured in a nice video on this subject:

Fortunately, I will happily argue that not much hinges on this question for the analyst. The analyst manipulates numbers all day, but those numbers are measurements of things, so any metaphysical explanation for them will do, so long as mathematics is reliable. Even though the number eight may or may not exist as a thing in itself, a statement like ‘there were eight transactions in the last hour’ is still true if and only if there were in fact eight transactions in the last hour.

Money

What about the prize of business, the almighty dollar? Money has a similar ontological problem. We accept little discs of metal and little pieces of paper in exchange for goods and services that are worth far more than the actual materials we get back. Sometimes we don’t get anything physical back, but can read on a screen somewhere that our bank balance is higher than it used to be. A social convention exists which says that these symbols mean something, that they have value, and that they can be exchanged again for goods and services.

Does money exist? Of course, coins and banknotes exist, but what about the amount they’re supposed to be worth? If my bank account says that I have ten million dollars, like I wish it did, what that generally means is that I have a certain amount of purchasing power. Is that a fact — a social fact? I could get things, real things, in exchange for my imaginary things, these dollars.

What a deal.

And what happens when people stop believing in money? It’s happened before. Numerous bank runs and cases of hyperinflation have resulted from people losing confidence in a currency, resulting in a huge increase in prices of goods. So if money is such a fragile, imaginary, conventional thing, why do we base so much of society on it? Why is it the end goal for a business?

Decisions, decisions…

The problem with metaphysical questions like these is that there isn’t much agreement on the answers. Philosophers tend to be split up into various camps with names like ‘realist’, ‘physicalist’, ‘nominalist’, ‘idealist’ and so forth. Fortunately, it’s easy enough to ignore these questions when you’re in the game, but occasionally it’s fun to think about what exactly it is you’re working on, and working for, in a metaphysical sense — not just whether or not it matters but whether or not it is.

The idea here is that a business analyst works regularly with many abstract concepts and hypothetical objects. Whether it’s business, a business, numbers or dollars and cents, one can quickly feel like he or she is a long way from counting simple pebbles.

Next time, I promise to bring it back down to Earth with some ethical questions. What can we say about how an analyst ought to do his or her job? There are some very important considerations there.

Until then, cheers!

Colin

Our origins as a company are in eCommerce analysis. We not only understand how the data works in online retail, but we have a strong understanding of best practices around all aspects of running an online business. (10+ years of looking at the data will do that.).

Retail clients of ours always appreciate our unique insights, and I thought it might be helpful to provide some proactive insights to an online retailer that I currently have a bit of a love-hate relationship with: Indochino.

Indochino is a Canadian-based eCommerce success story (of which there are way too few, but that’s another blog). It was founded by two friends in University who had just learned firsthand how hard it is to get a first suit–and knew there had to be a better way. By tying together the retail impact of eCommerce (demand) with the many talented tailors in southeast Asia (supply), Indochino has quickly grown to become a lead player in the online men’s apparel category.

Those of you who know me are familiar with my ‘dereliqute’ approach to fashion. I have wanted to add a nice suit to my wardrobe for years, and have visited the Indochino site a number of times. The only drawback I had was sizing; I knew I would screw up the measurements if I took them myself. Recently, in response to a local social media campaign (nice work there, Indochino), a one-week-only pocket store was opened in downtown Ottawa, complete with tailors, sample suits and fashion consultants.

I immediately got an appointment, and the following week went to the event, where I was properly measured and got some great advice. A shirt and suit were promptly purchased and they even took all my measurements and info to make a site profile to facilitate future purchases. I commented to my wife after that I never needed to shop for suits again.

So, that was the good.

Image credit: Hello Turkey Toe

And then it happened. For the last several weeks, I have been power-bombed by shotgun advertising from Indochino, in the form of retargeted ads and email.

The ads, which were triggered by one of my recent visits to the site, show up on almost every webpage I go to, and in every video I watch in YouTube. Not only do I feel like my entire Internet use is now ‘sponsored by Indochino’, but I am also being treated to generic copy and product. This means that Indochino has decided to get as much retargeted inventory as they can, and haven’t taken advantage of the advanced business rule capabilities that exist in almost every retargeting tool.

The emails, which I have gotten up to three times a day, let me know every time a new category or product has been created, or when they have a daily special (which seems to happen a lot). Once again, despite having worked with me in person to make a detailed profile, there is no personalized copy or product relevance in the messages.

In short, a great business with an amazing approach to customer satisfaction hurt their brand (and future sales) by doing sloppy digital marketing.

