Getting to Know Conjoint Analysis

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

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7 Ways to Handle Missing Data

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

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How To Weight Data To Make More Balanced Decisions

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. 

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Getting to Know Your Data Types

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

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Predictive Customer Analytics 101: The Correlation

Want to know what customers are likely to do? You’re not alone. Most organizations would love to predict their customers’ next action or attitude. Unfortunately, there isn’t an analytics crystal ball that provides a clear and accurate picture of the future. Instead, we have to rely on the much murkier reality of past data to

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How Confident Do You Need to be in Your Research?

Every estimate we make from a sample of customer data contains error. Confidence intervals tell us how much faith we can have in our estimates. Confidence intervals quantify the most likely range for the unknown value we’re estimating. For example, if we observe 27 out of 30 users (90%) completing a task, we can be

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What Does Statistically Significant Mean?

Statistically significant. It’s a phrase that’s packed with both meaning, and syllables. It’s hard to say and harder to understand. Yet it’s one of the most common phrases heard when dealing with quantitative methods. While the phrase statistically significant represents the result of a rational exercise with numbers, it has a way of evoking as

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6 Tips for Better Data Analysis

At our company, we collect and examine a lot of data from various studies: usability tests, branding studies, customer-segmentation analyses, and so on. While you always can’t control the quality of the responses, or the questions asked to participants, you can make the most with the data you receive by using these six techniques the

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How to Compute a Confidence Interval in 5 Easy Steps

Confidence intervals are your frenemies. They are one of the most useful statistical techniques you can apply to customer data. At the same time they can be perplexing and cumbersome. But confidence intervals provide an essential understanding of how much faith we can have in our sample estimates, from any sample size, from 2 to

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