Quantitative and Qualitative Variables: A Guide for SMEs

Qualitative and quantitative variables, in a nutshell: a quantitative variable is expressed as a number that you can measure or count (average ticket, visits per month); a qualitative variable is expressed as 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 type? 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 drinks, bakery), branch, payment method, contact channel, drink size (small, medium, large).

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

It depends on how you use it. Each customer's response (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.

The difference in a table

This separation determines whether you send the same promo to your entire database or trigger an action that actually boosts revenue.

Quick example: a coffee shop in Puebla

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

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

What are quantitative variables

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

In coffee shops, restaurants, and retail 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)

• Revenue per customer in a period

How to use them in operations

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

2. Set goals by branch: 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 open rates; look at the 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 a simple spreadsheet:

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

What is a qualitative variable

A qualitative variable classifies. It doesn't say how much was spent; it says what they prefer, where they buy, or how they want to be contacted.

Typical examples in food SMBs and physical retail:

• Preferred drink or dish

• Regular branch

• Channel (counter, WhatsApp, delivery)

• Payment method

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

• Loyalty tier 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 + bakery at the Downtown branch via WhatsApp is actionable: stock, sales pitch, and campaign channel.

A business doesn't need advanced statistics. It needs to avoid mixing categories with absurd averages ("average branch") and instead cross-reference 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 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 crossing them:

This logic powers data-driven decision making and relies on sales reports with examples to move beyond intuition.

Mistakes that cost margin

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

2. Treating IDs as metrics. Receipt or customer IDs identify; they do not measure behavior.

3. Same promo for everyone. Without qualitative data, 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 total. The overall number hides the specific branch that is losing frequency.

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

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

1. Quantitative variables (numbers indicating 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, wait minutes, kilos of coffee, temperature.

2. Qualitative variables (categories or attributes)

   • Nominal: categories with no order. Branch, product category, payment method, contact channel, favorite drink.

   • Ordinal: categories with a logical order. Small → medium → large size; new → frequent → loyal tier; 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. Useful for frequency and repeat purchase goals.

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

• Note: the average of a discrete variable can have decimals ("the average customer visits 6.8 times a month"). The variable remains discrete; only the summary does not.

Nominal vs. ordinal qualitative variables

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

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

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, money, points. Here, saying "double" actually makes sense.

What calculation and graph to use with each type

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

5-Second cues

• “Visited 6 times” → quantitative

• “Prefires cold drinks” → qualitative

• “Ticket above average” → quantitative

• “Buys in Downtown” → qualitative

• “High loyalty” → ordinal qualitative

Example of a segment ready for WhatsApp

Segment name: At-risk high-ticket cold drink lovers
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, ticket of the return visit, second visit within 7 days.

That is customer segmentation applied to the shop floor, not to an academic paper.

3-Step plan for this week

1. Audit 10 fields in your database: mark them Q (quantitative) or C (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: repeat purchase within 7/14 days, ticket, and points redemption.

If you want to move from scattered data to segments and loyalty campaigns that actually boost retention in your coffee shop or restaurant, Swirvle centralizes customers, purchasing habits, points, and results by branch to act with context — not with end-of-day intuition.