Monthly Archives: November 2010

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Tomorrow, November 11th 2010, is World Usability Day. Why should you care? Because usability can directly impact the bottom line of your firm, helping to increase conversion, leads and ultimately revenue. And in this time of the ‘great recession’ nothing should be more interesting to you and your firm than increasing conversion (revenue), especially if it can be accomplished in a manner that is relatively low in cost and fast in results.

World Usability Day logo

The purpose of World Usability Day is to provide education and information opportunities throughout the globe about:

  • What usability is
  • Why usability works
  • Where usability can make a difference

This year’s theme for World Usability Day is ‘Communication.’

In my little corner of the world, the Austin Texas area, the World Usability Day event will be held at MutualMobile, a firm dedicated to building applications for the mobile marketplace. This fits well with the theme of Communication as there’s been tremendous growth in the number and usage of mobile applications to aid communications between individuals and businesses.

Of course, usability goes well beyond the bottom line benefits of improving conversion and revenue for firms. The benefits of a more usable app / tool / website etc. include:

  • Increasing customer satisfaction
  • Reducing friction and churn
  • Decreasing psychological and physiological stress

I’ve surveyed hundreds of companies in the past year, and the good news is many of them recognize the importance of usability. Many seek software or hardware development team members with experience in usability. However, there are still many who don’t.  Frankly, there is still much work to do. Not a day goes by when I, and probably you too, come across an application or website that is not easy to use, causing us unnecessary stress and inhibiting or halting our ability to complete our tasks.

I say this about usability…

“Education about usability and its benefits is the single most important tool for a more user-friendly and satisfying world for each of us.”

I urge you to seek out and go to a local World Usability Day event near you. And if you are unable to make it to one, I hope you’ll stop by the World Usability Day website to learn more about usability and what it, and you, can do to make things better for you, your firm and our world.

Why Jakob Nielsen got it (mostly) wrong

Summary: Do not assume, as Jakob Nielsen’s Alphabetical Sorting article implies, that A-Z listings are usually not the best way to present a list to users. Instead, carefully evaluate your typical users and their tasks, based on the following listing selection parameters, and make a more informed decision about whether to use A-Z listings, or something else.

It’s not every day I disagree with Jakob Nielsen, arguably a living legend, a guru, an Einstein of usability, but today is that day. I’m a big fan of Jakob Nielsen and his Alertbox articles, but this time I think he left out critical information in an article that could lead you astray. So I am here to humbly set the record straight, and fill in that missing information.

Nielsen photo Nialkennedy via Flickr Creative Commons license
Jakob Nielsen got his A-Z listings article (mostly) wrong

Jakob Nielsen’s October 4, 2010 Alertbox titled ‘Alphabetical Sorting Must (Mostly) Die’ is in my opinion (mostly) wrong.

Actually, it’s not that it’s so much ‘wrong,’ more that it completely left out crucial information on a rather complex and critical choice designers and information architects face every day. That choice is:

‘What type of ordered listing should be used so a user can pick from a set of items?’

Here’s Nielsen’s summary from his Alertbox article:

‘Ordinal sequences, logical structuring, time lines, or prioritization by importance or frequency are usually better than A–Z listings for presenting options to users.’

Note the word ‘usually.’ I take issue with the word ‘usually.’ Now if Nielsen had picked any other words, such as; ‘frequently, somewhat, potentially, sometimes, typically,’ or other variations, I would have been fine (mostly).

But the word ‘usually’ infers that other choices are better more often than A-Z listings. That my friends is (in my humble opinion) just plain information architecture crazy talk.

Determining which type of listing to use should not be made until more information is know about the typical users and the task or tasks that are to be performed. For some instances, A-Z listings won’t be the best solution, but for many others, they will.

Alphabetical or ‘A-Z’ listings are your friends:

Alphabetical listings, aka A-Z listings, are incredibly powerful and useful sorting systems and can be an information architect’s best friend. On a side note, the actual term to use when defining ordered listings of items (such as in A-Z listings) is ‘collation,’ as defined by WikiPedia.

What is missing in my opinion from Nielsen’s article is identification of the parameters that should be evaluated prior to making the decision about what type of listing (A-Z or otherwise) to use.

