Qualitative and Quantitative Data

This blog series will talk about few principles (we are still drafting them) that drive what we are building at Strike. We launched private beta on the 24th of July and we have been gathering feedbacks from our beloved early customers and iterating on our product over the past 2 weeks. You can join the waitlist here: https://strike-app.ai/

This one’s about Qualitative and Quantitative data

As product builders (essentially everyone within a product team), we use myriad of tools and apps on a daily basis - 10+ based on some user researchs. As an example: Notion to write down ideas and document things, Slack to communicate, GA/Mixpanel/Plausible Analytics/others to understand users better through data-analytics, Figma for design collaboration, Linear for task management, the list goes on and on.

We can already see the ranges of data in the examples: text, image, analytics data and on and on. That’s a lot of ideas, states and know-hows spread over tools.

As a product builder, building products mean combining knowledge out of all(or most) of the tools, deriving an understanding and building assumptions/goals and painstakingly planning and testing them out. That’s genuinely a lot of cognitive overload. It’s understandable we fallback to our intuition when we try things as a product builders - it takes tons of times (1.8 hours +) on a daily basis to gather data and knowledge.

At Strike, We are building product analytics that aims to combine both qualitative and quantitative data to understand your product better by deriving insights and signals from your data and present you with stories that offer you opportunities to capture and/or address problems detected by Strike.

We are building ML models to analyze quantitative and qualitative data resulting in generation of metrics, insights and signals. We also cross compare results with industry benchmark we have been gathering over past 6 months. Our ontological model is designed to integrate and analyze both qualitative and quantitative data to enhance our product conversion funnels. We are drafting entities, attributes, relationships, and classes for the ontological model currently gathering feedback from our customers (and other sources).

We do not pass user data to LLMs - we are not another ChatGPT Wrapper! As we build further, our models will fit better with the product and market needs. The best way to test it out is through our private beta program. You can sign up here: https://strike-app.ai/sign-up