User research

Why I don’t use my Kindle much

I realized recently that I had not used my Amazon Kindle (Kindle 3, or now Kindle Keyboard) in more than a month, despite the fact that I read books every day. In fact, I found it buried under some physical books, battery dead, and then charged it, forgot it again for another month, and discovered it dead again.

As an HCI advocate, avid reader, and someone who initially enjoyed the Kindle a lot, I want to examine some of the reasons I drifted away from it. I think there are five key reasons, which I’ll discuss in turn.

  • Multiple books & tactile
  • Notes
  • Random access
  • Layout
  • Content pricing

Multiple books & tactile. I usually read multiple books at a time, and the Kindle doesn’t fit that very well psychologically. True, you can easily navigate from the current book back to the home menu, select a different book, and pick up right where you were reading earlier. But the psychological problem is this: the book will look exactly the same as the one you were just reading. There is no tactile, olfactory, kinesthetic, or any other sense that you have changed your reading material and returned to another place. This reduces the psychological effect of having switched gears, which is a bit of excitement every time I set down one book and pick up another. You also lose the sense of place within a book (the differential weight of pages on each hand, the sense of location within the text, etc.) Thus, the Kindle pulls strongly for reading one book at a time, which I don’t do.

Notes. I often take notes as I read, and the Kindle is not good for that. True, you can select the menu, insert a note, and type it on the miniscule keyboard “ but going back to those notes later is tedious at best, and in fact only once have I reviewed notes that Iv’e taken. In a physical book, there is a sense that marginal scribbling is a creative act in some way. Indeed, I’ve seen scholarly libraries where margin notes were the primary value. On the Kindle, it feels like notes are an afterthought that exist to check off a feature list, not something that actually works well.

Random access. Closely related to the notes issue, there is no good way to skip around in a Kindle book. The physical nature of printed books is extremely helpful for both memory and search. One may recall, for instance, I saw that chart somewhere around the middle and it was at the top of a page, and a quick flip through the pages will reveal it. Likewise, reading often needs a quick flip-back-and-return to locate some fact (now who is Charlotte again?) before continuing. The Kindle is terrible for that. Partially offsetting this is that one can do a text search; but I suspect the need to find a particular word or phrase is less than the need to do quick page-throughs and reviews.

Layout. The Kindle layout is great for fiction and general non-fiction, but not very good for technical materials such as books about statistics and programming. At any given time, I’m usually working my way through a couple of technical books, which I prefer in print. (Besides the layout problem, technical books also pose issues for note-taking and random access, as described above.)

Content pricing. Finally, Kindle has not yet nailed its pricing model. Amazon seems to be apologetic about this, with notes on some titles proclaiming that the price was inflated by the publisher. For my part, I often see things like this: printed technical book is $40 while Kindle is $35. Ouch. In that case, I get print because it adds a lot more value. What I’d really like, however, is a bundle option: Kindle = $35, print = $40, print + Kindle = $50. I need the print edition for all the reasons noted above, but would like to add a Kindle edition for convenience.  But that’s not worth an additional 80-100% of the print edition price. I’m guessing that publishers insist on this, and don’t want a bundle model because someone could buy the bundle, sell the print copy, and keep the Kindle copy at a discount. But come on! My guess is that such behavior (a) would be rare; (b) would be more than offset immediately by market gains; and (c) would increase overall readership and loyalty which has downstream bonuses.

Positives. Of course there are many positive aspects of the Kindle. In particular, the form factor is nicely engineered for a compromise between size and comfort; text is crisp and easy to read; the price is amazing; the availability of content is enormous; and the portability of one’s library is delightful. I won’t rehash those in detail, but they are the reason I bought one to begin with, and why I enjoyed it so much for the first couple of months.

Conclusion and recommendation. If you read a lot of fiction or general non-fiction (history, etc.) that proceeds linearly through a text, then it’s a no-brainer: Kindle is great. If, however, you read mostly highly formatted or technical works (statistics, science texts, cookbooks, art, etc.) then Kindle is mediocre to poor. And if you want PDF support, then it’s downright terrible (except perhaps for the DX, which I haven’t tried).

ART Forum 2012

One of my favorite conferences is the Advanced Research Techniques Forum (ART Forum) from the American Marketing Association (disclosure: I’m chairing the conference for 2012). At this year’s conference I was happy to announce that the 2012 conference will be:

June 24-27, 2012 in Seattle!

If you’re a researcher interested in the latest customer/marketing research innovations, please consider submitting your work. The CFP will be out soon, with abstracts due in late Fall. For ideas, check out the 2011 ART Forum program.

We also welcome suggestions for the conference: topics to include, tutorials you would like, or suggestions for speakers or future locations. You can find my contact info on the “about me” page, or on LinkedIn. We hope to see you in June in Seattle!

