In my experience, engineers often develop primarily for themselves. Until I began my design research, I never seriously considered how other people would perceive, utilise or interact with my programs. So long as they made sense to me, they were great. It was when I started developing ubiquitous computing systems that the concepts of usability and user-centred and participatory design were introduced to me by my advisor Margot Brereton and Danish user-centred designer Jacob Buur. When I was writing my dissertation on design it truly startled me to realise how sheltered I’d been from human-computer interaction best practices.  According to much of the literature and commercial practice, user-centred design and usability are now thought to be a common and necessary practice, but it still isn’t a core component to engineering education.

My own experience was a fairly typical engineering education which was a computer systems engineering degree at The University of Queensland. During my studies, I interned at Compaq, I continued programming for fun and competition, and I completed an honours thesis designing a mobile payment system.  The fact that someone immersed in software engineering hadn’t heard about user-centred design (or considered the user perspective in his own time) speaks volumes for traditional design education for engineers. This type of education, and the resulting world view, has ramifications for how user-centred and participatory design is approached within commercial design practice. It would seem (from my experience) that the way engineering is taught with a single-mindedly technical (or rather, problem solving) focus. This makes sense of course. Engineering is all about solving hard problems — engineers are given a requirement for solving a problem, and it is pretty darn satisfying finding the best way to solve it. This is seen in sites like StackOverflow and TopCoder. Engineers also tend to hold a strong personal interest in their field and in deriving optimal solutions for it. In adhering to the design specification given, whether it is then in turn usable to others, is not a primary consideration.

When supposing how people might use their software, many software engineers create a solution that works for them or an idealised version of the user.  Otherwise, the responsibility is left to a design team – who again, often idealise how a user might interpret and interact with the design.  Both react to later data on use and usually a few iterations are made.   In the past, the engineer would be the first step, but a greater proportion of systems design now attempts to create the interface as the first step in the design process (which causes its own problems of essentially asking the engineers for a pony – you can ask for whatever you want, but it doesn’t mean you can have it).  Either way, it is not until after several iterative cycles that a user-friendly and technically possible interface takes shape.

I first noticed the problems with these approaches when I started my PhD program. My initial role was of the computer engineer to implement technical solutions for “innovative means of multimodal interaction” (specifically, developing a standalone gesture recognition system that could be worn on a hand in an unobtrusive way), and I would be working closely a multi-disciplinary team of designers, with backgrounds in design, engineering and computer science, all with an interest in exploring different human-centred design approaches.

The project was described as requiring a researcher to

“investigate and design ways of interacting with the information infrastructure that maintain natural social interactions, take advantage of physical space and utilize our extensive human abilities and recognize and manipulate physical objects.”

and my main interest was the use of ubiquitous computing to provide alternative modalities for computer input in an as-yet-unspecified context. I wanted to develop embedded devices that afforded new interaction modalities for computing. I soon found that this required considerations of problems that went beyond recognition algorithms and new hardware. I became involved in methods and ideas completely foreign and new to me, such as ethnography and user participation. At first, I couldn’t see their benefit and felt we were “wasting our time” and should press on at the “true” problem at hand, of building a working system.

In other words, I was doing a pretty great job at fulfilling the stereotype that engineers “don’t play well with others” and tend to exclude non-technical designers. That said, there can also be difficulties in accommodating technical members to a team well-versed with qualitative design practices. On my research team, I tended to retain a deeper technical focus than others I collaborated with. New and unfamiliar concepts that I did not agree with meant there was occasional friction during design activities or when contributing to academic papers, and my technical inclination affected integration within the design team. Ultimately though, it was in understanding and accommodating these different perspectives on design that it became possible for me as a ‘classically’ trained engineer to rethink my contribution and involvement in the design process.

