Analytics spawned yawning among analysts?...is that possible?

By Judith Pascual | Dec 22, 2008 2:50:00 PM

I never thought I'd see the day that I would yawn during an analytics discussion.  But it happened.  I always thought it was my job to motivate people, show them the value and they will move forward.  I get little butterflies as I pull and integrate numbers and find a story to share.  I often feel like the journalist, breaking news.  But this time, I felt like I had been transported to 2004 and I though that was a good year for me, I was not pleased.

The lack of analytics mobility is starting to get boring. Apparently, I am not alone.   After so many years, let's move on folks!  I keep hearing things like, 'analytics is our focus, we need to act'...okay so why do you shy away from tracking based on your goals, not just the 'data' you are able to get at this time?  Why when you are given insights you don't act on them.  Segmenting is a bad word and you still find geography 'views' valuable.  This is just all very lame.

Now, during this economic turmoil, more than ever we need to stop making excuses for why analytics funding is not a priority and why you cannot act on customer requests.  Don't get me wrong, we have worked with so many clients that have grown and are now data driven businesses. But far too many are stagnant.  Ensure that you are not on that list. 

For 2009, you have already asked yourself, what am I spending money on?  Now, ask yourself what are you spending time on?  What are key stakeholders focusing on?  How is that growing or even maintaining your business?  Look at your analytics maturity level and if you see yourself having the same discussion you had in 2004. Stop.  Start the roadmap on moving forward and monetizing your business so you can optimize. 

Of course I know you are still thinking about costs so...put together a cost benefit worksheet (yes, it takes time and you do need to understand what you are doing) and among the obvious ensure to include:

1. Speed - what customer driven projects can we quickly turnaround that is going to influence return?

2. Better Results - improvements in results because actions were taken - savings included -

3. Shorten the meetings and discussions on items you have action plans for or documented a roadmap...and use them already...you'll be surprised how much time and money you'll save.

You can adjust things as you go...but take the step.

Remember that yawning is contagious.  Don't put yourself in a position where your analyst' yawn, de-motivate your stakeholders and it all transcends to consumer behavior.

Primacy: When Good Redesigns Do Bad Things

By Jason Carmel | Dec 9, 2008 10:53:56 AM

One of my most favorite websites in the world, eMusic, just got a facelift. I love eMusic because they have a great selection of indie music, they are cheaper than most of the competition, and the music you get is legal and DRM free, as it should be. The reviews of the redesign have been generally positive: now eMusic sports some fancy AJAX-y interface bits, a wealth of third party content (youtube videos, reviews, etc), and a new music recommendation and personalization engine, among other things.

So why am I, a loyal eMusic customer, not happy with what are clearly improvements to the site?

The Primacy Factor

I've been a member of eMusic for almost three years now, and I visit the site like 27 times a day. I don't know why I visit the site so often. Maybe I'm afraid of getting in a rut, which at my age, can quickly lead to a well documented form of musical taste fossilization- the kind where I sit in a corner yelling at pesky kids to "turn down that noise," while I lament the crapification of music these days, and pine exclusively for Hair Metal, Grunge, Disco, or some other fixed aural remnant of a bygone and best-forgotten era.  Before you know it, I'm eating dinner at 4:30, I have a regular bingo night, and I can quote Matlock extensively.

When you visit a site so many times, you get comfortable using it. All of the rough user interfaces are smoothed over by the calm running waters of routine. Any glitches and inconveniences morph into idiosyncrasies and "power user" workarounds as you become increasingly efficient at dealing with the user experience. This is precisely what happened to me relative to eMusic- I adjusted how I browse and purchase music to conform to the old eMusic interface (warts and all) and did such a good job of changing my behavior over time, that this new, improved interface actually reduced my comfort level.

In the world of site-side optimization, we refer to this phenomenon as Primacy. If a customer routinely uses the same feature or traverses the same path on a site, then any change- even one that improves an experience- will negatively impact that customer (who now has to re-learn an entirely new method of doing things). Re-learning anything is annoying at first. As such, the short term effects of an altered experience could make a great improvement erroneously appear like it is underperforming relative to the old version, as these veterans struggle to adjust to the new (improved) world.

