24

I call stuff like this "impediments", since stuff that causes annoyance to the user is basically stopping/impairing the user from what he is trying to achieve. But i think there are many words that fit, in my opinion "annoyance" is not a bad choice either.


12

What about "obstacle"? It's a general term for something that is making something else difficult.


11

If you are looking for a phrase that connects with technical UX literature, I suggest "cognitive burdens". The term cognitive burden /cognitive load comes from psychology, but it is commonly used in UX. For more about cognitive load, see this Nielsen/Norman Group article, "Minimize Cognitive Load to Maximize Usability": https://www.nngroup.com/articles/...


10

Effect Size In A/B testing, effect size is the observed difference in performance between A and B. Take, for example, the following A/B results: A: 10 conversions out of 103 visits B: 6 conversions out of 97 visits. So A has a conversion rate of 10/103 = 9.71% while B has a conversion rate of 6/97 = 6.19%. The data suggest that over many visits, A will ...


8

Too me it sounds like you are trying to trigger the subconscious mind faster to take action, so the conscious mind has less time to make a decision. Isn't that partially a large part of UX? Reducing cognitive load for users and ensuring features are easy to use intuitively? Why is this unethical? Would you consider Amazon's "One click buy" button ...


7

i think the word you're looking for is "hindrance"?


6

Some thoughts on the experiment are: Subjective. Your test seems to be more like Luscher color test, but bear in mind that color preferences are subjective and not permanent. Randomize. To get the proof of the golden triangle effect just mix randomly the webpages before each test session. Bias. You are biased experimentator, as you have some expectations on ...


6

The ecommerce company I work for uses modals (pop-ups) to show a quick view of products. Google Analytics doesn't detect this interaction (no trigger was set on it either) so I don't have any data of people who click on the quickview vs. people who navigate to the products detail page. After implementation the effect of it (on conversion rates or something ...


5

The answer to this can be a little complicated and i know lot of layout designers and grid specialists have spent a lifetime contemplating on this. Before entering into the discussion we need to ask whether you want to be open about the profits to your advertisers or whether its going to be an under the covers thing. If you are not going to be telling this ...


5

I'd look at plotting the ideas on an Impact/Effort Matrix, and use that to prioritize. The low-effort, high-impact ideas would be prioritized first; the high-effort, low-impact deliverables would be disregarded. If you use Google Analytics, you can use Google Optimize to knock out the easy ones, like testing colors and button labels: https://www.google.com/...


4

Here's a great article from Baymard on Quick Views Baymard writes in their article that quick views often lead to small increases in conversion. However... Though quick views may slightly increase conversions at times, there is an overall poor design on the product listing page leading users to rely on quick views. We are simply optimizing something that ...


3

There are two problems with your approach: You're only counting the success - that makes it hard to evaluate performance as your a/b changes - e.g. if you change only b to c, you now have twice as many users who saw a then either b or c. You want to count all views as well as conversions Maybe it's OK for your scenario, but you're ignoring the user's flow - ...


3

Should each user try all four versions? Re-using the same user gives you the ability to statistically discriminate smaller performance differences among your versions, assuming you know the right statistics to apply (e.g., repeated-measures ANOVA, for task completion time). Re-using users allows you to factor out the users’ idiosyncratic differences (as ...


3

Test in Context First, I understand your explanation of the difference between having the choice to use software and not having the choice, but I don't really see how that's going to affect your testing. A user is either using the app to complete a task, or they're not, and you can't really control for all the reasons they might not be using it. Sure, in a ...


3

This is the same as asking 'are surveys reliable?'. The reliability of an experiment depends on the methodology followed, context, participants and time. There are advantages and disadvantages of lab and field tests. And in both it is impossible to achieve full control of the variables. Yes, in lab tests you have more control but that doesn't mean that ...


3

For starters, a rigorous Quality Assurance (QA) process is needed before launching a test. Your situation is quite common - find the variant losing only because it contained a bug. So you need to check if the new variant works as it should on all browsers and devices. Not only that but also if all the goals are set up correctly in your testing tool (often ...