My initiation to both methodological and technical considerations was at one of Jacob Buur’s workshops. Previous to the workshop I thought of design as the implementation of a system to solve a specific problem. My view was that in creating such a system, the problem’s requirements would be defined both abstractly (i.e., as I came to realise, without a holistic consideration of the context of use) and subjectively by engineers, who then set about solving the problem. Buur’s workshop impressed upon me the importance of user engagement and expanding the design requirements based on a detailed consideration of the context of use. During the first workshop, when reviewing videos of design studies, I critiqued the products being presented. Buur critiqued the design process taking place.

(Actually, I should also point out, that although it seems like I’m saying in a fairly self-loathing way “Ugh, engineers never think about the user and that’s a huge mistake,” I am not advocating against pure engineering research. Such research provides technical advancement that plays an invaluable part of design — it is the actual implementation/deployment of new technology that is problematic.)

My initial efforts during my PhD were to appropriate and improve gesture technology (the initial prototype I worked on was designed and built by two friends of mine, Michael Day and Sarah Alexander who did an amazing job cobbling together a portable system using accelerometers for sensors and neural networks for rapid recognition). However my advisor in the meantime was encouraging me to explore different domains for potential new use cases. Even at this point I still had a strong disconnect between the technology and its application. I saw ethnographic studies as something I merely “had to do as part of the research”. I initially did not consider ethnography as part of the design process.  So instead, the first eighteen months of my research were spent learning about neural networks, methods of pattern recognition, and how to interface sensors to learning networks. It became clear to me during this time that the scope of developing a more accurate system would require me to focus on technical breakthroughs and exploring the field of artificial intelligence. However, from my earlier undergraduate studies with handwriting and speech recognition on personal digital assistants, I knew embedded pattern recognition was already a mature field. I had seen firsthand what was possible with existing technology, and observed recognition systems which worked with a high rate of recognition in the laboratory which had not been implemented for a variety of reasons. Knowing this, I changed tack and focussed instead on why these existing systems were not being used and to investigate means of integrating them into a system in a manner that made them both usable and useful. Instead of technical development, I began to focus on what the user required and how their needs could be met with adapting off-the-shelf technology.

In terms of technical maturity, gesture recognition, 8 years after I started my PhD, is still a developing field (with the exception being touch-screen, or two-dimensional gestures, for example the Apple iPhone and iPad), while in comparison, handwriting and speech recognition are both fairly mature in their content and application. While handwriting recognition was not directly useful for the work I was studying, I hoped to incorporate the technical lessons learned for two-dimensional space to three-dimensional space. Speech is almost always used in tandem with gesture by people while communicating, offering a further avenue of recognition for enhancing new systems. Speech recognition can also be deployed to support interaction in an ambient fashion, and is a well studied and technically advanced field of research. Based on its possibilities, I planned to augment the gesture recognition systems I was working on with off-the-shelf speech recognition. The focus of my research switched to creating a ubiquitous computing system to support work practice, while focussing on usability and limiting the time required for technical development.

Through my exposure to and understanding of participatory and user-centred design, I also wanted to develop a system that satisfied the user from a personal and social perspective. The system prototyped needed to be integrated with the practitioner’s work context, while supporting ready appropriation by an individual user (for example, supporting accent, word choice and functional expectations of such a system). I needed to consider localisation of the system, the context it was to be deployed to (to accommodate both the unique challenges of the environment it was used in and the expected interaction paradigm) in addition to the technical challenges faced. I began to realise that while engineers and designers may restrict themselves to a particular field, there was potential for overlap between the two.

Engineering can be seen as the devising and analysis of systems of technical systems to solve problems, while design is the speculative and synthetic process to develop new products and services. Where these meet is in human-centred and participatory approaches which focus on human-experience and acknowledge human agency in human-computer systems. Having to consider such a comprehensive range of constraints affected the design process. I realised it was not merely enough to provide a more technically advanced method of pattern recognition that afforded new interaction paradigms, I also needed to adapt and configure such a system for its context of use. The outcomes of my prototyping and design methodology are discussed in my dissertation. The culmination of these concerns led to my ultimate research question:

How may engineers, designers and practitioners be better involved in and served by a design process for complex information systems that adequately addresses the needs of the practitioner?