For eMusic, I've been biased by almost three years of the old site, and so I will need a lot of time to adjust before I become an advocate. Of course this is dangerous ground to tread for any site, since the people most at risk of defection following a significant change are the heaviest users.

How to address the Primacy factor

It is important for sites that experiment on areas with significant return traffic to not be discouraged by early results of those tests. As a test designer, your options for dealing with the effects of Primacy are to let the test run long enough for the veterans to get used to the new idea, or (even better) to segment the test by new and returning customer groups so you can see whether the design has a positive net impact for visitors who have not been biased by a previous experience. eMusic should definitely be segmenting here to reduce the risk of alienating hardcore users and testing to quantify the results of their new design.

I'm pretty sure the new design is better. It just might take me a while to get used to it.

Site Optimization: Lipstick on a Pig?, Or, Bacon and the Theory of Local Maximum

By Ryan Turner | Dec 5, 2008 12:34:33 PM

Cross-posted from Web Social Architecture.

 

Jason Carmel is a colleague of mine I learn a ton from. His expertise is in web site optimization--running experiments where he tests versions of web pages against each other to see which performs best. (Not to be confused with search engine optimization, improving a site's visibillity in search engines.)

Jason is a fairly unflappable guy. Nonetheless I recently started making an effort to get his goat. He gives me just enough encouragement that I keep going. The gist of my teasing is that optimization is nothing but a mechanical exercise to determine whether a red button works better than a blue button. "Glad to hear that red button worked out better by 2.84 percent Jason. The sum of your creative energy has produced yet another quarter million in revenue. You must really love your life, man. Hey, have you thought of trying one of those animated GIFs instead of a regular button?"

Fortunately, Jason is twice as nice as me, as well as twice as smart. He takes my ribbing well--and responds thoughtfully to the serious question underlying my teasing: We know optimization can move big numbers in terms of revenue, but can it do more than simply tweak pages to bump up conversion? Can it vet creative concepts? Can it maximize the creation of mutual value between businesses and customers? Can it help create more engaging experiences?

(image credit, found via Lee)

The short answer here, according to Jason, is that it depends, partly on what you're trying to achieve. If all you're focused on is moving business value measures, you're probably putting lipstick on a pig. But testing against value creation has the potential to uncover game-changing opportunities.

Here's an email exchange between Jason and me, in which he explains in a little more detail:

 

RYAN:

That whole web site optimization thing—isn’t it really just putting lipstick on a pig?

 

JASON:

I think “I hate you so much” might be a succinct way of responding, but I'll provide a little more detail:

Web Site Optimization is exactly like putting lipstick on a pig, but only if you start out with a pig. And if you are starting out with a pig, your opportunities for improving things are limited, and you’d be using the wrong tool to fix the problem. We are talking here about the concept of a “local maximum” which is a fancy, math term applied to mean “the best something can be within a limited dynamic.” Consider the aforementioned pig’s ability to fly, which, metaphorically speaking, is not particularly developed. We could take a pig and genetically modify it to be more aerodynamic. We could investigate building pig hang-gliders and attempt to train the smartest pigs to use them. But even in the best case, with the most aerodynamic pig, benefiting from the best training, and using the best pig flying technology, it will never fly as well as a bird. The best case flying scenario for a pig (the pig’s local maximum as far as flight is concerned) is nowhere near as effective as a bird’s. In that scenario, you’d be better off exchanging the pig for a bird at the start, rather than waste any time or effort teaching a pig to fly better.

Applied to the web: if a site sucks so much- if the goals and purpose are unclear, if the information architecture looks like my desk (at the moment), if the navigation is counterintuitive and the messaging has absolutely no intersect with the audience, then no amount of optimization in the world will make it right. The local maximum of that crappy site is too low for any optimization to matter. Or (even worse) you’d need the infinite number of monkeys to stop typing Shakespeare and to start applying experiments to your site to get the right combination where testing would make a real difference. Neither is very efficient. If your site is the pixilated equivalent of a pig, you need much more elemental help from a user experience expert first (know any?). Until you fix the fatal flaw(s) in a site, anything else you do will be throwing good money after bad.

