32 Qualitative and Quantitative Data in Business

32.1 Understanding the Two Types of Data

Data is essential for decision-making — but not all data is the same. As a data analyst, you’ll work primarily with two types of data: quantitative and qualitative. Each serves a different purpose and helps you answer distinct kinds of questions.

Quantitative data involves measurable, numerical information that tells you what is happening.
Qualitative data involves descriptive, explanatory information that tells you why it is happening.

Both are equally important in providing a full picture of business performance and customer behavior.

32.2 Quantitative Data

Quantitative data represents facts that can be counted or measured. It’s objective, precise, and often used to identify trends, measure outcomes, or compare results.

It answers questions such as: - What happened?
- How many?
- How often?

Examples include: - Monthly sales revenue
- Average customer rating
- Number of visitors per day
- Product return rates

Common quantitative tools:
Structured interviews, surveys, polls, and transaction records.

32.3 Qualitative Data

Qualitative data provides context and depth to numerical findings. It describes qualities, opinions, and motivations that can’t be represented numerically.

It answers questions such as: - Why did it happen?
- How did people feel or respond?

Examples include: - Customer comments in reviews
- Observations from focus groups
- Interview responses
- Social media feedback

Common qualitative tools:
Focus groups, open-ended survey questions, social media text analysis, and in-person interviews.

32.4 Example: Applying Data Analysis in a Movie Theater Chain

Imagine you are a data analyst for a national chain of movie theaters.
Your manager asks you to track trends in three key areas: 1. Movie attendance over time
2. Profitability of the concession stand
3. Evening audience preferences

You already have quantitative data to analyze these trends. However, you also plan to collect qualitative data to better understand customer behavior and motivations.

32.4.1 1. Movie Attendance Over Time

Starting with historical data from the theater’s loyalty and rewards program, you review attendance figures from the past three months.
Because this period didn’t include any holidays, you extend your analysis to a full year.

Your findings: - Average attendance is 550 per month,
- Attendance increases to 1,600 per month during holiday periods.

This quantitative insight shows that holidays drive higher attendance. You decide to revisit the data again after the theater raises evening ticket prices, to assess how that change affects attendance.

32.4.2 2. Profitability of the Concession Stand

You analyze the theater’s concession sales data and find that, while profitable, the profit margins are under 5%, with average purchases totaling $20 or less.

To gather deeper insights, you recommend creating an online customer survey that includes both quantitative and qualitative questions.

For example: - Quantitative: How much did you spend at the concession stand?
- Qualitative: What do you think about the quality and value of your purchases?

This additional information could help the team redesign the menu, adjust pricing, and improve overall profitability.

32.4.3 3. Evening Audience Preferences

Historical data shows that 7:30 PM is the most popular showtime, followed by 7:15 PM and 9:00 PM.
You notice that the 8:00 PM showtime has low attendance, so you suggest testing an 8:30 PM slot instead.

To support your hypothesis, you plan to include a qualitative survey question asking: > Which showtime do you prefer, 8:00 PM or 8:30 PM, and why?

This open-ended question helps you understand customer preferences beyond attendance numbers alone.

32.4.4 4. Ticket Pricing and Customer Behavior

Because the theater plans to raise evening ticket prices soon, you decide to include another qualitative question in your survey: > Under what circumstances would you choose a matinee over a nighttime showing?

This helps measure price sensitivity and explore how customers’ decisions might change with new pricing.

Your combined dataset — numerical trends from ticket sales (quantitative) and customer feedback (qualitative) — will enable you to make well-informed recommendations for pricing strategy.

32.5 Bringing It All Together

By using both quantitative and qualitative data, you gain a comprehensive view of the theater’s operations and customer behavior.

Business Focus	Quantitative Insight	Qualitative Insight
Attendance	1,600 average monthly attendance during holidays	Customers prefer holidays due to free time and special releases
Concession Profitability	5% profit margin	Customers feel prices are too high for portion size
Showtime Preference	7:30 PM most attended	Customers prefer later shows after dining out
Ticket Pricing	Anticipated drop in attendance	Families prefer matinee shows due to affordability

32.6 Key Takeaways

Quantitative data provides the what: measurable facts, counts, and patterns.
Qualitative data provides the why: reasons, opinions, and explanations.
Businesses need both to make informed, human-centered decisions.
Quantitative tools include structured interviews, surveys, and polls.
Qualitative tools include focus groups, social media analysis, and open-ended interviews.

By combining both data types, analysts can explain not only when and how often customers act, but also why they make those choices.
For example: - Customers might attend matinees to save money.
- They might prefer later showtimes to align with dinner schedules.
- They might choose a specific theater because of its comfortable seating or unique snacks.

As a data analyst, your goal is to connect numbers with narratives — transforming data into meaningful insights that drive smarter business decisions.