Now there’s a reason why this is happening to me, and every other web user who has given their email address or visited the site. Display Advertising and Email are both numbers games. On average, when you send X emails, you will get Y visits and Z sales. Same with Display; show X million people the banner and you get a somewhat predictable ratio of eyeballs and wallets.

I did some quick research on other ways that Indochino might be driving traffic, and while I found a number of good articles on the firm (good), I found very little presence in paid or organic search (bad) and no attempt by Indochino to funnel certain types of traffic to targeted landing pages (very bad!).

Next week we’ll post a follow-up where some relevant key metrics and best practices for selling will be explained, all of which would add significant value to the Indochino marketing strategy.

And if anyone on the Indochino team is reading this, I’m still a happy customer. As a web surfer, I am less than delighted; and as a digital analyst, I’m shocked by the money you are leaving on the table.

Cheers,

Jim

This series of posts is going to look at business analysis from a meta-level, analyzing the process and delivery of analysis itself. I’m going to cover the five traditional areas of philosophy, which are epistemology, metaphysics, ethics, aesthetics and logic, giving a bit of explanation of each and where our profession stands. It should be a fun and wholly geeky adventure.

The Epistemology of Business Analysis

Epistemology is the area of philosophy concerned with knowledge. Its fundamental questions are as follows: What counts as knowledge? How can we acquire knowledge? Can we trust our knowledge? Closely related to epistemology is the philosophy of science, which covers a breadth of topics related to the acquisition of scientific knowledge.

As analysts, we like to believe that what we’re doing is scientific. Rightly so, I think. We’re concerned with understanding why the data we get is the data we get, and what data we will get next based on certain actions. When we’re doing things right, we follow a scientific methodology that involves testing our hypotheses and constantly revising our theory. All of this ties in closely with questions of epistemology.

Business Analysis as a Science

My remarks about science above echo those of 20th-century philosopher W. V. Quine:

From impacts on our sensory surfaces, we in our collective and cumulative creativity down the generations have projected our systematic theory of the external world. Our system is proving successful in predicting subsequent sensory input. How have we done it?

Quine’s question is an important one. Somehow, we as conscious beings, have been able to take our basic perceptions and turn them into a view of the world. We believe all kinds of things about reality beyond what we’re perceiving at any given time, and we use that information to predict what’s going to happen next. We’re pretty good at it, too.

Such is the case with business analysis: we collect data from all areas of a business, and with it we develop a theory about what’s going on with a business. We take the data we get, which come in the form of raw numbers, and interpret that to form propositional beliefs. We believe things like ‘the website is selling more goods internationally this month’, ‘the inventory can’t keep up with demand’ and ‘we need more salespeople’. We use these beliefs to predict what will happen if we put more resources into international marketing, order more inventory or hire more salespeople. We then watch the results and decide whether or not we were right.

Knowledge is thought, by many philosophers, to be a specific kind of belief; specifically, it is belief that is both true and that one is justified in believing. Much has been said and written about what exactly ‘truth’ means, and how we should cash out ‘justification’. But the idea boils down to this: we count ourselves as possessing knowledge when we believe something that is true, and we have discovered the truth of the matter through some reliable method.

In business analysis, does this ever happen? I’m comfortable saying that it does, at least with a loose definition of ‘justification’, but I think a fair bit of what we count as knowledge doesn’t quite fit the bill.

Prove Yourself Wrong

The philosopher Karl Popper made an important contribution to the philosophy of science. Popper wrote that in order for a statement (like a prediction or an assertion about a business) to be scientific in nature, it must be falsifiable. That is, we must have a clear understanding of what situations, or what data, would prove our statement to be false. For example, if I were to claim that a business will fail if we don’t invest in more salespeople, I know that I can be proven wrong if the business doesn’t fail despite a lack of such an investment. So, my statement is a valid scientific claim. If I were to claim that there are invisible, undetectable unicorns that live in our socks, that’s not a scientific statement — you can’t prove that there isn’t something undetectable if it’s meant to be undetectable.

For this reason, it’s of paramount importance that analysts never fear being wrong about something. An analyst must always be honest, of course, and I’ll touch on honesty when I talk about ethics. But in general, an analyst should welcome the case in which he or she is proved to be wrong in some prediction. After all, learning that you are wrong is learning something new about the business, and that’s a tremendously valuable thing. It makes your next predictions better. Like a good scientist or philosopher, a good analyst is always trying to prove his or herself wrong — that’s the scientific attitude that makes for good progress. Chances are, if your analyst is always right in every prediction they make, your analyst is either lying to you, or your analyst isn’t doing anything that’s worth doing.