Site Side Optimization works well in circumstances where the local maximum is high, but for some reason, the site is not achieving it. This can be due to single points of failure on the site, like a specific conversion path or page underperforming, or because the audience needs to be targeted more specifically, or because the existing content is stale/irrelevant. In each of these cases, experimental testing can make a huge difference. Optimization also works exceptionally well (and this is far more interesting to me) when applied as a method of trying out a new (and potentially risky) idea that could radically change and significantly improve an experience.  In both of these examples, the basic site is healthy, and the optimization program serves as a tool to reach its fullest potential.

 

RYAN:

But what I keep looking for is the way to test birds against pigs, not in the sense of which flies better, because as a user experience expert I do have the capability to predict the winner of that contest—but when I don’t have a clear sense of the best conceptual solution. For example, maybe I just can’t decide between eggs and bacon. Can optimization help design a better breakfast, or only decide between pulp and no-pulp in the OJ?

 

JASON:

Optimization can test more conceptual ideas, but it will be really hard to unpack the WHY after we determine which one wins. Most sites aren’t deciding between bacon and eggs, but rather between the bacon, eggs and hashbrowns with coffee or the granola, fruit and yogurt with yerba matte. If the former wins in a test, I don’t know whether it’s because of the bacon (which will usually win over everything) or the coffee, or because the person deciding had granola for the past three days, and would have taken ANYTHING other than more granola.

The other trick about testing high concepts in a website format is that you would have to build each solution to test them, which is usually more expensive than testing out wireframes or front-end prototypes in front of a more controlled audience.

 

RYAN:

First, you seem to be suggesting that a test win for the bacon breakfast might not imply extensibility for bacon breakfasts in general: That because, lacking control, the results might be idiosyncratic, they might not therefore apply broadly. Next week you might get a different result. My question is, why does that matter? And why does “knowing why” the bacon breakfast worked matter, as long as you know it worked.

 

JASON:

It is definitely a question I get from clients a lot. Why do I care about the individual elements of a variant- if the variant as a whole makes us more money, let’s just launch it and move on. I can’t fault the sentiment, but knowing why the bacon worked could lead to better tests, more focused messaging and (even more) cash money. I want to know that it’s the bacon by itself that is the motivating factor outside of all the other influences. Let’s say that we ran a test breakfast against a bowl of Total cereal and we tested bacon with powdered eggs as the experimental variant. Now let’s assume that bacon and powdered eggs lost to the control by 1%. You could take the position that we would do better to serve Total because we want to avoid losing that 1%, and you would be right. But what if you knew that the bacon by itself actually improved the breakfast by 15%, but the powdered eggs were so crappy that they hurt the breakfast by 16%, so you netted the 1% loss? If I controlled for all the variables in my breakfast, that knowledge would a) help me make a better breakfast overall (just serve bacon), and b) will also prevent me from throwing away a positive variable simply because it was paired with a really negative one.

I imagine you run into that a lot with prototyping and wonder how you deal with it in the UX world.  If a subject totally fails at a task, are you ever afraid of overcorrecting a prototype to account for it? Do you ever throw the baby out with the bathwater? How do you control for that?

 

RYAN:

A great question. One of the answers is that in usability testing, you're looking for usability problems. So as long as your test participants are representative of your users as a whole, major failures are, practically speaking, never anomalous. If your user population is one million, and one of the eight people participating in your test has trouble understanding some aspect of the interface, what are the odds they're the only person who's going to have that problem?

The other thing I wonder about is what happens when what you’re trying to accomplish is harder to measure than conversion (e.g. brand lift) or if you want to measure it over time (e.g. engagement). Especially in social media, it’s quality that matters, not quantity. You want to know how valuable your user-generated videos are more than you want to know how many of them you have. Can web site optimization help you get to answers?

 

JASON:

Ah, you and your social media. When are you going to come to terms with the fact that this whole thing is a fad? The future is in email, Ryan, and lots of ‘em. Mark my words.

