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The 7 Rules of Mobile Data User Experience


A Formula for Delivering Successful Mobile Data Applications




Elliott Drucker


Drucker Associates




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A great deal of time and money is being invested these days in development of mobile data applications, and even more is being spent on deployment of the high speed wireless data networks on which they will operate.  At the same time, there is a lot of talk in the industry and financial press about “mobile data user experience” (MUE), a term intended to encompass the general satisfaction – or perhaps lack of frustration – that a typical user will experience in the operation of a given data application or feature.


Until the much more recent introduction of data services, the idea of mobile user experience for wireless voice telephony has largely been predicated on quality of service and completeness of coverage, two factors which might not be particularly simple to measure but at least are easy to define.  For a variety of reasons the user experience situation is very different – and far more complex – in the delivery of wireless data applications and features.  Yet MUE will be critical to the ultimate success of commercial wireless data services for both individual applications and data transport networks.  It therefore seems useful to identify characteristics for mobile data applications that will predictably result in a “good” MUE or at the very least avoid a bad one. Most critically, these characteristics need to take into account the limitations of data bandwidth and user interface that are inherent in the mobile data environment.  I’m not suggesting a quantitative scoring system by which applications could be ranked against one another on some “MUE index” but rather a set of subjective benchmarks that can serve as general guidelines for developers of mobile data products.  To that end I have developed what I call “The Seven Rules of Mobile Data User Experience”.  Here they are.



Rule 1: Consistent User Interface


There is currently no shortage of mobile data applications available to consumers, and more are being added every day.  Users are most likely to embrace those applications which collectively meet there needs and interests.  This suggests that one means to success in the development of a new data application is to identify and exploit some as-yet unmet need.  That’s true, but hardly useful.  It’s kind of like saying that one means to wealth is going about picking up hundred dollar bills that people have dropped.  Novelty of content is pretty rare; a more reliable route to success is making the content as easy as possible for the user to access.  And since most users will rely on a number of different data applications, ease of use is largely dependent upon commonality of user interface. That means that navigation, menu access and formats, image layout, data entry, command selection and execution, and data display should all work pretty much the same from one data application to the next.


Consistency of user interface is a critical component of user comfort and familiarity, which in turn strongly impact the user learning curve and user acceptance.  Of course, making the user interface natural and intuitive is also very helpful, but I believe that consistency is even more important.



Rule 2: Simplified Data Entry


Many in the industry would like to pretend that a wireless handset with Web access can deliver the same data services, in the same way, as a personal computer connected to the Internet.  This is patent nonsense.  Even top-of-the-line smartphones like Apple’s iPhone and Motorola’s Droid have far more rudimentary user interfaces than those of even the most basic computers.  With simpler handset devices data entry can be agonizing.  As I write this I am sitting at my desk plunking away on a full-sized QWERTY keyboard and watching words I type appear on a 21 inch flat panel display.  Could I do the same chore using the keyboard and display of my basic cellphone?  I might start, but long before I finished the first page I would probably get so frustrated that I would do something to render the cellphone permanently out of service.


It is one thing for the wireless industry to tell consumers that they can access Internet data services from their cellphones just like they do from their PCs, but it is quite another to design mobile data applications as if this were actually true.  In fact, mobile data applications should be designed to limit and simplify user data entry as much as possible.  This is important even for smartphone users, and absolutely critical for users of more common basic handsets.


Optimally, a wireless data application should accommodate single-hand operation and should require a minimum of manual “typing” for data entry.  There are a number of means by which this can be accomplished, including pick lists, scroll lists, and radio button sequencing.  And of course the application should be able to “remember” commonly needed data such as the user’s name and address.



Rule 3: Efficient Network Utilization


Another myth being perpetrated in the wireless industry is that 3G data networks have abundant bandwidth for all users.  In fact, a radio channel which can indeed provide impressive total data throughput is a resource which is shared by all users using that channel at a given time.  The more users, and the more throughput each user demands, the lower the data rate available to each user.  This trend can be offset to a point by deploying additional 3G data channels in each base station and/or by deploying more 3G base stations, but such “solutions” are costly.  Furthermore, actual user data rates will be negatively impacted by degraded radio channel quality.  Emerging 4G networks will provide greater bandwidths and correspondingly higher peak throughput rates, but the increased capacity will likely be more than offset by growth in user demand.


