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3 Reasons why Quantitative Data is not enough

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ROI (return on investment), TAM (total available market), SAM (serviced available market), percentage of market share… The language of business is inseparable from the language of numbers! And all for good reason – a profitable business needs to know an idea’s potential for growth before committing resources to it

However, relying on quantitative data alone for your business model and product strategy is not enough and can adversely impact business growth! Rather, the most well-informed business decisions should be made by triangulating different data sources to get a more holistic picture.

Let’s set the stage by first looking at the differences between qualitative and quantitative data and the strengths of quantitative data. With that understanding in place, we will examine the limits of quantitative data and finally, explore ways to capitalise on the strengths of both data types in our research studies.

What are the differences between qualitative data and quantitative data?

In short, the two biggest differences lie in the scale at which the data is collected and whether the data is measurable. The table below lists some comparisons.

Why is quantitative data important?

Quantitative data’s power stems from the scale at which it can be collected. Some benefits include:

1. Offering different perspectives on an issue

For instance, we can learn from sales data when total sales are highest/lowest, what types of products sell best in which months, and when higher-ticket items are sold. These data insights are important for informing overall marketing strategy, by meeting customers where they are at in their customer journey.

2. Giving greater confidence in recommendations

Seeing 3 out of 8 users get stuck on the same spot while navigating a website might give us a hunch that something is wrong, but since it’s not quite the majority of users, we might be unsure how widespread the issue is. If 80 out of 100 users stumble on the same spot however, one can call for a design change with greater confidence.

3. More persuasive justifications for design/product strategy changes

Building on the previous point, business decision-makers might be unconvinced by the findings of 8 interviews, but seeing how widespread the problem is might persuade them otherwise.

3 reasons why quantitative data is not enough

But no data type is perfect and even quantitative data has its limitations. Here are 3 reasons why quantitative data is not enough:

1. It is not a good fit for the research objectives.

Since quantitative data is measurable, we must first know what needs to be measured. However, if the project’s research objectives are to understand what consumers’ shopping behaviors are in order to inform the direction of a new product launch, quantitative data is not going to yield useful data as the team doesn’t even understand what existing shopping behaviors look like.

2. Unable to understand the root of users’ problems

Sure, we might have noticed that respondents spend long amounts of time on a particular web page from web analytics, but we do not know why. Are respondents unable to find what they want? Or are they very interested in the content?

3. Can’t understand what users’ motivations are

Let’s use furniture shopping as an example. We can see the numbers indicating certain trends at particular months, but we don’t know why users behave that way. Are they buying more at certain times of the year due to sales? Or because it’s the moving season and they need new furniture? Or because it’s the holiday season and they need to host people?

Photo by Tim Gouw on Unsplash

How to design a research study to collect quantitative and qualitative data

To make better informed UX strategy and business growth decisions, the best approach is to triangulate your data to collect both qualitative and quantitative data. Here are the six main steps to designing a holistic UX research strategy, with the types of data collected indicated.

Surveys should be designed after initial qualitative research has been conducted, as we need to first have some idea of the questions to ask and the expected answers we might find. Knowing the expected answers is particularly helpful for designing survey question options, and can help save coding time as well as improve data quality.

Applying qualitative and quantitative data research methods

Photo by Ella Olsson on Unsplash

Let’s illustrate the theory with an example and assume that our company is planning to launch some meal subscription plans. We want to conduct research to understand how this product should be designed.

Our research question might look like this: How do people plan their meals for the week?

Based on that, our research goals might look like these:

  • Uncover people’s decision-making process behind meal planning
  • Learn about the current tools they use and any pain points and frustrations they face
  • Understand their goals and motivations for meal planning

Looking at customer data, we realise that new subscriptions are highest around August-September, but subscription cancellations and pauses occur around November-December. Although no one knows the reason(s) behind these data trends, you now have some ideas of questions you can ask in your discussion guide!

Our user interviews indicate that most people have recipes in mind first before they go grocery shopping, but some people only decide on the ingredients to buy after seeing what is on sale. We also find that two frustrations people face with meal subscription kits is the lack of instructions on basic cooking terminology (what on earth is medium heat versus high heat?) and the inability to see what meal subscription kits on a long time horizon (people want to be able to plan on a monthly, not just a weekly basis). We also find that people hit pause on their subscriptions around November-December because they are travelling or hosting friends from out of town.

Now, we can validate these findings at a wider scale, with surveys sent out to screened participants who both grocery shop and subscribe to meal plans. Some questions we could ask include:

  • What is the biggest frustration you face with meal subscriptions?
  1. Lack of instructions on basic cooking terminology
  2. Not being able to see what meals are offered on a monthly basis
  3. Can’t change my plans easily
  4. Other
  • What is the main reason for you pausing meal subscriptions?
  1. Travelling
  2. Hosting friends from out of town
  3. Not interested in the current meal-plan line-up
  4. Other
  • Which method best describes how you shop for food?
  1. I plan what recipes I want to cook first before grocery shopping.
  2. I look at online ads to see what’s on sale, then plan my recipes and grocery shop.
  3. I go to a grocery shop, see what’s on sale, and then plan my recipes from that.
  4. I have an idea of recipes I want to make, but make adjustments based on what I see on sale when I go into a store.
  5. I have no plan, I just buy whatever ingredient looks good.
  6. Other

From our survey, we find that not being able to see what plans are offered on a monthly basis is the biggest frustration and this aligns with our landscape analysis, which shows that all current companies only offer meal subscription plans on a weekly basis. We also find that most people pause meal subscriptions because they are travelling.

These lead to two exciting research possibilities. We can design a variety of meal subscription plans (monthly and biweekly) to better determine the most popular prices and subscription content. We also have a new UX product strategy to explore – could we partner with Airbnb to offer curated meal kits with local ingredients for various travel destinations?


Although the above example is fictive, it demonstrates how qualitative data insights from the user interviews gave us an idea of the areas to validate in our surveys. The surveys then provided us the quantitative data needed to determine which product strategy to focus on in the short-term and a long-term strategy. As with investing, research and business strategy are best served by diversification!

If you want to know more about user experience and its industry, do check out our other resources available on our website, such as our articles, weekly webinars, and podcasts.

CuriousCore offers mid-career professionals specialized and career accelerator courses and we also provide practitioner-led masterclasses and consultations for organisations to improve their customer experience strategy and business growth.