Big Data for smarter customer experiences

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It shouldn’t be a suprise  that a solid understanding of the relevant technology and user behavior is required to deliver the best possible user experience.
The real challenge is dealing with the huge amount of data that is being collected from different sources: the so-called “Three Vs (Volume, Velocity, Variety) of Big Data.” Most usability metrics deal with either qualitative or quantitative data.

Qualitative researchers ask broad questions of their subjects with the intention of uncovering patterns and trends. In this type of research the questions and answers don’t easily lend themselves to comparisons or sophisticated analysis. More complex examples of qualitative data include: video footage of user interactions with the system, tester observations of user navigation pathways, notes taken about problems experienced, comments/recommendations and answers to open-ended questions.
Quantitative researcher asks specific questions with a narrow focus, from which they collect numerical data samples from the participants to try and answer the question. These data are collected through anonymous user statistics, eye tracking, click-mapping and other methods that can be subjected to rigorous statistical, mathematical, or computational analysis techniques. Examples? Success rates, task time, error rates and satisfaction questionnaire ratings.
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Combining both as part of your usability research and testing is the best way to use them. Even though sometimes is hard to get the right results. Why?
Qualitative research reveals what the users think and do, but it doesn’t always show how or why they do it. On the other side, the use of quantitative research appears as a double-edged sword. The volume of data collected can provide very powerful insight for the decision makers, but there are also many drawbacks in terms of the effort required to obtain and process the informations. It’s also easy to jump to the wrong conclusions based on the weight of evidence that can be open to interpretation without fully understanding the whole context of the user interaction.
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With this amount of data collected it’s important to step back and think about what all of these values represent.

Data should not be used for finding evidence to support our own opinions and assumptions.
Data gives us an opportunity for to reach out to more users and understand them better.

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When Does Quantity Become Quality? How to navigate big data by Michael Lai