Absent a more qualitative tie-in with optimization (surveys, satisfaction scores, etc.) you will be hard-pressed to get good data about branding or the impact of social media. But I’m not saying you shouldn’t optimize for branding or social media. I’m saying you need to get that qualitative kicker. I’ve done a few branding tests, and I think they provide some interesting feedback. But I’ve never optimized where a KPI has to be judged on quality (e.g., good comments vs. troll comments) or off the site entirely (e.g., buzz in the blogosphere). Sounds fun.

Site Optimization: Lipstick on a Pig?, Or, Bacon and the Theory of Local Maximum

By Ryan Turner | Dec 1, 2008 8:01:09 AM

(Cross-Posted from Web Social Architecture.)

Jason Carmel is a colleague of mine I learn a ton from. His expertise is in web site optimization--running experiments where he tests versions of web pages against each other to see which performs best. (Not to be confused with search engine optimization, improving a site's visibility in search engines.)

Jason is a fairly unflappable guy. Nonetheless I recently started making an effort to get his goat. He gives me just enough encouragement that I keep going. The gist of my teasing is that optimization is nothing but a mechanical exercise to determine whether a red button works better than a blue button. "Glad to hear that red button worked out better by 2.84 percent Jason. The sum of your creative energy has produced yet another quarter million in revenue. You must really love your life, man. Hey, have you thought of trying one of those animated GIFs instead of a regular button?"

Fortunately, Jason is twice as nice as me, as well as twice as smart. He takes my ribbing well--and responds thoughtfully to the serious question underlying my teasing: We know optimization can move big numbers in terms of revenue, but can it do more than simply tweak pages to bump up conversion? Can it vet creative concepts? Can it maximize the creation of mutual value between businesses and customers? Can it help create more engaging experiences?

(image credit, found via Lee)

The short answer here, according to Jason, is that it depends, partly on what you're trying to achieve. If all you're focused on is moving business value measures, you're probably putting lipstick on a pig. But testing against value creation has the potential to uncover game-changing opportunities.

Here's an email exchange between Jason and me, in which he explains in a little more detail:

 

RYAN:

That whole web site optimization thing—isn’t it really just putting lipstick on a pig?

 

JASON:

I think “I hate you so much” might be a succinct way of responding, but I'll provide a little more detail:

Web Site Optimization is exactly like putting lipstick on a pig, but only if you start out with a pig. And if you are starting out with a pig, your opportunities for improving things are limited, and you’d be using the wrong tool to fix the problem. We are talking here about the concept of a “local maximum” which is a fancy, math term applied to mean “the best something can be within a limited dynamic.” Consider the aforementioned pig’s ability to fly, which, metaphorically speaking, is not particularly developed. We could take a pig and genetically modify it to be more aerodynamic. We could investigate building pig hang-gliders and attempt to train the smartest pigs to use them. But even in the best case, with the most aerodynamic pig, benefiting from the best training, and using the best pig flying technology, it will never fly as well as a bird. The best case flying scenario for a pig (the pig’s local maximum as far as flight is concerned) is nowhere near as effective as a bird’s. In that scenario, you’d be better off exchanging the pig for a bird at the start, rather than waste any time or effort teaching a pig to fly better.

Applied to the web: if a site sucks so much- if the goals and purpose are unclear, if the information architecture looks like my desk (at the moment), if the navigation is counterintuitive and the messaging has absolutely no intersect with the audience, then no amount of optimization in the world will make it right. The local maximum of that crappy site is too low for any optimization to matter. Or (even worse) you’d need the infinite number of monkeys to stop typing Shakespeare and to start applying experiments to your site to get the right combination where testing would make a real difference. Neither is very efficient. If your site is the pixilated equivalent of a pig, you need much more elemental help from a user experience expert first (know any?). Until you fix the fatal flaw(s) in a site, anything else you do will be throwing good money after bad.

Site Side Optimization works well in circumstances where the local maximum is high, but for some reason, the site is not achieving it. This can be due to single points of failure on the site, like a specific conversion path or page underperforming, or because the audience needs to be targeted more specifically, or because the existing content is stale/irrelevant. In each of these cases, experimental testing can make a huge difference. Optimization also works exceptionally well (and this is far more interesting to me) when applied as a method of trying out a new (and potentially risky) idea that could radically change and significantly improve an experience.  In both of these examples, the basic site is healthy, and the optimization program serves as a tool to reach its fullest potential.

