Registration is a crucial initial step that most online businesses have to impose on people along their journeys. Registration is necessary to check people’s authenticity and start meaningful conversations with them based on the provided details. Capturing people’s details correctly is paramount since storing, for example, an incorrect email address opens the door for trouble down the line. With an incorrect email in the database, not only does the business lose the opportunity to reach out to its customers, but the business’s bottom line may suffer. For example, I have heard about cancelled orders due to mistyped email addresses.

It is no wonder then that registration forms try to make sure details are captured correctly. But how to do it while still preserving a positive user experience? Registration forms basically represent a barrier for people to be overcome before they can do what they actually want to do – finally use the website!

Here is how others have tried to handle this (with varying success):

Confirming entry

I frequently see a registration form that has duplicated Email or Password fields. Now, this is a little bit annoying, especially if both Email and Password need to be confirmed (as below).

In the above example, the person’s interaction flow is significantly interrupted by having to answer two identical questions. As per Don Norman’s model of 7 Stages of Action, answering each single question on a form is a small diverting action on the person’s journey towards accomplishing her goal.

Disabling copy & paste

Harry Brignull wrote about a registration form that does not allow pasting into the ‘Confirm email’ field. Quite creative, but I agree with Harry that it could feel patronising, especially for the more tech-savvy people (who know how to copy-paste). On the other hand, it prevents people (hopefully) from simply replicating a typo made in the first field. And typos are arguably one of the commonest kinds of incorrectly entered details. Now let me ask, why do most websites actually use the wording ‘Confirm your email’? Let’s use ‘Re-type your email’ instead, and it might not be necessary to awkwardly disable standard system interactions like pasting.

Repeating key details before submit

A more elegant solution is not to display the second confirmation field at all. But how can businesses eliminate the eventual errors on forms then? I quite like concept prototypes created by Jonathan Knoll and Russ Unger, that repeat the entered email just before submitting. Jonathan and Russ have produced multiple variants, but variant 5 (picture below) is my personal favourite. It puts the entered email within the person’s locus of attention which is at that point in time on the Submit button.

Unmasking passwords

What about passwords, that are by default masked on most forms (even at registration)? First of all, I believe masking a password does not bring any value in most usage scenarios. Nielsen calls for the death of masked passwords, and I am happy to agree with him. However, as opposed to offering a checkbox to mask the password, as he is suggesting, I think the way to go is actually offering a checkbox to unmask the password. After all, in most contexts security is more important than interaction efficiency.  MailChimp is doing this already, and based on a recent live demo of FontDeck, it seems like we will be seeing this pattern more often.

A pattern for unmasking passwords is also frequently used on mobile devices. This is due to the lack of tactile feedback provided by touchscreen keyboards when inputting a password. Moreover, people also cannot rely on their motor memory (remembering the finger movements like in touch-typing, as opposed to the actual password characters). People often utilize the motor memory to enter passwords with little conscious effort, and this does not translate so easily to touchscreen keyboards as visual identification of keys is needed.

Most mobile interfaces support people by revealing the last character entered for a short time and then masking it, thus giving people the necessary feedback. I am not aware of any website doing the same, but it might be a solution for standard monitor-keyboard setup too. On the other hand, the utility of this short-time revealing is debatable since most people type so fast that revealing the last character and masking it with a time delay is very difficult to implement seamlessly.  Try it for yourself - here is an example of automasking.

Inline validation

Another powerful weapon against incorrect entries is inline validation. Validation can only catch a small proportion of specific errors, but it is generally a good approach since people are notified something is not quite right before they hit the Submit button. Therefore it eliminates the need for the dreadful error messages. “Fatal error - you have not filled in all the details!”. “Oh my god, fatal error - someone actually died!” screams the user in horror.

There are multiple ways of implementing inline validation. Luckily for us, Luke Wroblewski put a few validation variants to the test. Based on his study, validation ‘after’ (after the person indicated that she was done answering a question by moving on to the next one) is the winning option - both in terms of efficiency and satisfaction.

So what?

Incorrectly entered details in online forms are a frequent problem that can cause a lot of hassle down the line. However, when designing forms, make sure you use a sensitive approach to minimising those errors and do not make the people do all the hard work for you.

I would love to hear about your tips for minimising errors in forms.

