Overwhelmed by the data jargon? Here are some definitions from our experts.
Data-driven companies are six times more likely to be profitable than the competition. So it probably comes as no shock that marketers are really into data these days.
Walk into a marketing meeting and you’re probably going to hear phrases like qualitative and quantitative data, first-party data, behavioral data, structured data, or unstructured data.
And for those of us just getting started with data, all this terminology can get a bit overwhelming. Which is why today we’re going to dive into the definitions of two common data types—qualitative and quantitative data—and talk about what kind of value they bring to marketers.
Quantitative data is any data that’s easily structured, segmented, and analyzed.
For example, anything with numbers is quantitative. Website visits. Number of form fills. Number of transactions. Dollar value. Those are all things we can consistently measure and quantify across all types of people. Three hundred web visits is three hundred visits, no matter who’s visiting or why.
Similarly, any data that can be easily segmented into set answers (with no gray areas) is quantitative.
For example: a yes or no field on a survey (Have you ever used a meal delivery service?) is easy to segment – people either have or haven’t. A field with set answers (Which state do you live in?) is also easy to segment. You only have 50 options, assuming your audience is in the US.
Anything that can be measured consistently across all types of people is quantitative.
Because of its consistent measurability, quantitative data is great for data visualizations, highlighting potential areas of concern (or big wins we want to duplicate), and measuring our progress against targets/goals.
It’s also very easy to get in real time, which means we can respond to problem areas quickly.
Qualitative data—which is data about feelings, attitudes, needs, and desires—is more difficult to measure. It’s the open-ended question on your survey. It’s the wildly variable comments on your blog post. It’s the sliding scale of how your customers feel about your brand.
Qualitative data is hard to put into a dashboard or represent with the same kind of concrete certainty as quantitative data, but it’s great at getting to the root of a problem.
For example: If quantitative data tells me that all of a sudden our form completion rates have dropped by 20% (a huge number), qualitative data can help me figure out the cause.
Maybe there’s a problem with the form. Maybe there’s a problem with the site. Maybe I’ve been targeting a whole different type of user and they’re not interested in filling out this form because they aren’t qualified. If I set up some exit surveys asking why people didn’t complete the form, I can get direct answers (qualitative data). Without that, I’m just making my best guess.
So, which kind of data is better?
The answer: both.
These two types of data are both important because they answer very different questions.
Quantitative data tells us what’s going on. How many people have purchased our new satchel? How many people have returned it? How many people opened our upsell email after the purchase?
And then qualitative data swoops in to tell us why.
What did the customers who returned the satchel say in the “why are you returning this?” box? How are they feeling about our brand? What kind of sentiment is coming through on reviews for the new satchel?
Quantitative data often pinpoints problem areas and successes. And qualitative data answers the more difficult questions around context. Which is why the most robust analytics teams rely on both.
Demographic data is typically quantitative. Fields for age range, country of residence, household income, height…they’ve all got a limited number of possible answers and are categories we can define without measuring nebulous concepts like sentiment.
Now, are there places where demographic data can be harder to segment, where possible answers actually are unlimited? Certainly. Job titles, for example, run the gamut these days. But with demographic data, that’s usually an easy problem to solve. You can create fields and segment respondents by seniority and department instead of title, for example.
Behavioral data is also typically quantitative. It’s there to describe people’s behavior and usually it starts out doing so in a quantitative way. John Doe visited your website 10 times. Brody King stopped by 12. And Shady Lane visited twice. Those are all quantifiable answers and they’re all behavior.
Quantitative data comes from many sources, but the three most common places our clients collect quantitative customer data are:
:: Their websites and microsites
:: Their ad platforms
:: Their CRM system
These are typically the three big data-source pillars for any company just starting out with data collection. And once you have those nailed down, you can start building from there.
Since qualitative data measures things like sentiment and desires, it’s typically collected in a more free-form way. Some common sources include:
:: Exit surveys on websites
:: Product reviews
:: Comments on blog posts/websites
:: Social media posts
:: Other forms of surveys
So, if both types of data are important, where do we begin? Which data should marketers start collecting first? How can we make smart decisions about data collection and tracking? Just like anything in life, the answer is about taking things one step at a time:
Before you start collecting any data, it’s important to understand the strategy behind what you’re doing. If our main goal is customer retention and all the data I’m collecting is about new customers, I’m probably focusing my efforts in the wrong place.
Quantitative data helps us pinpoint where we need qualitative answers, so it should always be the first step in data collection.
Trying to do too much at once often means a longer ramp-up time without real results. When this happens, it’s easy to lose steam on a data project before it really gets off the ground.
Instead, focus on a single business goal to start and collect (and use) the data that supports that specific goal. It’ll take a lot less time to see encouraging results, and you can layer in more goals and more data as you go. (This is the foundation to our own StartSmart program.)
Of course, when it comes to understanding data and making it actionable, the fastest path to success is trusting the experts. With a CDP like Lytics and our StartSmart program (designed to get you to results in just 60 days), you could skip a lot of the learning curve.
We’d love to show you how. Reach out to our team today for a free demo.
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