Tips on Learning R

Colleagues often ask me: “How can I learn R?”  Recently, I helped teach an “Introduction to R” class for the Advanced Research Techniques Forum (http://www.marketingpower.com/Calendar/Pages/2011ARTForum.aspx). So that’s one answer.  Here’s another:

Find an R-suitable project and force yourself to use it!  R is really a programming language, not a “statistics package” … and like any programming language, you can only learn it by using it to accomplish something.

What makes a project R-suitable?  I divide that into three groups:

1. Projects that need cutting-edge or custom statistical methods. R quite simply is the tool where new methods are developed first. If you need to try the latest in Bayesian, machine learning, classification, genomics, or similar areas: do it in R.

2. Processes that benefit from R’s language and object structure. This is why I started with the S language back in 1997: I needed to run hundreds of models and extract key information from them. If you need to bootstrap a process, or compare or iterate models, R is the place.

3. Something that you know quite well.  This is where R offers little attraction, but where you can leverage your knowledge. A frequency analysis you do every day; a regression model you run every month; a chart that you can make in 5 seconds in Excel – those are great places to replicate the work in R just to force yourself up the learning curve.

Note that groups #1 and #2 are the easiest and luckiest places to be: if nothing else does what you want (except complete custom code), then R is an obvious answer.  Group #3, choosing a problem you could solve elsewhere, is the most frustrating and requires enormous discipline. You’ll be questioning R every step of the way (“why can’t I just point and click?!”) … until something clicks and you discover the answer for yourself.  OTOH, #3 is the easiest place to start from the perspective of finding specific help for your task; if it can be done easily somewhere else, then a recipe has likely been developed for R.

There are scores if not hundreds of R books that can help you. If you use R for long, eventually you’ll own a shelf of them. Meanwhile, a great first book to learn and get stuff done is Paul Teetor’s R Cookbook (http://www.amazon.com/Cookbook-OReilly-Cookbooks-Paul-Teetor/dp/0596809158). 

But again, and most importantly: Pick a problem, use R to solve it, and stay with it until you’re done. Then repeat. R undoubtedly will frustrate you. It may take hours or even days for something that seems like it should be simple. Remember that you’re learning a new language, so progress should be slow. Yet every time you go through the process (choose, use, stick with it) you’ll know more and will work faster and better. Good luck!

Rcbc utilities for discrete choice models in R

I just posted my “Rcbc” code (Rcbc R scripts), which demonstrates some core functionality for choice-based conjoint analysis (CBC) in R. This code is in development, and represents a package-in-progress. [For those new to CBC, it allows one to determine user preference and tradeoffs among products or product features using a variety of logistic regression.]

The Rcbc code is primarily useful for didactic purposes to show how conjoint models work and to show a relatively easy-to-understand gradient descent method for aggregate multinomial logit model estimation (MNL). It may help supplement commercial CBC software (e.g., Sawtooth Software) for some analytic tasks such as MNL estimation on subsamples, or determining attribute importance, or for getting data and design matrices into a simple format. Note that more complete R functionality for conjoint models is provided in the “bayesm”, “clogit”, and “mlogit” packages.

One novel and useful feature in my Rcbc package is a new attribute importance estimation function (cf. ART Forum poster on attribute importance).

I have not yet written a code vignette, but the code is reasonably well-commented and there are various executable walkthroughs presented inside “if false” blocks in the code. Note that there may be both large and small bugs!

To use: (1) save the file as a “.R” file. (2) source it in its entirety (warning: functions will go into global namespace). (3) read the code and try the examples.

Rcbc R scripts

Now fixing the Lenovo F1/Esc layout

Using CapsLk to mimic CTRL is going well. The frequency of my inadventent “FN-C, FN-V, FN-X” presses is down about 90%. (See previous post).

But a new annoyance has surfaced: I keep pressing F1 instead of Escape (which on Lenovo is by itself above the F1 key). It pulls up Help, which is esp. annoying in Office apps because it launches a new window.

Solution: I use Help very, very rarely, so I map F1 to be Escape. (And then I map F12 to be F1, since I have no idea at all what F12 does, and I occasionally do need F1.)

The program SharpKeysmakes this very easy: run it, add a new key map, write the change to the registry, and reboot. Only works for Windows (and works fine in Windows 7, which I’m using). It can also handle the CapsLk issue instead of the .REG file I posted earlier.

So now my Lenovo keyboard is mapped like this:
CapsLk –> Left CTRL
F1 –> Esc
F12 –> F1

I still wish I could swap FN and CTRL. It would save me at least 10 mistakes a day. Lenovo: please make your BIOS update available retroactively!

