"Intelligence" not "analytics"

I have spent almost 12 years building tools and platforms that provided intelligence for customers. At first, the reports which customers received provided all the graphs and tables but provided little/no value. Customers didn't want to do analysis, so we ran the data through multiple layers to provide "intelligence". While analytics provided as the raw metrics, intelligence dives deeper where analytics is contextualised, prioritised, and connected to an action.

This is what I am trying to achieve as I build and evolve "Illumea" (https://illumea.ai). A chatbot might handle hundreds or thousands of conversations every month. Each conversation generates data.. analytics, which would say - "Answer rate dropped by 74%". You look at it every month, nod, and move on. The platform doesn't stop there. Every month (for now), customers can generate an intelligence and strategy report with actionable insights that would say - "The answer rate dropped to 74% because 23 visitors this month asked about enterprise pricing that doesn't exist on your website, and those 23 visitors came from your highest-spend ad campaign." Now that hits your ROI!

With the kind of technology we have these days, this might be easier to achieve. What is important is how you think of the word "intelligence" and what value it gives your customers. During almost every pitch, there is a question on this topic. Even the best answer might fail to convice since sample data or data that is not theirs doesn't speak to them. This is the most challenging part of such conversations - convincing customers on what they would get from their own real data and its value.

Do comment to let me know your thoughts on the subject and how all this can evolve and improve our decision-making and help customers.