Quantitative and qualitative variables: examples

Qualitative and quantitative variables, in one line: a quantitative variable is expressed with a number that you can measure or count (average ticket, monthly visits); a qualitative variable is expressed with a category or quality (favorite drink, branch, payment method).

Quick questions (short answer)

What is the difference between qualitative and quantitative variables?

Quantitative answers how much? with a number that you can add or average. Qualitative answers what kind? with a label that you can only count or group.

What are examples of quantitative variables in a business?

Average ticket ($85 MXN), visits per month (6), items per ticket (2), waiting minutes (4.5), accumulated points (120).

What are examples of qualitative variables?

Product category (hot drinks, cold, bakery), branch, payment method, contact channel, drink size (small, medium, large).

Is a 0 to 10 rating from an NPS survey qualitative or quantitative?

It depends on how you use it. The response of each customer (0 to 10) is an ordered scale; when grouped into promoters, passives, and detractors, it becomes ordinal qualitative. The final NPS (percentage of promoters minus percentage of detractors) is a quantitative indicator.

Is a number always quantitative?

No. ZIP code, phone number, or branch number are numbers that only identify; they are qualitative. Averaging them means nothing.

Difference between qualitative and quantitative variable

The difference is in the type of response: the quantitative variable is answered with a number that makes sense to add, average, or compare (age, height, pesos spent); the qualitative variable is answered with a category or quality that you can only count or group (eye color, marital status, branch).

This separation decides whether you send the same promo to the entire database or trigger an action that actually drives sales.

The 4 types of variables in a table

Examples of quantitative and qualitative variables

These are 10 classic examples of each type, useful for homework, surveys, or any database:

Age, height, weight, temperature, income, hours of sleep, and distance are continuous (they accept decimals); number of children and siblings are discrete. Education level is ordinal; the other qualitative variables in the table are nominal.

Now let's see how these same ideas look in a real business.

Quick example: a coffee shop in Puebla

Imagine a coffee shop in Puebla with a loyalty CRM. In one week you see:

Same "average sale" does not mean the same customer. Qualitative and quantitative variables together tell you who, with what offer, and when.

What are quantitative variables

Quantitative variables measure quantities. In a brick-and-mortar business, they are the data that you can add, average, or compare month over month without inventing meanings.

In coffee shops, restaurants, and physical stores with loyalty programs, they usually look like this:

• Ticket / purchase amount (MXN)

• Frequency (visits per week or month)

• Days since last purchase

• Points accumulated or redeemed

• Units per ticket (drinks, dishes, add-ons)

• Income per customer in a period

How to use them in operations

1. Detect real value: a customer spending $180 every 15 days is not "the same" as one spending $50 three times a week.

2. Set branch goals: average ticket and visits, not just total revenue.

3. Prioritize reactivation: if "days since last purchase" exceeds your threshold (for example, 14 days in an office coffee shop), the message goes out before the habit cools down.

4. Measure campaigns: not just opens; look at ticket and repeat purchases of the targeted segment.

Rule of thumb: if the data allows for "double" or "half" with commercial sense (visits, pesos, points), it is almost certainly quantitative.

Mini worksheet (CRM fields)

Copy these fields into your CRM or into a simple spreadsheet:

With Swirvle, those fields stop living in disconnected Excels: each purchase updates history, points, and frequency to segment without guessing.

In a business, these variables describe your customers: how much they spend and how often they come (quantitative) and why they choose you (qualitative). With them you can define your target market, recognize the types of consumers who buy from you and store them in a CRM system; Swirvle's CRM and loyalty program records them with every sale. They also help you analyze your market, for example to understand what a monopoly is and how it affects prices.

What is a qualitative variable

A qualitative variable classifies. It doesn't tell you how much they spent; it tells you what they prefer, where they buy, or how they want you to contact them.

Typical examples in small food businesses and physical retail:

• Preferred drink or dish

• Regular branch

• Channel (counter, WhatsApp, delivery)

• Payment method

• Preferred type of reward (points, coupon, free product)

• Loyalty level on a scale (new / frequent / loyal) — ordinal qualitative

Why it matters as much as the number

Knowing that the ticket went up is useful. Knowing that it went up in cold drinks + pastry at the Downtown branch via WhatsApp is actionable: assortment, sales script, and campaign channel.

A business doesn't need advanced statistics. It needs to avoid mixing categories with absurd averages ("branch average") and instead cross category + number.

Examples in a coffee shop: your classified metrics

This is how the data you already have in your POS, CRM, or surveys is classified (example values):

The rule: if it makes sense to say "double" or to calculate an average, it is quantitative. If you can only count how many there are of each type, it is qualitative.