3

I'd argue to use... drumroll, dramatic pause Annoyance Most of the other suggestions to me suggest an actual prevention of progress. I'd also argue there any perceived non-professionalism is far less than avoiding a precise term on some arbitrary grounds. I also assume this is meant as a set scale, where problems are ranked by severity: Bug report: ...


3

In the world of conversion optimization the most impactful way to convey the problem is: Bounce rate driver “Annoyances”, “interruptions”, “distractions”, and “obstacles” will be quickly dismissed by a conversion-focused marketer, for better or worse. “Clutter” is just as easily dismissed ...


2

I think you have the answer in your question. To me, AB-Testing isn't a post-launch service only. In fact we use it prior to launch to evaluate different patterns. If you use AB-Testing as a part of the UX design and evaluation, your customers would probably see the value of it. The other not selling post-services is really a marketing issue and I can only ...


2

Your common sense assumptions work ideally... for robots. Humans are more complex and irrational. Place do play some role in choice, but decisions taking goes beyond placement. In terms of Behavioral Economics, you'd consider effects, which are influence choice architecture. Some of them are (from the List of Behavior Economic Principles...): Choice ...


2

Smashing magazine published a nice article about AB testing best practices and a link to calculator of audience you need to ensure your results are statistically significant. Check these links: http://www.smashingmagazine.com/2010/06/the-ultimate-guide-to-a-b-testing/ https://vwo.com/ab-split-test-significance-calculator/


2

(most) Enterprise A/B testing tools will give you a margin of error calculation given your desired confidence index. What this means, (assuming you want 90% confidence) <confidence %> of the time my conversion rate will fall within +/-<margin of error> of <conversion rate> 90% of the time my conversion rate will be +/- 10% of a 5% ...


2

First to explain what A/B test is. Say you have a website where the homepage is really important. While you design it, you wonder whether it would be better to write in the heading section ( h1 tag). You say it would be better to write "Welcome to our website" while your colleague argues that "Take a look at our services" will do a better job. To answer ...


2

It depends on how your "b" variant are implemented. Separate pages are difficult to test, whereas javascript transformations are much easier. Here is some general advice, hope it helps: Javascript method: All of your product pages should share similar CSS selectors. For example, if you are changing all buttons on all product pages you could use generic ...


2

Multiple A/B tests not only are very common, but they are extremely useful, way more than simple A/B. However, it has a different name: Multivariate Testing. Just imagine this: I run a test for page A and page B and decide page B performs better than page A. Great, let's go with page B. Easy, no? Then I decide to test another element, say a button, and ...


2

Optimization tools usually give more statistics than the uplift %. Some will even tell you how many days/weeks the test should run given your current traffic, current conversion rate, and expected uplift. The two most important things to take note of are the Confidence Level, and Margin of Error. This usually uses a statistical measurement like Student's T-...


2

First and foremost, you need to figure out what the goal of the test would be? The big question is: What result would you need to see for your team to implement autoplay (b) as a successful winner? If b resulted in 100% more clicks, would you implement it? No - that could be clicks to pause. If b resulted in 100% more views, would you implement it? No - ...


2

No, you can test different sizes as long as they're the only variable you measure. Otherwise, if changes go further (including the fact that a different size of 1 element could mean different variables for all other elements), you'll need to go with multivariate , probably using MANOVA. An easy example: if your size change makes other content go over or ...


2

Where Option A and Option B have markedly different response rates, 1000 is enough. Popular A/B test platform Optimizely, explains it this way: https://help.optimizely.com/Analyze_Results/Statistical_significance_in_Optimizely The big selling point of A/B test platforms is that they provide "statistically significant" results, but "statistically ...


2

Is there a way to measure if version A or B is better based on usability? Yes. Select what dimension of usability you want to pursue, and measure that. Usually, A/B tests measure “success” or “conversion” rates, or the probability that a user will complete a key task. So if the task is to fill out a form (e.g., to make purchase, signup, or post), count how ...


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