The Limits of Business Intelligence

Epistemic concerns have always been a major part of philosophy. As far back as Plato, the question of how we, as humans, can grasp the truth have been central to philosophical questions. And in a science like business analysis, the question of whether or not we really know the things we think we do is central.

Let’s take a moment to think about the sources of our knowledge. Whether we’re pulling data from business intelligence tools, CRM, web analytics software or an order management system, we generally depend these days on one kind of technology or another. We also depend on processes of gathering and storing information, processes which are carried out by people.

Consider the list of things that could go wrong: technology can have bugs, databases can be corrupted, processes can be ignored, overlooked or improperly followed. Human beings are prone to errors which may come in by data entry, along with errors of interpretation and analysis, and so forth. To be honest, the idea that we’re getting a perfectly accurate picture of a business at any time is a little naive.

That’s not to say that business data isn’t valuable. But rather than thinking of data as either right or wrong, perhaps it’s better to think of data as having a resolution. In the same way that your high-definition television produces a much clearer image than a standard-definition one, better technology, processes and people bring the truth about your business closer into focus. Errors in the image, or the data, mean you never quite get to the level of reality. But that’s fine. Good movies look awesome in HD; your business becomes clearer with the right tools.

No Laws of Business

Physics is one of the most successful of the sciences. It has been extremely powerful in its ability to predict the future, in terms of what will happen when we control things. We can predict where in space the Earth will be a year from now, two years from now, ten years from now. We have predictions of what miniscule particles will do if we give them a jolt of energy. We know how much power we’ll get from splitting an atom. All of these things are possible because science is able to codify its findings into laws, such as laws of motion and thermodynamics. Those laws are in turn applied to future events, and boom! — predictive power.

Business analysis is an imperfect science. It falls into a group of sciences that follow scientific methods, but don’t quite have the predictive power of physics and chemistry. Sciences like this include psychology, social science, political science and economics — basically, anything that has to do with people. The reason is that we don’t really understand people at the level which we understand more basic physical processes. We haven’t quite figured out how consciousness works. We can’t quite predict what a person will do in a given circumstance with accuracy — there are always outliers, anomalies, rebels.

Business analysis isn’t about discovering laws of business, it’s about evaluating trends in business. While physics tells us how things work and will always work, business analysis tells us how things worked up until now. We can’t trust that things will always be the same. Recent economic troubles are a testament to that.

Consider this: every now and then, an email services provider will post some study that they’ve done on open rates and clickthrough rates, and announce some ludicrous claim like, “Fridays are the best day to send emails”. Now, it may be the case that they’ve seen the best open rates and clickthrough rates on Fridays. But that’s not a law of email marketing, that’s a finding for a specific set of emails. Every week of the year is different. Every company that sends emails is different. Every individual email is different. Never blindly rely on a rule-of-thumb like that — continuously try other things and measure which does best, then repeat. The same thing goes for all so-called ‘best practices’.

And that’s where business analysis has its grey areas. The subject of business analysis is business, but business ultimately boils down to human interactions. Businesspeople interact with consumers or other businesspeople. People interact with websites and other technologies. The technologies and the ideal processes are easy to understand, but people make it messy. World events, economics, social attitudes, psychological oddities, holidays and other variables each play a major role in how consumers of any kind make decisions and buy products and services. Even an analyst equipped with a calendar, a newspaper and a master’s degree in psychology will have trouble connecting all the dots. Ultimately, there are just too many variables to get a perfect image. The data isn’t perfect, so the predictions will be imperfect.

It Still Beats Intuition

It might sound like I’m trashing analytics. After all, our data is imperfect, and even if it were not, it wouldn’t be able to perfectly predict. If all this is true, why bother?

The answer goes back to what I said about justification when it comes to knowledge. Some ways of believing are better than others — if we have good reasons for a belief, and that belief is true, we can say that we know it. I want to argue that business intelligence and analysis is the best thing we have to predict future events and determine the best course of action for a business. Specifically, I believe that analysis trumps intuition.

Here are some things we’ve discovered that at some point were counter to our intuitions of the time:

• The Earth orbits the Sun
• 0.999… = 1
• Some infinite numbers are bigger than others

At some point, all of these things were thought to be false, even paradoxical. But, through careful thought and analysis by those who did not merely accept their intuitions, we learned something new.

Don’t get me wrong — intuition can be a valuable thing. The problem is that it’s not reliable. Even analysts often use their intuition to guide their analysis, but this isn’t something a new analyst should do. Rather, an analyst’s intuition gets better the more ‘scientific’ they are in their methods. Eventually, you see patterns often enough that they occur to you before you run your tests. My argument is merely that you don’t know that they’re right until you perform the test and prove it. Intuition is a useful tool for guiding analysis; it is not a substitute for analysis.

