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Mobile eye tracking - part 3 of 3

Tuesday, July 6th, 2010

Here is a brief overview of three approaches to analyzing mobile eye tracking output.

1. Watch the video output and manually tally the ‘hits’. Slowly playback the video and count how often the point of gaze lands on the area of interest. This gives you a general sense of what is looked at and what is ignored, but only limited conclusions can be drawn with respect to the amount of time spent viewing each AOI. One could slow the video down further (frame by frame) and take note of the time stamp for each time the participant starts looking at one particular region. This is enormously tedious, especially for video segments longer than 2 minutes and with more than just a handful of AOI. It is also imprecise; the time stamps are obtained at 30 fps, with up to 5 or more data points lost with each frame, before and after the time stamp. There is also substantial opportunity for human error in tallying hits or recording time (I speak from experience).

cross-hair

2.Identify fixations, then tag AOI based on fixation. In this approach the analysis software identifies fixations following a particular algorithm developed by the manufacturer. Then, thumbnails are generated for the video segment during which the fixation occurred. The research analyst tags each thumbnail with an identification of which AOI is being looked at during that fixation. The output sums the tags and can compute dwell time based on the length of the fixation. This is all very promising and most certainly not as time consuming as option 1. Nonetheless, each data file needs to be addressed individually and to a certain degree, manually. A greater concern, in my opinion, is the reliance on the identification of fixations to then in turn make conclusions about viewing attention and dwell time. Fixations, by definition, assume a moment of movement cessation of the pupil as the eye fixates on an object. What if the object is moving and the pupil is following in smooth pursuit? How is the fixation captured? Experience with this approach has left me wondering why the dwell times on AOIs total up to only a fraction of the total testing session. What did the participant look at the rest of the time? Did he keep moving his eyes so quickly that he never truly looked at anything? Hard to imagine.

3. Draw regions ‘on’ the output videos and process the data against the defined AOI. This is essentially the same idea as most analysis approaches with desktop systems. Identify the AOI in the scene and the software will tally when the x,y coordinates of the point of gaze fall within that region. There are two approaches:

a. Draw each AOI ‘by hand’ for each frame. This is can be reasonable for a small number of AOI and a fairly stable scene. The markers for the AOI can be dragged and re-sized in order to consistently overlay the actual regions in the scene as the video plays. It is potentially more time consuming than option 1, but is about as precise as mobile eye data analysis can get.

7-6-2010-9-28-14-am

b. Use a form of image recognition software and have the software identify AOI. This approach works well if there is some contrast within the scene and you have primarily only one scene to deal. Here you create a video of the scene and identify the AOI for the software. It, in turn, uses this as a key to automatically identify the AOI in the recorded data files. One the AOI are identified, the process is the same as in (a) above. This potentially is a very quick process and results in data that is potentially very precise. It is almost too good to be true.

So that is where we stand at User First with our understanding of mobile tracking. We welcome corrections, other perspectives, elaboration, and the sharing of experiences as it pertains to this topic as we are ourselves learning each day about what is available, what is in the works, and how we might meet client needs in the future.

As we move forward with mobile tracking and expand on our experiences with different analysis methods in particular, we will keep you posted

Loop 11 Adds New Features, and Why They Rock

Monday, June 28th, 2010

If you have ever ran an un-moderated usability study you know that most solutions don’t provide advanced features for research panels or the functionality for custoloop-blog-imagem analysis. We personally use Loop 11 for un-moderated usability studies. Although we love the tool and the great insights we get we always sigh when it’s time to crunch the numbers. But not anymore!! Loop 11 just released new features that will make setting up and analyzing un-moderated usability studies a breeze. Within this post we will review the changes and why they are important.

1). Tracking participants using unique IDs
When using a research panel for a usability test tracking individual participants is important not only for segmentation but also to know exactly what participant completed the study and should be paid their incentive, terminated, or was over-quota. Previously, I had to ask the participant to fill in their Ids and as you can imagine some participants didn’t answer correctly. I would also download all of the IP address Loop11 gave me and try to match it back just to double check, which took hours!