 

RYAN:

But what I keep looking for is the way to test birds against pigs, not in the sense of which flies better, because as a user experience expert I do have the capability to predict the winner of that contest—but when I don’t have a clear sense of the best conceptual solution. For example, maybe I just can’t decide between eggs and bacon. Can optimization help design a better breakfast, or only decide between pulp and no-pulp in the OJ?

 

JASON:

Optimization can test more conceptual ideas, but it will be really hard to unpack the WHY after we determine which one wins. Most sites aren’t deciding between bacon and eggs, but rather between the bacon, eggs and hashbrowns with coffee or the granola, fruit and yogurt with yerba matte. If the former wins in a test, I don’t know whether it’s because of the bacon (which will usually win over everything) or the coffee, or because the person deciding had granola for the past three days, and would have taken ANYTHING other than more granola.

The other trick about testing high concepts in a website format is that you would have to build each solution to test them, which is usually more expensive than testing out wireframes or front-end prototypes in front of a more controlled audience.

 

RYAN:

First, you seem to be suggesting that a test win for the bacon breakfast might not imply extensibility for bacon breakfasts in general: That because, lacking control, the results might be idiosyncratic, they might not therefore apply broadly. Next week you might get a different result. My question is, why does that matter? And why does “knowing why” the bacon breakfast worked matter, as long as you know it worked.

 

JASON:

It is definitely a question I get from clients a lot. Why do I care about the individual elements of a variant- if the variant as a whole makes us more money, let’s just launch it and move on. I can’t fault the sentiment, but knowing why the bacon worked could lead to better tests, more focused messaging and (even more) cash money. I want to know that it’s the bacon by itself that is the motivating factor outside of all the other influences. Let’s say that we ran a test breakfast against a bowl of Total cereal and we tested bacon with powdered eggs as the experimental variant. Now let’s assume that bacon and powdered eggs lost to the control by 1%. You could take the position that we would do better to serve Total because we want to avoid losing that 1%, and you would be right. But what if you knew that the bacon by itself actually improved the breakfast by 15%, but the powdered eggs were so crappy that they hurt the breakfast by 16%, so you netted the 1% loss? If I controlled for all the variables in my breakfast, that knowledge would a) help me make a better breakfast overall (just serve bacon), and b) will also prevent me from throwing away a positive variable simply because it was paired with a really negative one.

I imagine you run into that a lot with prototyping and wonder how you deal with it in the UX world.  If a subject totally fails at a task, are you ever afraid of overcorrecting a prototype to account for it? Do you ever throw the baby out with the bathwater? How do you control for that?

 

RYAN:

A great question. One of the answers is that in usability testing, you're looking for usability problems. So as long as your test participants are representative of your users as a whole, major failures are, practically speaking, never anomalous. If your user population is one million, and one of the eight people participating in your test has trouble understanding some aspect of the interface, what are the odds they're the only person who's going to have that problem?

The other thing I wonder about is what happens when what you’re trying to accomplish is harder to measure than conversion (e.g. brand lift) or if you want to measure it over time (e.g. engagement). Especially in social media, it’s quality that matters, not quantity. You want to know how valuable your user-generated videos are more than you want to know how many of them you have. Can web site optimization help you get to answers?

 

JASON:

Ah, you and your social media. When are you going to come to terms with the fact that this whole thing is a fad? The future is in email, Ryan, and lots of ‘em. Mark my words.

Absent a more qualitative tie-in with optimization (surveys, satisfaction scores, etc.) you will be hard-pressed to get good data about branding or the impact of social media. But I’m not saying you shouldn’t optimize for branding or social media. I’m saying you need to get that qualitative kicker. I’ve done a few branding tests, and I think they provide some interesting feedback. But I’ve never optimized where a KPI has to be judged on quality (e.g., good comments vs. troll comments) or off the site entirely (e.g., buzz in the blogosphere). Sounds fun.