(n.b. ‘practitioner’ is participatory design parlance for the end-user)

The answer, in a nutshell was what became a design-manifesto of sorts for me:

“In order to create both usable and useful design it is necessary to respect the tacit knowledge of the user, while using user-focussed design techniques to tailor a system to be its most effective for a particular work context.”

This led to a thesis which ended up exploring the gap between engineering design and human use, and identifying principles for allowing engineers to connect this gap.

I like to think that these principles allow for improving the integration of engineers in a design process which emphasises usability and participant empowerment, and to prove it, I built a prototype system for performing periodontal charting with a group of dentists in Australia and New Zealand.  In the process I discovered a new sense for what was possible with design and the actors within and newfound sense of how important usable design is for people to support what their actions.

I’ll further discuss these methods and how the design proceeded in another essay…

Posterous
Posterous is named as such because they make it preposterously simple to blog.  It’s very easy to use and a great product — I’ve even heard people on the T to work raving about how much they love it.  I think their landing page, with its three steps of use, says it all:

I’d really like to enjoy that simplicity myself — I love the site, the implementation, and the look of posterous blogs, but with my comfy custom WordPress installation, I can’t see myself using it anytime soon.  Bummer.

Balsamiq

Balsamiq is what I spent years wanting to have.  It’s a very simple-to-use but powerful creator for wireframes.  Instead of doing the smart thing and inventing my own version of it, I languished in Visio, PowerPoint and Photoshop.  Balsamiq provides a great toolkit for quickly creating digital sketches of UIs and is a joy to use.  While it’s been very useful for my own personal projects on occasion, unfortunately it doesn’t fit in with my current work flow at TripAdvisor where we’re doing a pretty decent job with Photoshop and paper sketches.  I would’ve loved having a tool like this at Trovix though.  Oh, and a hearty congratulations to the Balsamiq team for what sounds like a very successful 2009.

Amazon Kindle

I got to borrow one of these from Google over Thanksgiving and I loved using it.  It meant I had plenty to read while on vacation (where I get the bulk of my book-length reading done), without the bulk of the books.  I bought Under The Dome by Stephen King recently, and wow, there’s a book that shows the utility of the Kindle (1074 pages).

Unfortunately the clunky update speed and grayscale screen doesn’t do it for me.  The lure of the mythical Apple tablet is proving too strong and I can’t pull the trigger on one just yet.  More than happy to keep borrowing one of Google’s though.

Google Voice

I managed to snag a GrandCentral account a while back, but the inertia of my existing phone number meant it was more of a technical toy than a serious phone replacement.  I do love the idea of a unified phone system, and with realtime voicemail and transcription, call recording, conference calls and a slew of other great features, it seems like an amazing product… but only if you can get around the limitations of having to change your number, and to call the Google Voice service to take advantage of said features.  I think the rejected-by-AT&T iPhone app would’ve gone a long way to helping me switch.

RadRails

RadRails is one of the few products where I’m not sure if it’s me at fault or them for not using it.  As someone who got very comfortable in Eclipse and is a little lazy, I’d like to continue my Rails hacking in a familiar IDE.  Unfortunately I just can’t seem to get RadRails to play nice with the latest releases of Ruby and RoR.  When I get more time I’ll take another crack at it.

In theory though, it’s a great environment for us ex-Eclipse users.  I’m not sure about other users, but I spent a fair bit of time in Eclipse using J2EE/Spring as a framework, and RadRails feels like home.

Edit: updated to add…

Google Website Optimizer

This is an amazing free product that allows for A/B and bucket testing.  Happily we have some very nice pool testing at TripAdvisor already, but perhaps I’ll get to use it on a future side project.