Why this rocks: Now all you have to do is customize the end of the URL with a unique ID for each participant. You can now easily pay your participants and segment your users without having to invest a lot of time.

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2).Re-Categorise multiple URLs at a time
Instead of re-categorising one URL at a time you can now re-categorise multiple URLs at the same time.

Why this rocks: Some websites have dynamic URLs, and you can’t set-up a goal for every possible combination within Loop 11. Now instead of re-categorising one URL at a time you can select what pages you want to re-categorise. This saves time during the set-up process of the study and analysis because once you re-categorised the URLs the data re-configures!

3). Pop-up invitation controls
If you are recruiting your participants by intercepting them via your website; you now have a feature allowing you to control the percentage of visitors that you ‘invite’.

Why this rocks: Selecting what percentage of your visitors is super valuable because too high a sample rate might mean you are surveying more visitors than you really need to in order to get valid results. Remember, too small a sample could produce results that lack statistical validity.

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4). Individual participant path analysis in exports
Full path analysis of individual participants can now be exported into reports. This allows you to segment the conversion funnel by your top segments, whether you segment participants by female only or participants that clicked abandoned when they actually succeed the task.

Why this rocks: In the past, the way we collected this data was by using the interface within Loop 11 and simply copying and pasting each link for each participant for each task. Needless to say that it took awhile and there was lots of room for human error. Now all you have to do is download the report. Once you have this data you can segment your funnel analysis to view how different users completed or failed the task based either on their demographics and/or geographic.

5). Individual responses for Rating Scale Matrix questions
The results for rating scale matrix questions, while always available at an aggregated level were never available so you could see the individual participant responses.

Why this rocks: Let’s say you asked a gender question because you wanted to know how many females or males participated in your study. Loop would just tell you the percentage but not who was female or male. Now you can download the report and know who was who. This type of information is important if you had follow-up question after the task, you can find what type of user rated the task easy or difficult. Or if you didn’t use a research panel you can start your test with some demographic questions so your analysis can get a little juicer with segmentation.

What is your experience with Loop 11, are you excited about these changes? As Toby Biddle would say “Happy Testing”!

If I Had to Pick One Report, This Is It…

Monday, June 21st, 2010

About two years ago, Avinash Kaushik asked “if you had to pick one web analytics report on a deserted island which report would you take.” I totally agreed with his selection of the outcome by all traffic sources report as I am also a fan of outcomes. (Show me money!) But what if I got stranded on this island with an iPad and because of this I got to cheat only a little? This post is about my one favorite report in each of the top three web analytics tools that I would.

google-analytics-blog1

1). Google Analytics-one of the coolest reports just released is the intelligence report. I have been adding upper and lower control limits to my dashboard for about four years now. It really helps me know whether an upward or downward trend is something I should be paying attention to. This new report not only automates that process but emails me when there is activity above or below my limits. I would take this report (although a dashboard, so it is a cheat) because I am assume that before I got stranded I took the time to set up this alerts about all the important KPI’s my company has, so I would still be on the pulse of the companies objectives.

coremetrics-blog

2). Coremetrics- ( let’s assume that we are an eCommerce website) one of the reports that I always go to is the top abandoned products report. Talk about leaving money on the table, this report tells you exactly how much -down to the last penny! As seasons change revenue is expected to change, but what if you knew what products online users wanted but didn’t purchase last year. Would you start rolling out some landing page test or re-design the products check out funnel to ensure that this year’s sales don’t follow the same pattern? Why I would take this report, simply because it is actionable. It lets me know immediately what users want but due to either bad usability or messaging and change it.

discover

3). Omniture- pathing analysis is something that I love to look at especially using segmentation (so this is why it’s a cheat). In the example above I selected my Facebook visits as my segment and can see exactly what path they took and see what the fallout rate was to my success pages. This type of information lets me see if my content has continuity and analyze key website process flows in hopes of identifying opportunities for improvement. From a usability standpoint I love this report because it will help me identify how user’s navigate through the website and using segmentation will allow me to find key traffic drivers that maximize ROI.