More and more websites are using collaborative filtering recommenders to personalise their goods and services for you.  For instance, Amazon’s “Customers Who Bought This Item Also Bought,” uses collaborative filtering technology to let you know about other products that might be of interest to you.

Figure 1.  An example of collaborative filtering recommendation demonstrated on Amazon.com

Simply put, collaborative filtering recommenders allow a website to recommend stuff based on how similar your browsing behaviour is to that of other users.  These recommenders will often rely on some correlation threshold value to determine whether you do or do not share mutual interests with various other users.  A really good example of music recommendations based on collaborative filtering would be last.fm (www.last.fm)

One issue with collaborative recommenders is a result of a user's divergent goals - different goals that a user might have when using a particular interface.  For instance, say that a given user is a keen fan classical music (a Mozart aficionado), but regularly listens to Lady Gaga (and other contemporary pop music) when with certain friends because that user knows that Lady Gaga-type music will facilitate a belongingness with those friends.  When that user is alone, s/he wants to listen to classical music and be recommended only this music without having to sort through recommendations based on when Lady Gaga has been selected.

A solution that has been devised to help users with recommender noise that results from divergent goals has been to include product information (e.g., classical versus contemporary pop music) when the recommender filters your recommendations.  That way, our example user only gets recommendations for classical music when listening to Mozart.  Recommenders that include content information with collaborative filtering are called hybrid recommenders.

Another issue for any recommender system is what to do when a new user or new item comes along, commonly known as cold start. For the last four years, I’ve been looking at the relation between people’s music preferences and their personalities, which could be used as an alternative way to help resolve the cold start problem and improve music recommenders.  An associate of mine at Cambridge University, Dr. Jason Rentfrow, does a great job in describing the music preferences and personality research (see http://www.youtube.com/watch?v=29-xYiOOc8w).

Researchers like Dr. Rentfrow have identified relations between the genre of music that people listen to (e.g., rap or jazz) and personality characteristics that those people generally have (e.g., extroversion or openness to experience).  Still, genres can be really vague.  I mean, you and I might both love rock music, but are you going to necessarily love the same rock music as I do?  Instead, to help recommenders to their job, I identified audio features prominent in certain music genres and link these to personality characteristics.  So, instead of saying that extroverts like rap music, I say that extroverts really like music that has a lot of beats that happen quickly together… constantly.  This relation might apply mostly to rap music, but might also apply to certain rock songs, electronica songs, you name it.  Conversely, my research suggests that introverts like music that has few beats in the music, which is typical of classical music, but again, is not exclusive to classical.  As a result, identifying the relation between personality and music preference toward specific audio features can help improve both issues described above by identifying and sorting music according to more precise and objective audio features.

In sum, the work that I have described is still very new and there are a lot of challenges to see through before recommenders can truly become 'personalised' by learning and understanding users’ personality, but there are a lot of opportunities that may result from this type of personalisation as well.

So tell me, do you think your musical tastes describe your personality?  Have you made personality judgements about a person because of the music they listen to?

Also, do you think that there are similar aspects common in most or all of the music you listen to?  Or, do you tend to have a favourite instrument that you like when it’s played in a song?

Finally, what do you think about music recommendation based on personality?

Your comments (positive or negative) are welcome.

What does it mean to be an expert on User Centred Design (UCD)? What does it require to be a User Experience (UX) expert? What kind of educational or experiential background do you require? What differentiates an expert from just a consultant? Is an expert someone that knows the UCD process and is proficient at a large variety of UCD methodologies? Is expertise measured by the consultant’s academic credentials, industrial experience, number of clients, or knowledge of a variety of industries and platforms?

What makes the foundation of an expert is all of those things; education, experience, and a solid knowledge of the processes, methodologies and tools. But what differentiates the consultant from the expert is not just being an expert at implementing UCD in perfect conditions, but the ability to implement UCD in the ‘not so perfect’ context of the client.

As experts we need to be able to assess the client’s current processes, phase of development, schedule, and budget, and then determine what activities and deliverables will provide the best returns within their context. But this isn’t where it should end. As a UX expert, you should be able to deliver not only tactical recommendations, but strategic ones as well. Those strategic recommendations are not just on the user experience of their system or product, but on the activities that can help to move the client towards a more efficient and productive implementation of UCD.

As true user experience experts, we need to propose and implement activities and deliverables for the best return in the context of the client, and work with the client to migrate to a process that will bring to fruition more of the strategic value of UCD.