But what are “useful” images?  First let’s consider the state of image search currently.  Firstly, good image search matters a lot to people, as made clear by the positive reception of Bing image search after years of Google image search languishing.  But what are the common use cases for image search?  Trying to show someone something you don’t have a picture of (“Here Steve, this is what a Huntsman spider looks like in Australia”)?  Trying to confirm what something looks like (“Ahh, so that’s what a Darwin stubby is”)?  Looking for something to use for a blog post or an assignment (“Where do I find a picture of a periodontal probe“)?

It’s the last use case that’s always been frustrating.  While writing my thesis I remember emailing dozens of people for permission to use their images.  Then there’s the size issues – most images online are thumbnails, with few print quality images – without using filters these are difficult to surface.  Finally, there’s relevance — finding the best image can be a chore.

So how to solve that?  Sprixi does this a few different ways:

1. Great interface. Much slicker to use than other image search engines.
2. Fair-use image crawling.  Sprixi aggregates all the best sources of images which can be re-used.
3. User contributed content.  Users can upload their own images.
4. Crowd-sourced relevance.  While images have a built-in relevance, Sprixi allows users to score photos so that the most representative photo floats to the top of the list.
5. Web 3.0 – like many new sites, Sprixi is part of the semantic web, by having an understanding of different concepts (also see Adioso and Trovix for other examples).

Sprixi learning a new topic

Rating an image

Using an image - notice the "choose" link on the left

As an example, let’s say I’m doing a presentation on user research, perhaps the ethnographic dental studies I completed for my thesis.  If I need a generic picture of a dentist to illustrate a slide, I simply search for “dentist” on Sprixi.  There’s an existing topic with sorted and unsorted images. I can then browse through the photos, rate them myself, and when I find one I like, I choose the size and click the “use” button (which is some very innovative use of horizontal real-estate for those of us on small screens), giving me the option to download or embed a link.  If Sprixi doesn’t understand the topic you’re search for it will create a pool of photos based on aggregation on the fly.

As a later engaged member of the community, I could also upload a copy of any photos of dentists I took as part of my studies for other people to use.  I could then populate a new concept, such as “participatory design” with some shots of researchers interacting with practitioners.  Now suddenly there is a site which has useful pictures of the participatory design process.  Trying this search on Google, Bing and Flickr revealed reasonable results, but with lots of cruft.  Sprixi wants to get rid of the cruft.  According to Andrew, it’s not explicitly necessary to rate images manually either, as Sprixi does some implicit rating through site interaction.

I was a little disappointed that Sprixi didn’t pick up on more obscure topics (e.g. periodontal probes) with its aggregation, but I’m sure this will continue to improve.   Managing contributions (and the sign-up process in general) seems to be a work in progress.

I do wonder how big the user base for a product like this will be — however there are an awful lot of bloggers, uni students and Powerpoint presentation creators out there.  I am still interested to see how Sprixi plans to monetize and grow beyond the few sites its crawling at the moment.  If it gets traction though it could easily become a fantastic repository for free-use images.

Give Sprixi a try and let Andrew know what you think.

Incidentally, Gelateria Cremona has what is easily the best gelato I’ve ever had.  I can only imagine how popular they would be in San Francisco…

Recently I noticed a forum post where one of the developers behind Plex (a really amazing media centre application for OS X) had to defend removing features.  I still think their reasoning is correct, but due to user backlash they decided to put the feature back into the next version.

This is the main reason for feature creep and too many options.  People have different tastes and use products for different purposes.  The problem is, by kowtowing to existing users, you continue to alienate potential ones that you didn’t even realise you were alienating.

So let’s say you have a feature that tests equally well – 50% of your users love it, and 50% of your users find it confusing and difficult to use.  Do you keep it (to satisfy the 50% of users) or lose it (to preserve simplicity)?  What about you have two different versions of a feature that you have A/B tested and each are equally popular in those tests?  It can be tempting to even provide both!