What about you, what report would you take? would you also have an iPad and have one workaround this question? I would love to hear your thoughts or comments.

Mobile eye tracking - part 2 of 3

Monday, May 31st, 2010

Challenge 1: Make the eye tracker small enough to carry about, secure enough to prevent shifting with movement and discrete enough to not make a scene.

Desktop eye trackers include a monitor, a series of cameras trained on the eyes that either stand alone or are built into the monitor, and a recording unit of some kind, both for a steady stream of eye data and video capture of the monitor screen. Mobile trackers include the same, but now a monitor is not needed; instead, there is an additional camera unit, the scene camera. This camera records the scene as the participant encounters it and needs to be ‘attached’ to the user close to same plane and position as the eyes. This is potentially a lot of equipment that now needs to be carried around by the participant.

SR EyeLink w/ scene camera

SR EyeLink w/ scene camera

One of the earlier mobile tracking systems that became available and was reasonably accurate was a modification of the desktop eye tracking system. SR Research took their EyeLink system and added a scene camera. This worked because EyeLink was not a remote system; it was a head-mounted unit and had room along the headband to host a scene camera. This head-mounted unit did not include the recording system and thus was tethered to a processor and hard drive. The cable was rather thick (thickness of a finger) and was limited to 40 feet. The recording system could be placed on a cart and with a long extension cord, could be pushed around after the participant. This system was certainly secure enough, but not designed for mobility or discretion. It was effective for small spaces, such as flipping through magazines, considering a display stand, or evaluating a single shelf set or package in hand. Nonetheless, the headgear was rather cumbersome and definitely drew attention.

Courtesy SMI

Courtesy SMI

Luckily in the last 1-2 years there have been tremendous developments. Scene and eye cameras are significantly smaller and lighter and can be attached either to a cap or to a pair of glasses. Mobile trackers are still tethered; wireless systems are in the works, but so far the data streams are too heavy (with up to 100 data points per second and video from 2 cameras at 30 frames per second). Nonetheless, the cables are small, not much larger than those to your ear buds on your iPod. And, more importantly, they are tethered to equipment that is substantially less bulky - usually a recording device less than half the size of your typical laptop. This can be easily carried in a pouch that hangs over the shoulder of the participant or is otherwise attached.

Courtesy ASL

Courtesy ASL

Which approach is more effective - glasses or wearing a cap? Glasses have certain appeal because they are smaller and less noticeable. With proper straps these glasses can be secured so vigorous head movement does not shift the cameras about. Camera movement can result in a significant and undesirable shift in the calibration (i.e. what the data or video indicates the user is looking at is no longer what the user is really looking at). Glasses are more easy to secure than a baseball cap.

But glasses pose certain problems. They cannot be used if the participant wears prescription glasses (believe me, we’ve tried!). Further, the positioning of the eye camera and cut of the glasses is designed for a certain face structure. Deviate from this standard and the edge of the opening cut into the lens falls between the camera and the eye, distorting the camera’s view of the pupil.

Wearing a baseball cap with cameras attached offers solutions for both of these

Courtesy SMI

Courtesy SMI

challenges. The camera units attached to a cap are more flexible and offer more options for adjustment, allowing for accurate tracking of virtually any type of participant, young and old, with or without glasses, and any nationality. What about the camera shift? We’ve been reassured by the manufacturer that camera shift is monitored and seamlessly corrected via the tracking of the corneal reflection. If this is indeed the case, we’re sold! We have the opportunity to test out such a system in the coming weeks.

