In this chapter, our final agile sprint, we will translate predictions into an action by diving further into what makes an email likely to elicit a response or not and turning this into an interactive feature. We’ll learn to suggest changes to email authors that will make their emails better.
Up to now we’ve created charts associated with entities we’ve extracted from our emails, entity pages that link together to form reports for interactive exploration of our data, and we’ve calculated probabilities that help us reason about the future to tell whether we can likely expect a response to an email or not.
This poses an opportunity to dig further. We’ve found a lever with which we can drive actions: predicting whether an email will receive a response or not. If we can increase the odds of a response, we’ve enabled a valuable action.
With driving actions - the improvement of emails - we have arrived at the final stage of the data-value stack: enabling new actions.
Code examples for this chapter are available at https://github.com/rjurney/Agile_Data_Code/tree/master/ch11. Clone the repository and follow along!
git clone https://github.com/rjurney/Agile_Data_Code.git
We’ve already seen that the time of day has a large effect on email - people send more emails at certain times ...