Image credit: Jacob Bøtter

Doubt, but Act Anyway

The ancient Greek philosopher Socrates famously said that his wisdom came from awareness of his own ignorance. Doubt is a wise thing to have, and it’s an analyst’s job to be doubtful — doubtful of what they’re told by executives, doubtful of their own intuitions, doubtful about what’s best for a business and doubtful of what industry ‘experts’ call best practices. An analyst’s job is to test everything and only believe what the data confirms, with a healthy dash of doubt about the data, too.

That’s not to say that an analyst should not act. Things need to get done, and money can’t be made by quivering in a corner, overwhelmed by doubt. Write reports and write them confidently, but also write them accurately. Write only what you can reasonably be sure of. Write down your intuitions, but label them as such. Be honest about the margins of error in your analysis. A good analyst delivers competent, honest assessments of the areas of business they analyze, always knowing that each report could be improved.

There are always ways to lower the doubt, to increase the resolution of the data, to verify that processes were followed in capturing that data, and so forth. But intellectual honesty is what separates a good analyst from a great analyst. A great analyst uses the tools of science to provide business executives with the justification component of knowledge. By becoming an authority on what is and can be known about a business, the analyst becomes a source of truth.

This is just an introduction to what could be said about business analysis from an epistemic standpoint, but hopefully it highlights a few key themes and gets you thinking. Next up in this series is metaphysics, which deals with questions of being. That’s a lofty subject, but I’ll try to bring it down to Earth as best I can.

Until then, cheers!

Colin

Why it’s awesome

• Because it’s a part of Google Analytics, all of your goal conversion tracking will work natively with Content Experiments. You define your experiments to work with GA goals.
• For the same reason, the setup is easier — you only need to add code in one place, on the ‘original’ page, which is an improvement over Google Website Optimizer.
• This also opens up the potential to pull experiment data from the Google API, which would be great for ongoing performance reporting.

Why I’m still wanting

• The change is great for the reasons mentioned above, but there isn’t much here capability-wise that wasn’t in Google Website Optimizer. You can easily compare different pages if they have unique URLs. That’s great, but what about variations in a page with the same URL? We often want to check variations of product and checkout pages, pages which often can’t easily have multiple URLs. There’s still quite a bit of custom work required to accomplish this with Content Optimizer as it is, since you’ll need to write server-side code that responds to a GET variable in the URL (ie. ?variation=3). For those working with a somewhat inflexible shopping cart software, like many online retailers, this isn’t always an option.
• Another annoyance is that you can only use URL and Event-based goals in order to track conversions from each variation. You can get ecommerce transaction revenue amounts through goals when you use a thank-you page and set your goal value to 0 — but I’d prefer to link my success to transactions themselves, so you can see more information on what products were purchased (and so forth) using each variation.

Overall, I think the consolidation of these two tools — Website Optimizer and Analytics — is a great move and a step in the right direction. Hopefully this is just the beginning of new features for Content Experiments that will give Google, and Google Analytics users, an edge in the testing space.

If you want to try out the new tool, it’s located in the Standard Reporting area of Google Analytics under Content > Experiments. Meanwhile, Google Website Optimizer is set to close its doors on August 1.

Cheers,
Colin

It has been months since we last fed you a blog.  Sorry about that.  We were a little busy bringing on some amazing new clients and we kinda dropped the ball.  We promise to keep feeding your search engines with regular doses of awesome.

Apart from new clients, we have a lot more on the go:

• The analyst team is now 7 people, and we’ll be giving them all the chance to talk about business analysis in the blog.
• New site: You may have noticed the look of the blog is different.  It’s because we have completely relaunched the Napkyn.com website.  Our business has evolved so much in the last 18 months, our site needed to keep up.  Check it out and please share your feedback with us.
• Napkyn’s got a product? We are in the alpha testing phase of our product, which we have creatively called the Analysis Engine.  Everything we have learned in three years of helping executives move from intuition to information-based decision making has been incorporated into an awesome SaaS solution.  We have a lot more to share about this in coming months.

As you can see, we’ve been busy.  We’ll make up for lost time by getting back to our weekly publishing schedule for the blog, and getting a lot more visible on Twitter, LinkedIn, et al. (so give us a follow why don’tchya.)

Cheers,
Jim

The word “dashboard” is not on my hate list, but it seems to be on a lot of other people’s. Everything has a dashboard now, from software products, to consulting deliverables (we do them weekly for our clients), even video games and LinkedIn have dashboards in them.

