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Data analysis in Qualitative research

Data analysis can tell you what is happening, but it won't tell you why. So qualitative approaches are a must to bring together the what and the why!
data analytics

An enticing thought advancing towards AI, ML, and predictive analytics is that more data is better. Why wouldn’t it be? As data is the new fuel of the upcoming digital economy, the quantity of data matters. But does quantity ensure quality data? How does it impact companies? 

Every organization uses data widely today. Although not all data is used for analytics; analytics cannot be conducted without data. The technologies required in data analysis exist in a wide range. It helps to explain the varied use — by organizations and vendors. 

Qualitative data is typically more tedious to analyze. It is under the domain of human analysts due to the high level of contextual understanding and social intelligence. Qualitative methods include interviewing groups of people, holding focus groups, and conducting in-depth observations. 

Here are some aspects of Data Analytics that you must consider:

Data Analytics for Marketers

In the last decade, data analytics has revolutionized the marketing industry. For marketers, big data is a weapon that gives businesses a competitive edge in modern markets. Consumers are willing to pay for personalized goods and services. But, companies keeping a track of all customers’ online activities can be a bit challenging. So, data helps with detailed insights on consumer behavior and can assist from consumer’s initial interest to final purchase. 

Marketing Analytics given details lead to proper planning of expenditures in the right platforms. They increased speed and improve the execution of marketing campaigns. By using data, marketers can foresee what customers want and build proactive customer experiences. Marketers would make appropriate investments in profitable channels. In this way, they value their customer as humans and not just any other number on a spreadsheet.

Data Analytics for Businesses

Strategic thinking and planning are essential for growing your business, especially during difficult times. With the introduction of data-driven decision-making, businesses have increasingly turned to data analytics to assist in decision-making. 

Entrepreneurs and business leaders should capitalize on the shift from traditional to modern methods by providing their analysts with the tools they need. These tools will aid in uncovering insights and designing an up-to-date process that will propel the company forward. Identify your objectives, and use that as a foundation, determining the data you need to collect and analyze. 

Create a data-driven culture in your organization by ensuring that all data is analyzed, understood, and implemented in the decision-making process. A solid data strategy is essential for effecting meaningful change in your organization.

To provide in-depth insights, start with hypotheses and inferences, then add contextual data to make it meaningful. Making good data-driven decisions requires a combination of qualitative and quantitative analysis, as well as data to back up your claims and conclusions. It’s crucial to stay up to date on the latest innovations that can help you outperform your competitors. The team in charge of this task is your business analytics team.

Understanding the “Why”

Let’s start with an example. Businesses can use social media analytics to pick out influencers for customer engagement segments. But the tricky part is understanding why the particular influencer draws the customers in the first place. And this needs diagnostics of social behaviors.  

Organizations experiment with contemporary ways to understand their stakeholders better.

By involving your potential customers in qualitative research such as interviews, focus groups, and observation, you can diagnose the “why”.

Personalization and optimization of the services can bring in the best out of your customer service.

Understanding the “Big Data”

For decades, the term “big data” has been used to describe data characterized by high volume, high velocity, huge variety, and other extreme conditions. Especially with the evolution of Internet use and computing power, data traffic provides a rich source of insights to improve decisions. But it also creates challenges for organizations in how they store, manage, and analyze big data.

It’s not a secret anymore that big data drives business intelligence. But again, professionals struggle to handle big data with its dynamic nature of multiple audio, video, and content. They need insights from advanced analytics. The broad data approach enables data analytics and synergy across a range of small and large data sources — both highly organized, hugely quantitative (structured) data and qualitative (unstructured) data. The small-data approach generates valuable insights using multiple analytical methods only with less data.

Qualitative data analysis

Qualitative data reveals how your customers feel and what they expect from you. Analysts achieve solutions to problems by examining your customer’s experience (CX) and placing the customer at the center of everything. However, to extract meaningful insights, the data analysis should be efficacious. You’ll need the right qualitative data analysis tools for this.

Determine the best data collection methods and frame the questions in a neutral format. To have optimal results, once you are done analyzing the data, you have to think of how to portray it with persuasive storytelling.


According to Gartner, 70% of organizations will shift their focus from big data to small. Big data will leverage available data, either by reducing the required volume or extracting more value from unstructured, diverse data sources.

There is a wide range of qualitative data analysis software available for meeting various requirements of the customers. Even so, to analyze your data, many of these tools require you to manually code it. SO, why wait to get started?

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