Contents
When to use transactional data for predictive modeling?
Source: Capgemini 200801 0.09 0.08 0.07 0.06 0.05 0.04 0.03 0.02 0.01 0 Attrition Rate Data Quality 200802 200803 200804 200805 200806 200807 200808 200809 200810 200811 200812 200901 200902 200903 200904 200905 200906 200907 200908 200909 200910 200911 6 3.2. Exploratory Data Analysis
What are the different types of transactional data?
These can include count of transactions, total dollar amount of transactions, average dollar amount of transactions. If individual transactions had flag values associated with them, then an aggregate count of flag value occurrences might make sense.
When to use side gap in classification model?
And that side gap may be severe and you can look at minimizing this versus predicting class 0 and ground truth being 1. This is highly relevant for say cancer detection where the higher clssses may be more severe cases. For AUC, you need to compare by 2 classes and analyse accordingly.
How is product propensity index used in predictive modeling?
Product Propensity Index Customer Relationship Strategy Customer Lifetime Value (LTV) Behavioral Segmentation Attrition ■Estimate of customers future potential revenue based on historical behaviors, product purchase propensity and credit bureau behaviors
How are predictors used to predict customer behaviour?
You apply your model to the test set, which will predict the behaviour for customers given a set of measured predictors. The outcome can be a boolean flag (yes / no) or an occurrence probability (There is 85% likelihood the customer will churn).
How can machine learning be used to predict customer behaviour?
If however the company could tell precisely which customers are going to complain and when, it could avoid the management of a complaint by calling them pre-emptively to enquire about their satisfaction and offer them an incentive or a boon. This would obviously go a long way in terms of building customer delight.
Why are most customer behavior models not reliable?
Because of this necessity, most customer behavior models ignore so many pertinent factors that the predictions they generate are generally not very reliable. Many customer behavior models are based on an analysis of Recency, Frequency and Monetary Value (RFM).