However to preserve a simple design, it’s at this point you need to make a subjective choice.  You need to evaluate:

• What will be better for the product’s image?
• What will provide extensibility for future plans?
• What provides a qualitatively better experience?

For instance — you may be trying to choose between a dropdown list and a radio button.  Which to choose?   A dropdown list takes up less real estate — is that important? Do users find it easier to make a choice if they can see everything all at once, as with a radio button?   Is the list of options going to grow in the future?

As a designer or usability expert, it’s easy to get caught up in always finding the “best” user experience or what the user “wants” most, but it’s important to remember that some aspects to design cannot be measured or quantitatively known.  A simple design will go a long way to giving your design universal appeal and application potential.

Deciding what should and shouldn’t go in is actually a very difficult choice.  You don’t want it to be overcomplicated, but you want to have a competitive edge.  Sure, it seems easy — just throw out whatever people don’t need and put everything else in.  Unfortunately every user is different.  I read an article which claimed that people only used 20% of Microsoft Office features, and so 80% should be removed.  Unfortunately, each person uses a different 20% of the features.

So how do you decide what goes in without overloading your product?  The easiest features to be sure about are the ‘standard’ ones.  What are the must-have aspects of your product to make it work?  What does everyone love about your competitors?  Put these in!

The rest?  While usability testing will give you some idea of what people would want or like, a user saying they’ll use something in a usability session does not mean they will actually use it.  To decide what might be used, you will need to use some of your best judgement, some user feedback, but most of all, pick features which are ambiguous.  People will always do surprising things with your product.  How people customize and appropriate a system for their own use is called “articulation work” in design academia, and the more ambiguous you make your design, the more people can appropriate it in innovative and surprising ways.

I think Twitter is a great example of articulation work.  Ostensibly it’s just a status update system.  However, people use it for all sorts of things — microblogging, link sharing, ad-hoc meetings, connecting with corporations, getting the news, etc.  What facilitated this was a simple system with a few key features – such as the “@” and “#” operators, and a real time search.  From these basics, the community began using it in new and unexpected ways.

So when you’re trying to keep things simple and to decide “should I put this feature in?”, wonder “how might this be used in other ways?”  It’s much better to put in one feature which can be used in a multitude of ways, rather than overload on catering to everybody.

However, the question remains — how do you decide which treatment for a particular feature makes it in?  For example, say you allow a user to select something via a drop down or with radio buttons, and both test equally well.  How do you decide which to use without providing alternative methods of interaction?  I’ll cover that in part 2.

Crowdsourcing information is usually a pretty good way of doing things.  There’s been plenty of research which has shown that if you get a crowd of people together and have them, for instance, guess the total number of jelly beans in a jar, the average guess will be pretty close to the real number.  Translating this to something usable in everyday life, I’ve seen people have a lot of success with Amazon’s Mechanical Turk for aiding research, since you can use the sheer weight of numbers to smooth the data.

This is great for something tangible and objective.  However, as soon as you start throwing subjective data into the mix, results get a little skewed.

In particular I’ve noticed lately I can’t quite trust online reviews (from a wide variety of users) the way I used to.  I’m not sure why things have gotten so skewed — perhaps I’m more discerning now, or maybe there are more outliers.  Either way, I haven’t trusted the average rating on sites like Amazon, Yelp and TripAdvisor for a couple of years now.  Instead I read a sample of reviews and then go straight to the upper and lower bounds and try to get a sense of why people are voting in a particular fashion.  Is the product/place being reviewed being unfairly penalized or rewarded? (for example, a hotel might get a lot of 1 star reviews for high parking fees, but are great otherwise.  A restaurant might get a lot of 5 star reviews because they’re cheap and have a nice ambiance, even though the food stinks.)

