Generating insights for your organization is a lot like being a detective. There is a question to be answered (what happened, and why), a variety of data and analysis methods (the scene of the crime, witnesses, alibis, ballistics reports), and synthesization of the data to generate insights and determine a conclusion (whodunnit!). All with the timeliness and confidence demanded by key stakeholders and the system they are operating in.
Leveraging a variety of data for more meaningful insights
One of the big challenges facing insights leaders in their detective work is around the “variety of data” to incorporate. Often, there is both quantitative data (such as NPS scores, demographic data, revenue per customer) and qualitative data (open ended text responses, focus groups) available, but it can be difficult to pull these data sources together and make sense of it all. These data sets can live in silos across the organization, in different formats, and within different tools. But successfully harnessing both data types can enable insights leaders to build a more complete picture of what might be going on with customers or the market. By combining both the “what” and the “why” you can generate more meaningful insights to help stakeholders make strategic decisions tied to business impact.
The good news is, recent advancements in AI can help insights leaders tackle these varied data sets and bring them together for insights that are useful to the organization.
Tips for integrating qual + quant for better insights
Here are some tips to integrate these approaches:
Set Clear Goals: Define what you want to learn from your analysis. What questions are you trying to answer for your stakeholders? Are you trying to identify a new product or service that can generate more revenue? Do you want to address churn within a high value segment? Setting clear goals will help guide what data and approaches you should leverage, and ensure you deliver insights that matter to stakeholders.
Identify the right data sets: Depending on your goals, identify data sets that can help build the picture. Ideally you can include data that has business impact information (such as lifetime value (LTV) of customers or purchase history), experience data (such as NPS scores or churn risk), and customer needs / motivations (such as product feedback from focus groups or customer feedback from open-ended survey questions). You’ll also want to include data that can connect your customer across data sets, such as segment data or anonymous customer IDs. This will allow you to not only organize your insights but you’ll be able link up insights from different data sources for more powerful insights.
Leverage modern analysis tools: Advancements in AI can make combining and analyzing this data much easier and more reliable. Insights leaders have been using AI-powered tools to run advanced analytics, such as predictive modeling, over large quantitative data sets for a while now. And natural language processing (NLP), topic modeling and sentiment analysis have helped make sense of qualitative data at scale. Now, advancements in generative AI are making these methods faster and more accurate. They are making insights more powerful by transforming the data in a way that allows quantitative and qualitative to be combined.
For more tips on incorporating AI into your research and analysis, check out our latest guide here.
Learn more how Beehive AI can help you unlock insights your quantitative and qualitative data
Unlock the answers in your data, with the modern analysis platform built on generative AI. With self-learning language models, validated by human experts, and built-in statistical analysis, Beehive AI helps research and insights leaders quickly, accurately, and safely generate actionable insights from their quantitative and qualitative data.
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