Because of these and other factors, at any given time and place a wireless data network may reliably provide to a given user only a fraction of the throughput that it can deliver under pristine conditions.  Of course, providing a good overall MUE means doing so reliably, which suggests that a mobile data application should use the data transport network efficiently.  Perceived delays in data transfer, which will very quickly frustrate a user, can be minimized by reducing to the extent possible the amount and frequency of data that needs to be transmitted to and from the mobile device.



Rule 4: Off-line Functionality


One thing about mobile data users: they tend to move around.  And sometimes they find themselves in locations (airplanes, subway systems, etc.) that even the most ubiquitous wireless data networks cannot cover.  However, lack of wireless data access does not mean that a data application cannot provide useful functionality.  In some cases a user may be able to anticipate the loss of coverage and pre-load data for later use by an application program resident in his or her mobile device.  Alternatively, a user temporarily outside of network coverage could set up the application, including required data entry, so that required information transfer can be executed immediately upon reacquiring service.  In appropriate applications an optimal MUE would require such “off-line” functionality, and in addition would require that its operation, particularly in transitioning in and out of coverage, be as intuitive as possible.


There is one additional requirement for off-line functionality in order to make it usable aboard commercial airliners.  In order to comply with FAA regulations, the user must be able to disable the transceiver of the host mobile device while using the resident data application program.



Rule 5: Automated Data Sharing


Rule 1 suggests that wireless data applications should have a consistent “look and feel”.  Now Rule 5 suggests that they should also be eager to share data among themselves.  As discussed in Rule 2, data entry on typical mobile devices can be a disagreeable chore and should be minimized as much as possible.  One way to do this is to automatically transfer data from one application to another.  This is pretty obvious for “boilerplate” information such as user name and address, but there are other instances where automated data sharing can be usefully employed.  Example:  an application that provides diving directions from point A to point B could port data to an application that shows locations of gas stations along specified routes.



Rule 6: Dynamic Personalization


With the possible exception of identical twins, no two people are alike.  Differences in tastes, needs, and technical sophistication extend to selection and use of wireless data applications, of course, and successful applications will allow for appropriate levels of user customization.  But in order to deliver an optimal MUE mobile data applications need to take personalization a step or two further.  At the very least, the application should automatically store such things as personal data, favorite websites, locations of interest for weather reports, and stock ticker symbols on the basis of what the user has done with the application in the past.  Ideally, user interests and preferences could be predicted by extrapolation from such stored information so that the information or option most likely desired by the user could be the first one offered.


Such “dynamic personalization” of a mobile data application can be very seductive when it seems to “magically” anticipate what the user wants to do.  However, that seduction will quickly turn to frustration if anticipated preferences cannot be not easily and intuitively overridden by the user when the program guesses wrong.



Rule 7: Mobile Device Independence


Unlike the realm of personal computers, the world of wireless data user devices encompasses a myriad of different capabilities, user interfaces, and operating systems.  To gain the broadest possible market opportunity, a mobile data application must therefore operate over a wide range of environments.  That alone is a difficult enough task for a developer, but unfortunately it’s not sufficient to assure a good MUE.  Because users change their mobile devices with some regularity, a successful application will also need to operate in those different devices in much the same manner.


Obviously, it will usually be impractical if not impossible to design a given application so that it works identically in mobile devices with vastly different user interfaces.  I suspect that most users understand and accept this reality.  But a designer can seek to minimize operational differences, and even more importantly can try to make such differences as intuitive as possible.


One final note, as long as we are talking about how users frequently change their mobile devices.  It should be obvious that an optimal MUE requires the ability to easily transfer personalized data applications from one device to another.



Well, there you have it, my “7 Rules of Mobile Data User Experience”.  Completely arbitrary and subjective, of course, but I believe that they collectively define the important characteristics of successful mobile data applications.


Of course, it’s easy for me to come up with these rules.   The real challenge is to develop mobile data application that obey them.  Fortunately, some very robust mobile application platforms are now available that should go a long way toward helping developers in their quest for a better MUE.



For more information on getting help in understanding the realities of the mobile data services environment and how they impact MUE, contact Drucker Associates.