By Sarah Sejer
Product Owner

Three Challenges of Qualitative Research & How to Solve with Topic Tagging

Key Takeaways:

  1. Automated topic tags make qualitative research analysis faster and easier, and reduces time-to-insight.
  2. A tagging taxonomy containing universal tags streamlines qualitative data analysis within the organization.
  3. Keep tags accessible and searchable with simple wording to enable nonresearchers, as well as researchers, to analyze qualitative data. 

Topic tags that are automated, simple, and universal will enable productivity, scalability, and a consistent quality of insights.

Executive Summary

Collecting qualitative customer feedback is becoming a cornerstone for building outstanding customer experiences. However, the process is not without its challenges. Analyzing qualitative data is time-consuming, difficult to streamline, and most often requires trained researchers to ensure method-proof insights. In this blog we discuss how you can overcome these challenges with the right tagging approach.        

The wealth of detail in qualitative data makes it not only cumbersome to analyze, but also difficult to see patterns and ultimately produce unbiased and method-proof insights. Consequently, qualitative research risks not having the impact intended.    

 

In this blog we discuss the 3 main challenges preventing impactful qualitative analysis and how to overcome these challenges with the right tagging approach. 

Executive Summary

Collecting qualitative customer feedback is becoming a cornerstone for building outstanding customer experiences. However, the process is not without its challenges. Analyzing qualitative data is time-consuming, difficult to streamline, and most often requires trained researchers to ensure method-proof insights. In this blog we discuss how you can overcome these challenges with the right tagging approach.        

The wealth of detail in qualitative data makes it not only cumbersome to analyze, but also difficult to see patterns and ultimately produce unbiased and method-proof insights. Consequently, qualitative research risks not having the impact intended.    

 

In this blog we discuss the 3 main challenges preventing impactful qualitative analysis and how to overcome these challenges with the right tagging approach. 

What is so challenging about qualitative analysis, then?  

1. Analyzing qualitative data is time-consuming  

The level of detail in qualitative data makes the analysis process tedious, requiring hours spent on categorizing, organizing and systematizing through tags. Consequently, the findings risk having lost their relevance once they are finally obtained.

2. Qualitative data analysis is difficult to streamline

One of the main drawbacks of qualitative research is that the analysis process requires personal interaction with the data, which often makes collaboration and consistency around this type of research difficult. Without a shared ‘code-book’, insights derived from the data may end up in several different directions depending on who is analyzing the data.

   

3. Maintaining quality in qualitative analysis requires specialized knowledge which ultimately creates research bottlenecks within organizations

Because of its complexity, analysis of qualitative data requires thoughtful planning and experienced researchers to ensure that the obtained results are accurate. Ultimately, researchers in organizations risk not being able to keep up with the growing demand for qualitative insights.

What is so challenging about qualitative analysis, then?  

1. Analyzing qualitative data is time-consuming  

The level of detail in qualitative data makes the analysis process tedious, requiring hours spent on categorizing, organizing and systematizing through tags. Consequently, the findings risk having lost their relevance once they are finally obtained.

2. Qualitative data analysis is difficult to streamline

One of the main drawbacks of qualitative research is that the analysis process requires personal interaction with the data, which often makes collaboration and consistency around this type of research difficult. Without a shared ‘code-book’, insights derived from the data may end up in several different directions depending on who is analyzing the data.

   

3. Maintaining quality in qualitative analysis requires specialized knowledge which ultimately creates research bottlenecks within organizations

Because of its complexity, analysis of qualitative data requires thoughtful planning and experienced researchers to ensure that the obtained results are accurate. Ultimately, researchers in organizations risk not being able to keep up with the growing demand for qualitative insights.

The solution? Topic Tagging.

After years of conducting more than 6,000 qualitative interviews per year, we’ve recognized the solutions to overcome these challenges in topic tagging. 

The solution? Topic Tagging.

After years of conducting more than 6,000 qualitative interviews per year, we’ve recognized the solutions to overcome these challenges in topic tagging. 

Automate, automate, automate.

We believe in the value of automation. Not because we necessarily claim that AI can substitute or replace a human researcher, but because we believe in AI as a way of assisting with the ‘heavy lifting’ of UX analysis and delivering a faster and easier way of getting from raw data to actionable insight

Automated topic tags represent a key pillar of this, and reduces time spent on labelling and classifying each individual nugget, allowing you to get a head-start on identifying trends and themes across your data.

Automate, automate, automate.

We believe in the value of automation. Not because we necessarily claim that AI can substitute or replace a human researcher, but because we believe in AI as a way of assisting with the ‘heavy lifting’ of UX analysis and delivering a faster and easier way of getting from raw data to actionable insight

Automated topic tags represent a key pillar of this, and reduces time spent on labelling and classifying each individual nugget, allowing you to get a head-start on identifying trends and themes across your data.

Use universal tags to the extent possible. 