Parameters for evaluating a type of listing to use:

Domain Expertise of Persona

By Domain Expertise I am referring to the general knowledge the Persona (typical user) has in relation to the subject matter of the website or application. For example, a consumer shopping for new tires for a car may have very limited domain expertise regarding the subject, but a tire-store owner may have very great domain expertise about the subject.

A-Z sorting may be helpful in situations where Personas with lack of expertise in the domain can be provided all the possible choices, allowing them to scan the listings of items. Indeed, this is a very common and useful way to present groupings of names or Brands, as demonstrates with their ‘Tires by Brand’ A-Z listing as shown below.

Although A-Z listings of brands of tires may be better for the limited domain expertise user, an alternative listing technique, such as Frequency of picked items, might be better for the more advanced domain expertise user, our tire-store owner for example (assuming such frequency of use data is known). uses an A-Z sorting of Brands

As shown above, the A-Z listing of Brands on can be helpful for low domain expertise users.

It is important to consider the domain expertise the Persona has prior to making a choice of a selection tool because familiarity (or lack thereof) of subject matter may impact the ease of use of various sorting systems. Enabling low domain expertise users to ‘reorient’ themselves by using an A-Z listing might be helpful when users are trying to scan the total set of choices, in an attempt to pick from one or two choices from the entire set.

Specific Label Expertise of Persona

Going into a bit more detail than with Domain Expertise, by Label Expertise I refer to the knowledge the typical user has in regards to the actual words (labels) used in a listing of items. For users with limited exposure and knowledge about the meaning of specific labels, an A-Z listing can be a helpful way of enabling them to familiarize themselves with the listing terms. Likewise, users with advanced knowledge regarding the topics may not find it necessary to have an A-Z listing, and may find it easier and faster to use frequency of use or other types of sorting schemes.

Side note: A best practice is to use card sorting (including completed and blank cards) with participants who closely match the Personas when determining the words to use in a listing of items.

As an example, Fluitek uses an A-Z listing of industrial filtration parts on the home page. This enables users who may have limited understanding of the definitions of some terms to be able to quickly scan the entire list of choices in an orderly fashion. This enables them to easily navigate the choices and thus zero-in on likely candidates.

Fluitek uses an A-Z listing of items for industrial filtration parts

As demonstrated above, uses an A-Z listing of items for industrial filtration parts.

However, as seen in the next example from, a non-alphabetical listing of products is used down the left hand side of their product page. Whether to aid recognition of terms or to help users navigate to the specific product, or both, a visual categorization to the right is also used on the page.

The visual categorization uses thumbnail photos of each product type under its corresponding label. This visual aid can help provide additional clues to help users find the correct item, if they are unfamiliar with definitions of labels. However it is a limited use tool as it is problematic if more than a handful of thumbnails are presented for users.

Pur uses a non A-Z listing on the left, and thumbnails on the right, for product identification

As shown above, the products page uses a non A-Z listing on the left, and thumbnails on the right, to assist with product identification

Listing Tool Frequency of Use

Another parameter that can be considered when choosing a listing type is the amount of listing tool usage a typical user experiences, or will experience. Frequently utilized listing tools may not require alphabetical ordered listings, and indeed other forms of ordering, such as listing item frequency of usage or even keyboard entry may be a better choice (picking a U.S. State as an example, keyboard entry of the two letter abbreviation of the State can be significantly faster and less error-prone than choosing from a drop down list).

For selection tools that are used rarely, A-Z listings can provide a more effective and efficient way to present items in the list.

As an example, the Texas Department of Motor Vehicles uses an A-Z drop down menu tool of ‘How Do I…’ choices to assist users in finding the right information for various actions. Most likely, a typical user rarely needs to visit their site and use this selection tool. Thus this tool’s use of an A-Z drop down list helps organize the total set of choices for those low tool frequency users.

Texas Department of Motor Vehicles uses an A-Z drop down menu tool

As shown above, the Texas Department of Motor Vehicles uses an A-Z drop down menu tool for their ‘How Do I…’ selection listing.

Listing Item Frequency of Use

Akin to the tool frequency of use, the listing item frequency of use is the frequency of which users pick each item in a listing of items. With a listing of items by frequency of use typically the most picked (popular) items are placed at the top of the list, with less popular items below. This is a handy way to present listings, especially when the popularity data directly links to the actions of the typical users.