There are different mobile trackers available, and they differ not only in the hardware; some use dark pupil, some light, some with or without corneal reflection. Steps to calibrate, record and monitor in real-time varies by manufacturer. The robustness of the systems, especially if tracking in daylight or in particularly ‘bumpy’ environments (such as road car rallies!) varies. Detailed discussion of this will be dealt with in another post. For now, let me just say that not all mobile trackers are the same and do need to be carefully evaluated.

Call to Action Buttons: Designed to Impact User Experience

Friday, May 21st, 2010

I recently finished up an un-moderated usability test; I usually ask open ended questions after every task. Part of the fun and the blog-image-6madness is making sense of the all the juicy comments. For this particular study, I was taken aback by how many respondents mentioned that the website task longer took because all of the” good links” were on the bottom of the page or some were confused by the links themselves.

This got me thinking about Seth Godin (The Big Red Fez) helping users find the banana in less than 3 seconds.

What the website needed were call to action buttons (CTA) above the fold, and the links needed intuitive labels names (“read more” wasn’t cutting here). Calls to action in web design are meant to make people take an action and in user experience they are meant to make a task easier. Creating effective call to action buttons that grab the user’s attention and entice them to click can be challenging. This post will share some quick effective techniques. (For best practices read Smash Magazine’s post on design awesome CTA’s)

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Location, Location, Location,

Just like real estate, placing CTA’s in a prominent location such as the top section of a web page can lead to higher return of investment because users will likely notice it or remember it later, after they have looked at the site’s content. Another way, that CTA’s can be utilized is within the content itself. Why would a user want to read and scroll through the whole page if they have enough information and ready to proceed?  Call to actions buttons within the content in my experience result in user taking action because  they have already skimmed enough content. If you need more than one page to convince your visitors to take action, feel free to repeat yourself. If you offer a product tour or use several pages to explain complex features and options, place your call to action in a consistent position. This way visitors will know where to go when they are ready to take action.

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Language

Having a great call-to-action buttons is not as easy as just designing one from the best practices CTA guide (i.e. using irregular shapes, color contrast, larger sizes),  a great design  with the right language will help  guide users to follow a particular path and get to the valuable information they seek.  Labels on the buttons includes everything from simple things like “buy now” or “add to shopping cart”  and if you’re in the B2B vertical to  ”Download This Whitepaper”  or more specific wording.  A big pink button is pretty hard to miss but the example used design to catch the readers eye but the language of the offer  helps the user make the descions on whether or not they should download this software.

Have you been testing your call to actions? What combination has worked for you, would love to hear your experiences.

Mobile eye tracking - part 1 of 3

Tuesday, May 18th, 2010

For many years eye tracking has been limited to a controlled, virtual environment, making precise data collection and analysis relatively simple:

  • the computer screen doesn’t move,
  • participant movement is limited,
  • and stimulus presentations on screen are generally consistent across participants.

The biggest challenge researchers face is correcting for head movement (turning sideways or leaning forward) and managing point of gaze data on sometimes unexpected dynamic stimuli (pop-ups, animated ads, video, scrolling, etc.).

Nonetheless, it was relatively simple to track samples of 100 or more and evaluate their viewing pattern as they looked at on-screen presentations. Dynamic backgrounds posed a challenge, but as long as all participants looked at the same stimulus, it wasn’t too big of a deal to analyze this. It was time consuming to identify the areas of interest (AOI) for a background that is constantly changing, but once they are identified, everyone’s data can be run against the same AOIs (as identified, for example, in a television commercial). The output is then the same as for static backgrounds: precise dwell time and fixation information for areas of interest that can be aggregated across all participants. And this can easily be plotted on a static image of the background for visualization.

This type of testing was great for websites, TV commercials and software usability testing to name a few, but was less realistic when evaluating stimuli such as shelf displays, package designs, magazines or products in hand. This posed a problem; a golden rule in user research is to test real users in real environments. Eye tracking participants looking at virtual shelf displays on a computer screen just isn’t the same as eye tracking them as they look at actual packages on a shelf (though this certainly is up for debate).

