To answer most user-research questions fundamental statistical techniques like confidence intervals, t-tests, and 2 proportion tests will do the trick. But to answer some questions most effectively you need to use more advanced techniques. Each of these techniques requires specialized software (e.g., SPSS, Minitab, R) and training on how to set up and interpret the
When you’re planning a study to compare multiple interfaces, one of the first choices to consider is whether to use a within-subjects or between-subjects approach. The interfaces can include anything you want to compare: design mockups, competing websites, or a new mobile app design with an old mobile app design. The choice comes down to
You can slice and dice data in a lot of ways using a variety of statistical tests. The type of study, data type, and your research question will dictate the right statistical test you need to perform. With the five tests I cover here, you can test most of your hypotheses in the customer experience.
Geek out this summer and catch up on some stats. With your kids home from school with their reading lists, now’s the perfect time to create a reading list of your own to tackle this summer. I know reading statistics articles probably isn’t your idea of a summer vacation; however, I’ve selected ten articles from
Which design do you prefer? Which product would you pick? Which website do you find more usable? A cornerstone of customer research is asking preferences. A common scenario is to show participants a set of designs or two or more websites, and then ask customers which design or site they prefer. While customer preferences often
What do customers want? It’s the million dollar question with what seems like a million answers. Prioritizing product features, including conducting a top-tasks analysis, is an essential step in creating the optimal product and experience. During the prioritization phase, our clients will on occasion specifically ask for a conjoint analysis. As data geeks, we love
Data goes missing. It’s a fact of life for the researcher. You put time and money into a research study. You do what you can to prevent missing data and dropout, but missing values happen and you have to deal with it. How do you address that lost data? First, determine the pattern of your
You know the scenario. You’re browsing books on Amazon, and you scroll down to see the now-familiar phrase: “Customers Who Bought This Item Also Bought…” Amazon then presents you with a list of books that, in many cases, are of interest to you. Have you ever wondered how Amazon knows what books to recommend? The
Rarely is a customer population made up of a homogenous group of customers who share the same attributes. Consequently, our samples contain a mix of customers who may or may not reflect the composition of the customer population. There are a number of variables that affect how customers think and behave toward products and services.
Know your data. When measuring the customer experience, one of the first things you need to understand is how to identify and categorize the data you come across. It’s one of the first things covered in our UX Boot Camp and it’s something I cover in Chapter 2 of Customer Analytics for Dummies. Early consideration
