Qualitative intelligence vs quantitative, and why it matters.
I am Johnson, one of the co-founders of Adaptive Pulse. Launching your startup is hard, but what is harder is to convince yourself to take that leap of leaving a promising and comfortable career and any safety nets behind to launch full-time on this new venture. Jen, the other co-founder, and CEO, also leaped into this venture full-time by leaving her career and maternity leave 5 months earlier, to better the world. In the end, we are embarking on an ambitious mission to empower businesses with Qualitative Intelligence to be more customer-centric and data-driven.
Oh, Jen is also my wife. And mother to our 14-month-old baby Karter, but that's another story for another day.
Let's start by defining the differences between Qualitative vs. Quantitative data.
Quantitative information or data is based on quantities obtained using a quantifiable measurement process. In contrast, Qualitative information records qualities that are descriptive, subjective, or difficult to measure.
We can think of Quantitative analytics tools like Mix Panel, KISSmetrics, and Amplitude which work mainly with the behavior side of customer data. It encompasses data that is measured in numerical value and can commonly and easily be quantified like clicks, page views, and demographical data. Quantitative data can tell you the who or the when, but it is also important to know the why or the what. That is why we focus on the qualitative data to enrich the quantitative.
From a qualitative perspective, it is more in line with research tools for deeper data analysis, including surfacing trending themes, key phrases, and sentiment analysis. There are few companies tackling customer feedback analysis and some are even using NLP to discover new ways to surface insights. These insights can surface the motivations and context behind the who and when you are analyzing.
We want to deliver advanced Qualitative Intelligence at scale and democratize Qualitative analysis by empowering businesses to be more customer-centric and data-driven.
By combining state-of-the-art advances in Natural Language Processing and Deep Learning, we enrich customer data, deep thematic analysis, and the ability to segment and cluster in real-time we hope to become the Qualitative Data Analyst businesses can rely on.
This analysis empowers teams to act on qualitative intelligence on their customer data at the right time with the right segments. Our users have a comprehensive understanding of why their customers act or react in a specific way and what they are trying to communicate with their business.
In the coming weeks, we will be writing more about Engineering, AI, ML, and NLP launching a startup in the Silicon Valley North, and doing all this while raising the sweetest and wildest baby. We hope to inspire a new way to utilize Qualitative data and to challenge outdated conventional data analytics.
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