One method I’ve found for making a better decision is to look at the shape of the score curve.  For example, here are the scores for the top 5 TripAdvisor hotels in San Francisco at the moment, order by TripAdvisor by their average score:

1

2

3

4

5

There aren’t any that really stick out there as being obviously different, but you can see that the fourth one gets a far higher ratio of 5 star ratings to 4 star ratings than the others. These differences become more pronounced the further down the overall list of hotels you go. Hotel A is rated as a better hotel than Hotel B which is ranked (according to its average) after it:

Hotel A:

Hotel B:

Whenever I see a curve difference like this though, I always go for the latter when booking.  Since I changed tactics, I have been having great hotel experiences.  To give this a quantifiable score to compare, I tried out the following formula:

If ((x star votes) – (x-1 star votes)) > 0 then

y = x

else

y = 5 – (x – 1)

(y*(5votes – 4votes) + y*(4votes – 3votes) + y*(3votes – 2votes) + y*(2votes – 1votes) + 1votes) / total votes

Using a formula such as this the new overall scores become:

(260 + 96 – 9 + 8 + 1) / 128 = 2.78125
(600 + 108 + 6 + 8 + 2) / 206 = 3.51456
(1550 + 548 + 54 + 18 + 5) / 699 = 3.11159
(425 + 28 + 15 + 0 + 1) / 119 = 3.94117
(1260 + 948 + 60 + 16 + 13) / 883 = 2.60136

Which as you can see results in a much better looking ranking for the curves:

4

2

3

1

5

If we apply the formula to the hotels A and B, we see the difference becomes more pronounced:

(-26 + 244 + 15 + 22 + 6) / 185 = 1.41081

(250 + 236 – 9 + 46 + 10) / 301 = 1.77076

Introducing a new overall score would help people pick better hotels and for the hotel owners to strive for higher ratings.  I’m also a big fan of the trending data that Yelp has added recently, using Patxi’s as an example:

Coming from someone who wrote his thesis on qualitative user feedback, this has been really interesting for me to look at how you can properly interpret large amounts of quantitative data involving subjective scores.

Update November 7 2008: Thanks to Eric Liu for pointing out some weighting issues depending on vote numbers. We’re brainstorming some new algorithms to account for these situations.

In the meantime, anyone from the myriad of Netflix people who have stopped by, feel free to contact me! tim@<this domain>.

Personally I always assume a site is free and am surprised/annoyed when it isn’t, unless it offers something very compelling or a freemium model (the best, in my opinion, is Flickr).  However a lot of sites put “free” in large type (including Trovix!) on their landing page.  The only rationales I can think of (beyond the rare case that most other examples of your product are paid) split into two camps:

1) If you require an account to use the site, some people might think they will be asked for credit card details during the sign up.  But how prevalent is this concern?  It’s never come up in any focus groups or user testing.  When directly asked, the people in my study groups were unanimous in the assumption it would be free.

2) It’s used as an attention grabbing bit of text that is intended to make the user feel like they are getting something (which should be a paid service) for free.  This just feels tacky, and depending on how it is done, can detract from the brand.

I was struck by Mint’s use of this tonight, which prompted this post:

Perhaps it is a cultural thing — I have seen many products that label themselves as ‘free’ in a scammy way.  Regardless, unless everyone else is charging (or even if they are, like when Yahoo and Google introduced real time stock prices), it seems an unnecessary feature to draw attention to.

I started with the old vanity surfing evaluation.  I initially searched for plain old “cederman” (I’m number 2 on Google behind darn Lars-Erik at the moment!).

Very confusing list of results without any sort of rhyme or reason to them.  None of the vaunted contextual search options delineating between the few Cedermans on the web (there are only three or four of us, with three of us being published authors).  There are also some incredibly irrelevant results there, and why is an extremely old copy of my twitter page listed there, but not cederman.com?

“cederman-haysom”, “tim cederman” and “tim cederman-haysom” didn’t fare much better either.  Ouch, it’s not hard when there are literally only three cederman-haysoms in the world!

