This article was based on the interview with Daniel Erickson of Viable by Greg Kihlström for The Agile Brand with Greg Kihlström podcast. Listen to the original episode here:
The value of qualitative customer data cannot be overstated in today’s business landscape. In the age of big data and advanced analytics, companies have access to an abundance of quantitative data that provides insights into customer behavior and preferences. However, quantitative data alone cannot provide a complete understanding of the customer experience. This is where qualitative customer data comes in.
Qualitative data refers to unstructured feedback that customers provide through various channels, such as open-ended survey responses, app store reviews, social media mentions, help desk tickets, and sales call transcripts. Unlike quantitative data, which tells you what customers are doing, qualitative data helps you understand why they are doing it. It provides valuable insights into the motivations, emotions, and experiences that drive customer behavior.
The relationship between quantitative and qualitative customer feedback is complementary. While quantitative data can provide a broad overview of customer trends and patterns, qualitative data adds depth and context to these insights. By marrying the two types of data, companies can gain a comprehensive understanding of what customers think about their products or services.
One common oversight when it comes to utilizing qualitative customer data is neglecting to collect and analyze feedback from multiple sources. Companies often focus on collecting data from obvious channels, such as surveys or app store reviews, but fail to tap into other sources like help desk tickets, call transcripts, or in-app feedback. Each of these sources provides unique perspectives and can uncover valuable insights that may go unnoticed otherwise.
Another challenge is the sheer volume of qualitative data that companies receive. Managing and making sense of this data can be overwhelming without the right tools and processes in place. This is where AI analytics tools like Viable come into play. These tools can aggregate customer feedback from multiple sources, organize it, and automatically analyze it using AI generative analysis. By automating the analysis process, companies can save time and resources while extracting valuable insights from qualitative data.
The value of qualitative customer data lies in its ability to uncover hidden patterns, identify pain points, and highlight areas for improvement. It provides a more holistic view of the customer experience, allowing companies to make data-driven decisions and tailor their products or services to meet customer needs. Qualitative data can also be used to identify trends, spot emerging issues, and predict future customer behavior.
Furthermore, qualitative data can be instrumental in developing customer personas and understanding different customer segments. By analyzing qualitative feedback, companies can gain insights into the preferences, motivations, and pain points of specific customer groups. This information can then be used to personalize marketing messages, improve customer service, and create targeted experiences that resonate with different customer segments.
The value of qualitative customer data cannot be underestimated in today’s data-driven business environment. While quantitative data provides valuable insights into customer behavior, qualitative data adds depth, context, and understanding to these insights. By leveraging tools like artificial intelligence (AI) analytics, companies can effectively collect, analyze, and utilize qualitative data to drive customer-centric decision-making and create a truly agile brand.