A strong taxonomy relies as much as possible on universal tags to foster standardized and automated methods for data analysis. Tags should be used as meta-data to help signpost, locate, and organize quotes. A taxonomy containing a vast number of project-specific tags can lead to lots of conceptual overlap, synonyms, and ultimately make it harder (not easier!) to find signals from the noise. 

Use universal tags to the extent possible. 

A strong taxonomy relies as much as possible on universal tags to foster standardized and automated methods for data analysis. Tags should be used as meta-data to help signpost, locate, and organize quotes. A taxonomy containing a vast number of project-specific tags can lead to lots of conceptual overlap, synonyms, and ultimately make it harder (not easier!) to find signals from the noise. 

Keep it simple  

Tags should consist of simple wording to make them accessible and searchable to multiple colleagues and stakeholders. In addition, we recommend working with a smaller set of carefully considered broader tags. A ‘long tail’ of tags increases risks, both in terms of missing golden nuggets (e.g. that are tagged using a variation/synonym of the topic tag that your search is based on) and decreasing consistency, which makes your taxonomy less resilient when being considered in the context of multiple studies and stakeholders.  

Ultimately, by taking the complexity out of qualitative analysis you open up the possibility of expanding research activities to non-researcher roles and thereby tackle bottleneck challenges.   

Keep it simple  

Tags should consist of simple wording to make them accessible and searchable to multiple colleagues and stakeholders. In addition, we recommend working with a smaller set of carefully considered broader tags. A ‘long tail’ of tags increases risks, both in terms of missing golden nuggets (e.g. that are tagged using a variation/synonym of the topic tag that your search is based on) and decreasing consistency, which makes your taxonomy less resilient when being considered in the context of multiple studies and stakeholders.  

Ultimately, by taking the complexity out of qualitative analysis you open up the possibility of expanding research activities to non-researcher roles and thereby tackle bottleneck challenges.   

How can we help at UserTribe? 

Based on the components of automation, universality, and simplicity, UserTribe has developed a tagging tool that eases the complexity of insights generation with qualitative data. Our tool aims to deliver value in 3 key ways:

Productivity

When processing your qualitative data through the UserTribe Insights Hub, you can view auto-assigned tags in real time as you highlight quotes and observations within the Insights Hub video player transcript. By leveraging automation and removing from your plate the process of creating a taxonomy from scratch and adding tags manually to your research, we have made it faster and easier for you to gain access to actionable insights.    

And – the really great thing about our solution is that our machine learning models are trained on enormous amounts of historical data.

Consistency / quality 

Our tagging tool structures topics into three types: General, study type and project specific. While the first two categories consist of pre-defined tags reusable across projects, the last category provides you with the flexibility to expand and develop your own project specific taxonomy.    

With our solution drawing heavily on pre-defined universal tags (with the flexibility to add your own of course), filtering and finding insights becomes easier, and ultimately improves the quality of your output by ensuring that everyone on the team is speaking the same language.

Scalability

Researchers find themselves in a situation today where their service is in high demand. In response to this development the concept of ‘democratizing’ research has formed, which is the process of making research both easily accessible and digestible to your non-research colleagues, as well as empowering them to conduct research themselves.   

At UserTribe we want to ride the wave of enabling scalability through cross-functional reach with a simple, pre-organized, and automated tagging framework.

Ultimately, the UserTribe Insights Hub is a gateway for you to fully exploit the potential of rich qualitative data. Feel free to contact us if you find this interesting or have any questions.

< Go to blog overview



How can we help at UserTribe? 

Based on the components of automation, universality, and simplicity, UserTribe has developed a tagging tool that eases the complexity of insights generation with qualitative data. Our tool aims to deliver value in 3 key ways:

Productivity

When processing your qualitative data through the UserTribe Insights Hub, you can view auto-assigned tags in real time as you highlight quotes and observations within the Insights Hub video player transcript. By leveraging automation and removing from your plate the process of creating a taxonomy from scratch and adding tags manually to your research, we have made it faster and easier for you to gain access to actionable insights.    

And – the really great thing about our solution is that our machine learning models are trained on enormous amounts of historical data.

Consistency / quality 

Our tagging tool structures topics into three types: General, study type and project specific. While the first two categories consist of pre-defined tags reusable across projects, the last category provides you with the flexibility to expand and develop your own project specific taxonomy.    

With our solution drawing heavily on pre-defined universal tags (with the flexibility to add your own of course), filtering and finding insights becomes easier, and ultimately improves the quality of your output by ensuring that everyone on the team is speaking the same language.

Scalability

Researchers find themselves in a situation today where their service is in high demand. In response to this development the concept of ‘democratizing’ research has formed, which is the process of making research both easily accessible and digestible to your non-research colleagues, as well as empowering them to conduct research themselves.   

At UserTribe we want to ride the wave of enabling scalability through cross-functional reach with a simple, pre-organized, and automated tagging framework.

Ultimately, the UserTribe Insights Hub is a gateway for you to fully exploit the potential of rich qualitative data. Feel free to contact us if you find this interesting or have any questions.

< Go to blog overview



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