Anyway, whatever.  The true test of a search engine is looking for stuff that you actually want.  While TechCrunch did several broad searches (such as for “dog”, “apple”, and “france”), these are fairly rare in real world searches.  At the moment I’m planning a trip to Belgium, so I tried one of the cities I’m looking at accommodation for and trying to plan a tour of.

No results!  Search for the alternative spelling “Gent” only showed pages in Dutch.  The most curious aspect to this is that contextual search worked here.  The options shown up the top are valid Ghent related things you’d search for, and it’s actually really nice having them identified and clickable in this manner.  So why have “no results” for the main page?  Doesn’t make any sense at all…

Finally, what’s with the ordering of results?  At first I thought I had to read sideways, in rows essentially, but the results don’t line up.  If I have to read by column, then I need to scroll to the bottom, and then all the way back to the top.  Without a sense of relevance, it’s very disconcerting, particularly if you’re searching for something you don’t know much about.  Sure it might help provide some users with a shotgun spray of results, but I think there’s a good reason why Google’s layout still works best.

As an example, imagine someone looking to buy a new Roomba:

While Arrington also states “I want to reemphasize that Cuil is only an hour old at this point, Google has had a decade to perfect their search engine.“  This is disingenuous to say the least.  Cuil is certainly not an hour old, and Google was VERY impressive when it first launched.

Still, I’ll have a close eye on what they do next.

Witness the Roomba.  I love my Roomba – I bought it in October 2006 when I moved in to my cottage and realised I needed a vacuum cleaner.  I bought the bottom of the line model, no accessories, and as of August 2008, with some minor maintenance it still runs great.  Even its battery, while definitely not as great as it once was, still has enough charge to clean the whole room.  It’s easy to use, cleaning it is very simple, and maintenance is also very user friendly.  And of course, it does a great job of cleaning the whole cottage (although I am lucky to have Roomba friendly floors and furniture).

With so much love for the Roomba, it was a natural choice to buy a Scooba as well.  I don’t mop and I could see grime building up.  Scooba was only $99 on Woot, so I bought one straight away.  I noticed immediately that it’s a lot bigger and heavier than Roomba, making it more difficult to move around and to manipulate while cleaning.  It doesn’t fit as nicely in our cupboard, and its increased height means the light sensor keeps getting caught under cupboard doors.  It leaks water when being moved from room to room, and will often insist to check the tank for no discernable reason.  There are a lot more parts to clean, and maintenance is a lot more complex.  Emptying it becomes a gross chore (instead of tapping a box into the bin, I usually manage to cop a bit of spray back when pouring out the dirty water).  When I lift it up, the tank often separates from the body.  I am struggling to get it to clean a single room at the moment with its myriad of problems, and it’s only 6 months old.  iRobot won’t support it because it’s refurbished.

Long story short, I’d never recommend the Scooba to anyone.  But I’d recommend Roomba in a heartbeat.

So how did they go so wrong?  There were several key areas:

1. They identified an alternative niche and went for it at all costs.  I suspect their other products like the Looj will do better.  It seems like at no point someone wondered “will having <problem x> in addition to all the other problems mean people will just give up?”
2. It’s overcomplicated to the point it can no longer complete its original purpose.  Mopping by definition is more complex than vacuuming because it is a 2 phase process.  However I can’t help but wonder if it would be more successful if they sacrificed some of the cleaning capabilities for simplicity.
3. Too many choke points in the design.  The beauty of Roomba is it keeps working if some parts of it aren’t.  Scooba will fail if the tank connection gets clogged, if the hand-mixed formula is not done right, or if it detects a problem with its pipes.  One of these three things happens to me every time I try to deploy it.
4. They forgot the Roomba design ideals.  It’s clunky, hard to use, makes a mess and has inherent design faults (such as a battery that fails after just a few months).  It was like they started from scratch without taking on-board any of the Roomba lessons.

I love domestic robots, but unless you’re really desperate, do not buy a Scooba.