Mobile eye tracker - image courtesy http://www.mangold-international.comThus came the shift to mobile eye tracking - recording a person’s point of gaze as he or she is moving about in a real, 3-D environment. This could be selecting magazines off of a shelf and reading them, moving about a store selecting products off of a shelf, or interacting with signage at a baseball game. The same technology could apply and has been modified for this type of testing, but there have been a number of obstacles along the way involving either the hardware or the software:

  1. Hardware: While hardware has been reduced in scope and size, a fast processor with a big enough hard drive is still needed, as are at least two cameras (one for the eye, one for the scene). The connection between computer and cameras needs to be wireless or all be so lightweight that it’s easily portable. The cameras have to be secured to the head in a way to limit any shift between camera and eye. How to manage these limitations effectively?

  1. Software: The calibration process and recording the data is much the same. The challenge is in the follow-up analysis. How do you identify regions and analyze point of gaze data when the background is constantly changing and is unique for each and every participant? How can you identify AOI rapidly and accurately with such variability? How can we aggregate data across participants when the stimuli varies so significantly? Can it be done to match the stable scene analysis that we are accustomed to, or is it necessary to make a significant paradigm shift in how we approach mobile eye data?

Different manufacturers and engineers have approached and managed these obstacles in unique ways. With the next few blogs I plan to explore that a bit further. I can only provide information based on personal research and experience, and am eager to hear more about experiences others have had. It’s an exciting new technology; although it has been around for years, there have been and continue to be substantial developments that bring this research approach more to the forefront.

Testing Assumptions-Eye Tracking Approach

Monday, May 17th, 2010

 Testing assumptions can be challenging; it can be difficult to put some of our strongly held beliefs under a microscope to evaluate and question. However, being able to discover a wrongly held belief and subsequently improve design or user experience because of this new information is extremely valuable. 

Eyetracking data visualization

Eyetracking data visualization

Recently we tested our and our clients’ assumptions. We conducted a study on how consumers develop their perceptions: what features do they look at and evaluate as they consider a product on a given dimension, such as size or functionality. In this case our client was interested in:

  • perceive the width of a product.
  • how consumers perceive the height of a product.
  • what aspects are looked at as a judgment is made.

Participants were asked to evaluate a hamburger on the dimension of” height”. The assumptions were that users would scan the hamburger from top to bottom; that makes intuitive sense. If you want to determine how tall something is, wouldn’t you look from the bottom to the top and evaluate the distance? A fair assumption, but in this case, incorrect.
*We simulated the desktop eye-tracking portion of the study with the hamburger images.

 
Here is a heat map from our hamburger study; it is very similar to the output from the actual study. The areas in red indicate a high concentration of viewing attention; this is what users spent most of the time looking at. The areas in yellow, or those that have no color, were looked at less. This heat map clearly suggests users spent their time evaluating the center of the hamburger, not the top and bottom.
A closer look shows that there are a few hot spots near the top and bottom of the bun, but they are small and likely due to viewing attention of only a small portion of participants. This is confirmed by a different type of heat map. This output plots the number of participants who looked at a particular element. Notice the concentration of participants who looked at the center of the burger, rather than the top or bottom.burger heat map

 These surprising results prompted us to start asking questions; we followed up with participants and asked them why they were looking at the center of the burger. They explained that without a reference to compare the height of one to another, they instead evaluated each component – the height of the patty, the thickness of the tomato and layer of lettuce, the scope of each bun half. A fat patty would suggest a taller burger.
A few caveats and new questions (good research always raises new questions). These are considerations to investigate further; some also underscore the importance of thorough pilot testing (another topic for another time).
1). Did the wording of the question somehow inadvertently guide them to evaluate the center of the burger more than the overall height?
2). Should the image be presented differently so that the center of the burger isn’t in the center of the screen (thus automatically grabbing attention)?
3). How would viewing patterns differ if burgers were presented side-by-side and how could that output be interpreted?
4). If we had asked participants, without eye tracking, how they evaluate the height of a hamburger, how would they have responded differently? That is, were they aware of their own viewing patterns?