Improving the FN/CTRL keys on my Lenovo laptop

Got a new Lenovo X301 ultralight laptop that I absolutely love (thin, light, very quiet with the SSD drive) except for one horrible flaw: the FN key is where CTRL is on every other PC keyboard. Instead of CTRL-C and CTRL-V, I’m always hitting FN-C and FN-V, which are useless.

If it’s your only PC keyboard, you might get used to it. But if you switch back and forth with a desktop keyboard like I do, it will most likely be infuriating.

At some point soon, Lenovo will come out with a BIOS — on new machines only – that allow the keys to be swapped. Meanwhile, there are various workarounds ranging from removing the FN key to trying to glue FN and CTRL together. (Unlike every other key, the FN key cannot simply be remapped by itself because it does not generate a keyscan code.)

The solution I’ve adopted is to remap the (useless) CapsLock key to CTRL. This works across keyboards so it applies to both my laptop and my external desktop keyboard when docked. So now I can use CapsLk-C and CapsLk-V, which are pretty easy to learn and get accustomed to. Here’s discussion about the CapsLk issue.

If you’re using Windows, the easiest way to turn CapsLk into CTRL is to add a Registry key and then reboot:

1. Open Notepad and create a file called “RemapCapsLock.reg” (be sure to turn off the dedault *.txt file extension)
2. Put in these lines:

REGEDIT4
[HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Control\Keyboard Layout]
“Scancode Map”=hex:00,00,00,00,00,00,00,00,02,00,00,00,1d,00,3a,00,00,00,00,00

3. Save it to Desktop. Double-click on it and say Yes to add to Registry. Then reboot and your CapsLk key will be remapped.

Lenovo: PLEASE make the BIOS update available for older machines!

Assessing persona prevalence empirically

I just obtained permission to post our latest paper on Personas. We argued previously that the personas method should not be considered to be scientific, and that a complete persona almost certainly describes few people or no one at all. In the new paper, we present a complete formal model, and evaluate the prevalence of “persona-like descriptions” with both analytical methods and empirical data. Full paper on persona prevalence.

There are two key implications here: (1) if you want to claim that a persona describes real people, you need strong multivariate evidence. (2) Without such evidence, we provide a formula you can use that will give a better estimate than simply assuming something. We show how this formula has a better than chance agreement with 60000 randomly generated persona-like descriptions in real data with up to 10000 respondents.

None of this says that personas are not inspiring or useful. It just says that they cannot be assumed to have verifiable information content, unless that is demonstrated empirically. As for alternatives to answer key design and business questions using empirical data, check out our paper on quantitative methods for product definition.

Personas

One of my papers from 2 years ago is still causing discussion: “The Personas’ New Clothes: Methodological and Practical Arguments against a Popular Method” by me and Russ Milham. Email from researchers I didn’t know led me to look up citations, and the article appears to be commonly cited when people present criticism of the personas method. Google search. The paper itself is here.

There are a few misunderstandings of our position out there. Our basic argument is simple. Persona authors often make two claims: (1) personas present real information about users; and (2) using personas leads to better products. In a nutshell, we argue that neither claim has been supported by empirical evidence; rather, the claims for personas’ utility are based on anecdotes, generally from their own authors or other interested parties (such as consultants selling them).

This does not mean that personas are bad, but they cannot be taken at face value. As researchers, we suggest that persona authors should either provide better evidence (and we suggest how) or make weaker claims.

Some persona users don’t make claims about their personas’ usefulness or correspondence to reality; they simply say that personas might be helpful for inspiration for some people or teams. We take no issue with that, as long as they don’t forget those caveats and reify the persona. Unfortunately it is probably very difficult for people to read a persona and not think that it describes a user group.

We’ve recently published empirical work on (quasi-)persona prevalence using several large datasets, demonstrating that once a description has more than a few attributes it describes few if any actual people. I’ll put that paper up as soon as I get reprint permission. (If you have access to HFES archives, it is “Quantitative Evaluation of Personas as Information”, Christopher N. Chapman, Edwin Love, Russell P. Milham, Paul ElRif, James L. Alford, from HFES conference 2008, New York.)

What should one do instead of personas? I advocate stronger empirical methods that have more demonstrable validity.

New papers on user research

Just uploaded 2 new papers on user research. First is work on a multi-factorial product interest scale, designed to be easily administered in survey format and applicable to consumer products. See the abstract on my “papers” page, or get the file directly: wip337-chapman.pdf

Second is an overview of quantitative methods that are helpful in early evaluation of product needs and strategy. The abstract is on my “papers” page, or the complete file is chapman-love-alford-quantitative-early-phase-ur-reprint.pdf

I’ll be uploading more papers soon.