Quantitative and qualitative variables together (what actually sells)

The commercial magic lies in the intersection:

This logic fuels data-driven decision making and relies on sales reports with examples to keep you from relying on pure intuition.

Errors that cost margin

1. Averaging categories. "Average payment method" is useless. Use counts or share %.

2. Treating IDs as metrics. Order number or customer ID identify; they do not measure behavior.

3. Same promo for everyone. Without qualitative variables, you give away margin to those who were already going to buy.

4. Inventing order where there is none. Branch A is not "greater" than Branch B; they are different classes.

5. Looking only at the consolidated numbers. The total number hides the branch that is already losing frequency.

Types of variables: discrete, continuous, nominal, and ordinal

Every variable falls into one of these four boxes. Here is the complete map:

1. Quantitative variables (numbers representing a quantity)

   • Discrete: counted in whole numbers. Visits per month, items per ticket, customers served per hour, accumulated points.

   • Continuous: measured and accept decimals. Ticket in pesos, liters of milk, waiting minutes, kilograms of coffee, temperature.

2. Qualitative variables (categories or attributes)

   • Nominal: categories without order. Branch, product category, payment method, contact channel, favorite drink.

   • Ordinal: categories with a logical order. Small → medium → large size; new → frequent → loyal level; low → medium → high satisfaction.

Discrete vs continuous quantitative variables

• Discrete: there is no such thing as "7.4 visits" from a customer in a month. You count 7. It is useful for frequency and repeat purchase goals.

• Continuous: a ticket can be $92.50 MXN and wait time 4.5 minutes. Useful for averages, ranges, and alerts.

• Note: the average of a discrete variable can have decimals ("the average customer comes 6.8 times a month"). The variable itself is still discrete; the summary is not.

Nominal vs ordinal qualitative variables

• Nominal: no category is "greater" than another. Downtown branch is not more than Cholula branch; they are simply different.

• Ordinal: there is an order, but the distance between levels is not exact. From "frequent" to "loyal" there is no fixed "X points" difference, just a step.

Measurement scales (short version)

1. Nominal (qualitative): categories without order — branch, flavor, channel.

2. Ordinal (qualitative): categories with order — new → frequent → loyal.

3. Interval (quantitative): numbers without a true zero — temperature in °C (0 °C is not the "absence of temperature").

4. Ratio (quantitative): numbers with a true zero — visits, pesos, points. Here it does make sense to say "double".

What calculation and chart to use with each type

This avoids misleading charts (lines for "payment types") and invented averages.

5-second cues

• “Came 6 times” → quantitative

• “Prefers cold drinks” → qualitative

• “Ticket above average” → quantitative

• “Buys at Downtown” → qualitative

• “High loyalty” → ordinal qualitative

Example of a segment ready for WhatsApp

WhatsApp messaging is available only on the Swirvle Premium plan.

Segment Name: High-ticket cold-drink fans at risk
Rules:

• favorite_category = Cold (qualitative)

• average_ticket_30d ≥ 120 (quantitative)

• days_since_last_purchase ≥ 14 (quantitative)

• branch = Downtown (qualitative)

Message (example): “Your cold brew misses you. This week: +20 points if you return to Downtown before Friday.”
What you measure: redemption, return visit ticket, second visit within 7 days.

This is customer segmentation applied to your counter, not an academic paper.

3-step plan for this week

1. Audit 10 fields in your database: mark them QN (quantitative) or QL (qualitative).

2. Build a mixed segment (e.g., ticket > $120 + cold category + visits ≤ 2 in 30 days).

3. Launch an action and measure only that segment: repurchase at 7/14 days, ticket size, and point redemptions.

Frequently Asked Questions

What is the difference between a quantitative and a qualitative variable?

A quantitative variable is a number you measure or count, like ticket size or monthly visits. A qualitative variable describes a category or quality, like the purchasing channel or favorite drink.

Is age a quantitative or qualitative variable?

It is quantitative, because it is expressed as a number. If you group it into ranges like "youth" or "adult," you are treating it as an ordinal qualitative variable.

Is a ZIP code a quantitative variable?

No. Although written with numbers, it is a label identifying an area, so it is nominal qualitative: it makes no sense to add it or calculate its average.

How do I use both variables together in my business?

Combine a number with a category to build segments. For example, high-ticket customers who buy cold drinks and haven't returned in weeks, and send them an offer tailored just for them.

If you want to move from isolated data to segments and loyalty campaigns that actually drive repeat visits to your coffee shop or restaurant, Swirvle centralizes customers, purchasing habits, points, and results by branch to help you act with context — not just end-of-day intuition.