What do think? Have you ever been challenged to test an assumption, what were the outcomes?

The Five Elements of User Experience Management

Tuesday, December 8th, 2009

If you have ever heard User First speak you know that we consistently use the term “User Experience Management” when we talk about being more user centric. It’s not a word we created nor is it just a buzz buzzword around our office. User Experience Management is a way of thinking that encompasses everything we do. You might be asking “but what is it”? We welcome you to read our multi-phase approach and understand our process.

1). Analytical DNAUser Experience Management |User First

Information is vital in today’s online market. Website analytics can help support paid pay click initiative, monitor site performance, or optimize email campaigns. We know that whatever you use data for it should first be trusted information and second be able to tell a story. With every single project we first dive into the data ecosystem and help clients realize the value of their online customer intelligence. These insights provide visibility into your customer touch-point on your website to improve website performance.

2). Testing, Testing, 1-2-3

One of the smartest ways to make design improvements is by letting the customer be at the helm of the design. Landing page optimization is all about finding the right combination of elements that enhance the user experience. We get a good grasp on testing the right elements and start fine tuning top landing pages. By discovering how customers interact with your website we are able then to conclude questions that will guide an online intercept survey or a usability test.

3). Hearing it from them

We believe that the stories people tell about what they do and how they do it contain information vital to designing good interfaces. Whether doing usability test leveraging eye-tracking or simply asking the receptionist to download a PDF, getting feedback lets us create visual designs and websites that solve user’s problems.

4). Building Stories

Any design and development improvement that we make is based on the findings and insights from customer’s feedback and behavioral patterns. But before one design is drafted or is wireframe created we use storyboards to layout and organize users activities and to stimulate different thinking. These steps ensure that we generate the most usable and engaging website.

5). Passion

Our desk might be a bit of a mess and we might argue at time about using flash, but we are passionate about creating effective and memorable user experiences.

If you had any questions about user experience management or had any experiences getting your team to become more user centric please let me know. You can also find us on twitter at UserFirst

Onsite Behavioral Targeting: Friend or Foe?

Wednesday, October 14th, 2009


Recently a friend ask me what my thoughts were on using onsite behavioral targeting and if I would recommend it to my clients. I told them, to be honest I have only tracked and monitored the results for a massive eCommerce website. Not having enough knowledge about onsite behavioral targeting I couldn’t make any recommendations to my clients because I know what works for one website might not work for another. The question got me thinking and reading more about what behavioral targeting was. I found that there were credited articles for it and against. I thought I would share my research findings and hopefully the material will help you make the decision.

First let’s define onsite behavioral targeting: it is simply personalizing the experience for your visitor by serving ads that are most relevant to them. The theory is that by presenting each individual user with a personalized optimal experience, it will increase their likelihood of taking the desired action. Most platforms are built with data compiled from clickstream data and IP information or cookies.

There is a saying in the ad community which is “start where your audience is”. Onsite behavioral targeting can greatly benefit an organization’s online marketing program and Forrester Research reported this emerging category as the #1 area for planned investment. The ability to reach at an individual user-level with the right messaging with minimal human interaction is one the highest reward but other features of the platform are:

  • Improved acquisition spend
  • When visitors are engaging with your site, their actions and behaviors are captured and incorporated into the persona profiles
  • Real time data mining, reporting and analysis
  • The automation of optimization
  • Relevant content will always be king

A recent study from researchers at the Annenberg School for Communication, University of California Berkeley School of Law and the Annenberg Public Policy Center reports that most internet users don’t want tailored advertisements. When Americans are informed of three common ways that marketers gather data about people in order to tailor ads, even higher percentages— between 73% and 86%—say they would not want such advertising. Other than Private Privacy issues here are some other factors to consider:

  • Some organizations have not fully created and implemented their persona’s online
  • Most effective for organizations that have sufficient size / scale to provide a return on the required investment
  • Implementing a behavioral targeting program can be difficult (may need extra resources)
  • Requires human intervention to update creative, offers, and modify business rules

So after reading about onsite behavioral targeting would I recommend it to my clients? The jury is still out on this one. I only say that because I can’t stop thinking about Jakob Nielsen report on banner blindness. It states that if users are looking for a snippet of information on a page or are engrossed in content, they won’t be distracted by the ads on the side. I sort of agree but does your website use  behavioral targeting tactics for ad banners? If so please let me know what your experiences have been!

You can find us on Twitter and reply there as well: UserFirst

Keeping up with the Joneses, a Website Redesign Story

Monday, September 28th, 2009

Summary: If you’re tired of your website’s design and you think you NEED a redesign remember that users spend 5 hours a year looking at a website at best. Successful user interfaces are designed to improve functionality of the familar design and evolve the user experience. Taking a cue from Amazon, incremental changes to the website’s design just might be the best approach.

Lately, we have had people requesting information on eye-tracking for their latest website redesign. At first glance I thought it was great that companies were getting customers involved.  Then I started thinking about what brought on the redesign project, and as analyst I wondered if these companies have set strategic goals that will not only measure the cost of doing the redesign but successful measure user’s satisfaction (example KPI: decrease cost per new customer by 5%).

I have seen and heard of many website redesigns that unfortunately were trying to “Keep up with the Joneses”, embarking on a website redesign only to keep up with the competitors. I think this happens more often than not. If five out of your six competitors have made their website look better, fresher, and more au courant then I am pretty sure a request for a redesign will be coming soon. This “me too” syndrome can simply  come from being over exposed to the website and feel like the website design is dated and tired. I recently read Jakob Nielsen’s post on Fresh vs. Familiar Redesign (a must read in my opinion), he mentions that the number of “exposure hours” usually starts the redesign off on the wrong foot.

When embarking a redesign project  people often forgot about the two most important things:

1). How will this help the user’s key usability attributes (learnability, efficiency, and satisfaction)
Users usually spend no more than 2-3 minutes on a website and at best spend five hours a year looking at a website. When visiting websites or using applications, they don’t spend their time analyzing or admiring the design. They focus their attention on the task.  Changing the design purely to “stay fresh” vs design that enhances usability results in expensive changes that, after all is said and done, don’t really help the business.

2). How will the company’s KPI’s (key performance indicators) be redefined for success
The analytics team and the design team may still  work in silos, but to justify a redesign these teams should open their doors to discuss the purpose of a redesign and clearly define how it will help increase business goals. Understanding the current state of your website’s web traffic data and feedback from users will help you monitor the success of a new site architecture, navigation, and/or design.

When it comes to redesign, being au courant isn’t always better. Major overhauls often generate a lot of resistance and can even upset your most fervent users. That’s why some major brands on the web, such as Amazon, don’t redesign their websites anymore. In contrast, they make incremental improvements that create designs that really pay off for the organization (most changes can be implemented quickly by your team and cost less). In the short-term the design gets done, but the team ends up doing it all over again months down the road.

Where Experiences Come First

Sure, people make a snap judgment about a website based on its look and feel, but keep your KPI’s in mind. If your user satisfaction ratio is up then establishing a new site design might not be worth the cost. However, if a major redesign is on the horizon suggest usability or eye-tracking testing on wireframes or prototypes. These tests can help reduce the need for  risky major re-launches. Simply focus on the areas that need improvement.
User First loves hearing about your experiences. Please let us know if you are planning a website redesign or have gone through one recently!

Other insightful posts that I found on website redesign:
Seth Godin Blog: Things to ask before you redo your website
Grokdotcom Blog: Website Redesign Roundup
User Interface Engineering Blog: Thinking in the Right Terms- 7 Components for a Successful Website Redesign