So, I get why the thought of another dashboard would make many people want to barf, but it doesn’t make me think they are any less vital to being successful with data.

A few points below that you should consider before you give up on dashboards (I had a great Karate Kid reference I was going to make, but I have been informed that I need to dial back on the 80s film references…).

The obvious point:

A dashboard you don’t love wasn’t built properly: A good dashboard is supposed to provide ‘at a glance’ understanding of something you care about, giving you the ability to have increased understanding you wouldn’t have achieved any other way. If you are indifferent to a dashboard, it sucks. Get rid of it and have one crafted to your exact needs.

Less obvious but critical points:

1) Dashboards create commonality of language and goals: Ever notice that sales and marketing people use different words to describe the same thing? Ever notice that they don’t tend to get along? A well crafted (and agreed on) dashboard has the ability to create significant alignment in an organization, not just between different departments, but between different levels of the org chart. In helping a senior stakeholder build an exciting and relevant performance dashboard, we are very educated on where to focus our analysis on their behalf.

2) Dashboards decrease “weaponized” analysis: In organizations without proper executive dashboarding (so most of them…), the lack of common language, goals and structure creates “analysis anarchy”. This means that execs often ask the analysis to provide reports and data to support an idea or initiative. We call these reports ‘weaponized’ because they are only being created to provide data support to help win an argument, i.e., “Build me a report that shows how marketing has been wasting money on leads for the last 6 months”. Competing on analytics is supposed to be an external activity, not an internal one. It’s very hard to weaponize your reports when the whole team ultimately works again an agreed on performance framework.

3) Good Dashboards maximize analyst value and contribution: the lack of structure around data consumption is the Petri dish that dumb questions grow in (would that mean ‘bad culture’?). Many of the ad-hoc questions that web analysts get are based on a lack of overall understanding about digital. We find that instituting an appropriate and valuable weekly performance dashboard cuts down on ad-hoc requests significantly, and increases the quality of the questions being asked. Good questions tend to be harder to answer; your analyst will still be 100% utilized, but purely on creating insights of high business value.

If you are making plans for 2012, and someone comes out against using a dashboard, realize that they are tired of the abuse of the term, not the value proposition.

Just call it something cooler, like a ‘executive performance visualization’. At the end of the day, a well executed dashboard can have both corporate and competitive effects that are far reaching.

Cheers,

Jim

A quick recap on past write-ups:

• Batman: The boss, not the analyst.  A great executive-facing analyst knows who the hero of the story is, and works to empower them, not overshadow them.
• Doc Brown:  Great analysts will hack together what they need from what they have, not just stay inside a given tool.  They will also focus on the temporal nature of measurement, using the past and the present to predict the future.
• Columbo:  Relevant analysis can’t exist in a vacuum.  Constant interviewing of your internal stakeholders will help you learn their language and motivations, and ensure that your analysis is timely and relevant.

This week we are focusing on one of the least analytical and perhaps one of the most important of our pop culture references: Sam Malone from Cheers.

For those of you unfamiliar with Cheers (it was on TV for over a decade…), it was a sitcom about a group of regulars at a bar in Boston.  Sam was the central character of the show. He was the owner of Cheers and the mainstay behind the bar.

Sam showcased all the qualities inherent in the ‘perfect bartender’ stereotype.  He was a charmer and an amazing listener; strangers would often open up and tell him their problems (which he would then try and fix).  He had a great memory for drink orders, bringing people what they wanted before they asked for it.  This pathos and attention to detail is the glue that holds the motley group of regulars together, and creates a community within the bar.

I am guessing that Sam Malone would be a crappy analyst, at least from a data modeling perspective.  I also guarantee that he would be very effective at knowing what his internal stakeholders needed, and create the kinds of relationships internally that would allow his findings to be both appreciated and used to best effect.

As a real world parallel, Napkyn has become the analyst several times in organizations where there is interdepartmental conflict – sales hates marketing; eCommerce doesn’t agree with Merchandising.  Initially, we are often asked to ‘weaponize’ our reports, to frame the data in such a way where one group looks good and the other looks bad.

These situations are where the Sam Malone reference comes out.  By being positive and proactive with all stakeholders, we try to do analysis that creates positive impact through alignment.  The analyst has the ability to become the common person bringing together that motley group of personalities, which doesn’t just have the most impact on the business, it also positions the analyst in a much more important light than just someone who builds reports.

Getting an executive to care about analysis can sometimes have a whole lot more to do with customer service than Microsoft Excel.

Cheers,

Jim