In this article, we will discuss:
- Key differences: qualitative data Vs. quantitative data
- Qualitative open-source Datasets that businesses are collecting
- Top qualitative data collection methodologies
Key differences: qualitative data Vs. quantitative data
Quantitative data is information that can be quantified in numbers such as:
- How many shares, likes, and followers a social media influencer has
- How much a stock or product price has fluctuated
- How many new subscribers/downloads an app has
- How much time does it take for an eCommerce consumer to make a positive purchase decision
As you can see, the answers to these questions are numeric-based and very concrete.
Qualitative data, on the other hand, gives you context, and a clear narrative of what is going on in your industry, such as:
- How do consumers feel about a brand or product on social media?
- What is the sentiment on discussion forums regarding the current market value of a specific stock?
- How are consumers responding to competitor marketing campaigns?
- Are search queries highlighting buyer concerns about certain products?
As you can see, these are more abstract ideas that can help inform business strategists, as well as decision-makers.
Qualitative open-source Datasets that businesses are collecting
Here are some of the top qualitative Datasets that businesses are currently collecting:
One: Social sentiment
Social media could be a great place to gauge sentiment regarding a brand or product. By collecting and analyzing user posts, and group discussions, companies can discern a lot about target audience proclivities.
For example, ‘How do consumers in a certain area/age range feel about a brand, and what can be done to shift perception?’ E.g., A company perceived as polluting the environment may want to pivot to greener production methods, packaging, branding, as well as marketing tactics.
Automated LinkedIn scraping, on the other end of the spectrum, can shed light on the ‘sentiment’ of employees and executives at competing companies. This can help Human Resources better recruit while improving the corporate culture for employee retention, for example.
Two: Reviews and discussions
‘The review economy’ relies largely on peer-to-peer experience sharing.
- Whether a traveler is looking for a hostel to stay at
- A shopper trying to gauge the reliability of a vendor on a marketplace
- Or a user considering the utility of downloading a new mobile app.
Almost every single activity, especially in the world of digital economics, has some form of peer review, discussion, or feedback being generated.
For example, ‘What do real users think of a travel web site’s user interface, and what can be done by a Venture Capital (VC) firm looking to create value add?’ E.g., A travel site whose users perceive the system as clunky and buggy presents an opportunity for VCs to swoop in, fix what’s broken, and generate a handsome profit.
Three: Trends (search, shopping)
Trends may include what people are looking for on search engines and how they are phrasing their search queries. For example, ‘How bad is taking Advil for your kidney heath?’ is a search question that spotlights patient concern over the long-term effects of headache medication on their body’s health. A medication producer may want to then use this information and preempt consumer concerns vis-a-vis marketing campaigns and informational branding/packaging. Or by sponsoring academic research into the matter.
Four: User engagement
User engagement may include following the number of downloads of a certain Google Chrome extension for your company’s offering as well as for competing entities. This can be accomplished by mapping out user engagement on different web stores or Click-Through Rates (CTRs) on competing websites. Companies can then work to utilize this information to increase their market share in terms of:
- Traffic
- Downloads
- User engagement / retention
Basing their decisions on qualitative user engagement-driven insights.
Top qualitative data collection methodologies
Qualitative data collection has long been understood and approached from an active standpoint, i.e., conducting interviews, organizing focus groups, and distributing questionnaires. This article argues for a more ‘passive’ approach to web data collection methods, including:
#1: Buying ready-to-use Datasets
Ready Datasets enable companies to forgo any of the tedious and time-consuming data collection processes in terms of gaining access to qualitative Datasets. All a company needs to do is decide which Dataset they need access to, say social sentiment regarding their brand on social media. And then, go ahead and request and then subsequently integrate those data points into internal company systems.
#2: Using an automated data collector
This means actively targeting qualitative data points with an autonomous data collector that requires zero code, no infrastructure, or any data specialists. Collectors can be customized, or companies can simply choose from a ready-to-go collector template that has been pre-adapted to target site architectures. Here’s what the ‘data collector menu’ looks like:
