Data professionals strongly advocate quantitative when it comes to qualitative and quantitative data analytics. It’s the numbers that business leaders most often tend to look for. But those numbers are just like soup without salt if they lack context. Have you ever wondered what if you add an element of qualitative figures to those numbers and how would they help in getting better customer insights?
Though often subjective, Qualitative Data is rich and consists of in-depth information. Recently several breakthroughs and software specifically designed for qualitative data management have been made. This has two-fold benefits:
- Greatly reduced technical sophistication
- Ease laborious tasks. Thus, making the process relatively easier
Wondering how to incorporate and integrate both the aspects — an amalgam of qualitative and quantitative data analytics into customer insights? Let’s dive into the ‘Whys’ and ‘Hows’ of combining both qualitative and quantitative analytics!
Organizations are collecting data in various capacities from both internal to external sources. To understand customer insights and behaviour comprehensively, businesses need to consider a balance between insights from qualitative and quantitative data analytics.
It’s a fact that collecting qualitative and quantitative data analytics cannot be overlooked when you drive efforts to maximise sales and improve customer experience, customer insights, and business growth. While numbers and ratings from researchers, customer insights, and even competitors are easy to analyze, businesses require insightful qualitative and quantitative data analytics to have a complete picture of their customer insights and demographics.
Let’s look at the challenges which businesses face while identifying relationships between Quantitative and Qualitative data analytics to decode customer insights and the status.
Using data mining techniques aimlessly doesn’t generate customer insights and may blur true relationships hidden in the data. Instead, knowledge of consumer behaviour should guide analysis by identifying important variables. This isn’t always easy to achieve, and that’s why below are some obvious yet recurring issues that you might encounter while conflating quantitative and qualitative data analytics practice.
Oftentimes, the legacy enterprise infrastructure for managing data may not be efficient. Traditional data warehouses often lead to disparate silos of data which may prevent you from getting a holistic overview of consumer insights and data. With information about customers now coming in from hundreds of places- from internal to external systems
Accumulating data doesn’t mean just assembling documents, sheets, etc. You should be able to connect different types of data, both structured and unstructured, in meaningful ways. Traditional data mining techniques might not enable you to extract and discover deeper and more subtle patterns in consumer insights and behaviour.
To be able to predict consumer behaviour in both qualitative and quantitative data analytics, your analytical builds should relay relevant facts and contextualized answers to speciﬁc questions, rather than a broad search result with lots of irrelevant information.
To reveal the macro relationships and dynamics in business, qualitative and quantitative data analytics is important and also to get a fundamental understanding of consumer insights. Traditional data modeling techniques may restrain you from identifying relationships at a macro level.
Quantitative and qualitative data analytics provides access to a huge range of content ready to use for customer insights, together with the tools to integrate proprietary data. The deeper the data, the more confidence you can have in your business decisions and customer insights. We at Rawcubes offer the industry’s most comprehensive market data, including real-time and historical data.
Rawcube’s quantitative and qualitative data analytics capabilities provide pre-defined or customized analytics calculations to customers as a fully managed service.