Quantitative Data

Definición Rápida

Quantitative data in UX is numerical information that measures user behavior and attitudes. It focuses on the “how much,” “how many,” or “how often,” and is used to analyze patterns at a large scale, validate hypotheses, and measure the impact of design.

What Is Quantitative Data?

Imagine you own a store. Quantitative data is your sales report: it tells you how many customers came in, how many bought something, and which product sold the most. It gives you the numbers, the “what.”

In the digital world, this data answers questions like:

• How many users clicked the buy button?
• What percentage of users completed the registration form?
• What is the abandonment rate on the pricing page?
• How much time do users spend on average in the application?

This data is measurable and can be analyzed statistically to find patterns.

Quantitative Data vs. Qualitative Data

They are two sides of the same coin. You need both for a complete picture.

• Quantitative (What):

  • Focuses on numbers and statistics.
  • The sample is large (hundreds or thousands of users).
  • Answers “how many” and “how much.”
  • Example: “73% of users abandon the process at step 3.”

• Qualitative (Why):

  • Focuses on observations, motivations, and feelings.
  • The sample is small (5-10 users).
  • Answers “why” and “how.”
  • Example: “Users abandon at step 3 because the ‘Address 2’ field is mandatory and they do not know what to put.”

Why Is It Important?

• Measures impact: It is the only way to know if a redesign has had a positive (or negative) impact on user behavior at a large scale.
• Identifies problems: A spike in abandonment rate or a low conversion rate on a page are red flags that tell you where to investigate further.
• Prioritizes work: It helps make decisions about which problems to focus on. A problem that affects 80% of users is probably more urgent than one that affects 5%.
• Speaks the language of business: Stakeholders love numbers. Presenting your design arguments with quantitative data is much more persuasive.

Methods for Collecting Quantitative Data

• Web Analytics: Tools like [[Google Analytics]] or [[Mixpanel]].
• A/B Testing: Two versions of a design (A and B) are created and shown to different user groups to see which performs better based on a key metric (e.g., click-through rate).
• Large-Scale Surveys: [[User Surveys|Surveys]] with closed questions (multiple choice, scales) sent to a large number of users.
• Quantitative Usability Testing: Tests like those from [[Maze]] or [[Useberry]] that measure success rates, time on task, etc., with many participants.

Mentor Tips

• Always combine with qualitative data: Numbers tell you what is happening, but not why. Without the “why,” it is easy to draw wrong conclusions.
• Beware of vanity metrics: The number of page visits or average session time can be misleading. Focus on metrics that are directly tied to user and business goals (e.g., conversion rate, task success rate).
• Define your metrics before you start: Before launching a design, clearly define which metric you expect to improve and how you will measure it.

Resources and Tools

• Tools:
  • [[Google Analytics]]
  • [[Mixpanel]]
  • [[Hotjar]]
  • [[Maze]]
• Books:
  • Lean Analytics: An excellent guide for choosing the right metrics.