However, where design teams get in trouble with popularity based listings is when they make assumptions about popularity, without the actual user contextual data to back it up. Design teams who simply utilize web log data to extrapolate which items are used ‘most frequently’ often don’t realize their data may be suspect. For example, some users will just ‘pick’ items that are near the top of the list because they either do not know which item to pick, or don’t care.

As a case in point, while at Blue Cross I examined the most commonly picked ‘no-sale’ reason codes from the sales call-center’s non-sales reports. My goal was to use the no-sale reason code data to try to better predict which advertising sales audiences might provide better sources of leads, thus better sales.

The Agents had to choose a non-sale reason code from a list of over 40 items. They did this by using a drop down tool to select from this long list of codes. The listing order was by frequency of use, thus the most picked reasons were at the top.

Photo of a call center by via flickr creative commons license
Call center workers are under high time pressure to accomplish tasks, designers must consider this when selecting listing types

According to the Blue Cross non-sales reason code data, only the top 2 or 3 reasons were picked. It was very rare to find another code other than the top 2 or 3. Those top 2 reasons made up about 90% of all reason code responses. Why was there such a large discrepancy between the number of codes and the very few that were picked?

Instead of just taking the data at face value and using it to predict target audience potential sales activity, I decided to go and observe and interview the agents. I wanted to learn the ‘why’ of their selection behavior.

In interviewing the agents, and conducting contextual observations, I learned Agents were extremely pressed for time. Thus they just picked one of the first reason codes in the list, instead of scrolling through the entire list. Furthermore, agents were not rewarded for the quality of the reason code response provided, just for providing a response. So picking any reason code was good enough, from their perspective.

In essence, the agents were cherry-picking the top-most responses because it was the fastest way to complete the task and take another call.

Knowing this, I worked with the marketing team to try to dramatically cut down the list of non-sales codes, by determining which reasons were really needed, versus just a nice to have.

The new list cut down the set of reasons from over 40 to just 6. These 6 reason codes would still provide the marketing department with valuable data, but would easily fit on a drop down menu with no scrolling. Thus, agents could pick from one of the 6 reasons with little to no slow-down. This proved to be a highly effective way to better capture non-sales data that ultimately was used to determine which advertising audiences were more likely to be better prospects.

The screen shot below demonstrates an example of a reason code list that is short enough to fit in a drop down box (this one does not use A-Z listings).

Photo of reason code list

The moral of this story is designers must fully understand the ‘why’ of user behavior when determining which type of listing type to use. For frequency of use listings, guessing the frequency, or not fully exploring user reasons for frequency data, can potentially lead to a bad user experience.

In many cases, starting with an A-Z listing is the best way to proceed to gather frequency of use data prior to determining the order of a frequency of use list.

Number of Items in the List:

Really long lists are going to need organization, either by clustering in like groups, or more often by A-Z listings. Related to this is the very long listing most designers deal with when asking users to pick a State or Country. Especially for Country lists, placing the list in A-Z order is a best practice, but with the typical user’s Country listed first, if known (and again down in its normal place in the list, just in case they miss it).

As can be seen in the example below, Google’s profile editing page includes a country selection drop down menu in A-Z order.

Google Country listing is in A-Z order

However, it is often faster and less error-prone to enable users to key in their address data, especially when choosing for example U.S. States in an address entry order flow.  The point is, designers and information architects must carefully consider how many items will comprise an A-Z listing prior to determining whether to use it for a specific task.

Conclusion: Why Jakob Nielsen was (mostly) wrong

So now that I’ve provided a more complete list of parameters that can help you choose a type of listing, whether A-Z or otherwise, I hope you’ll see why Jakob Nielsen’s Alertbox was (mostly) wrong. Do not assume, as his article implies, that A-Z listings are usually not the best way to present a list to users.

Instead, carefully evaluate your typical users, based on the selection parameters, and make a more informed decision about whether to use A-Z listings, or something else.

Parameters for evaluating a type of listing to use:

  • Domain (overall subject matter) expertise of persona
  • Specific label expertise of persona
  • Listing tool frequency of usage
  • Listing item frequency of usage
  • Number of list items