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Tip #1 – covers knowing the outcomes you wish to predict, as well as the questions you want to ask based off your current findings from your historical analytical data. Let’s say when analyzing your sales and order data you have noticed a number of orders that have been cancelled. Further investigation shows those cancelled orders have gone unfulfilled for a specific period of time since being placed. This may be due to the sheer volume of orders and the number employees you have available to process them. You may want to get a better handle on understanding the pattern of cancellation and create some predictions to answer questions like:
Will a customer cancel their order before it is fulfilled? - Or -
How many days pass before a customer cancels their unfulfilled order?
Answering these questions will enable you to be proactive with certain customer orders and possibly direct your employees to give those order priority and fulfill them sooner to avoid cancellation. Now that we have our predictive question, we need to architect a data set that will support it so Qlik AutoML can use it effectively. Stay tuned for Tip #2 where I will cover the data you should be using to train machine learning models with Qlik AutoML.
