The Data Analytics Blog

Our news and views relating to Data Analytics, Big Data, Machine Learning, and the world of Credit.

Predicting Customer Behaviour (PART 2)

In Part One of this two-part blog, we started providing a short overview of just some of the propensity models that Principa has developed. In this Part Two, we continue to look at different types of propensity models available across the customer engagement lifecycle that are used to predict behaviour and solve business problems. 

Predicting Customer Behaviour (PART 1)

Propensity modelling attempts to predict the likelihood that visitors, leads and customers will perform certain actions. It’s a statistical approach that accounts for all the independent and confounding variables that affects said behaviour. The propensity score, then, is the actual probability that the visitor, lead, or customer will perform a certain action.

Psychometrics in Credit Originations

If 2020 was not hit by the COVID-19 global pandemic, many were touting 2020 as the year of alternative data. In the credit assessment world, data has typically incorporated demographic data and credit bureau data (where available), but now we are seeing alternative data playing more of a role namely in cellular behavioural data and psychometrics.

The Retaining and Reviving of Customers

Maximise profitability by reducing the churn of profitable customers Genius Retain is an analytics-as-a-service offering that allows you to statistically predict the probability of your customer cancelling their account, identify which customers are worth retaining and how to retain or re-activate these profitable customers. [FIND OUT MORE ABOUT GENIUS RETAIN] The ability to retain and re-activate dormant customers is vital to sustaining a profitable business. Companies with a clear plan to retain and activate dormant customers with a strong analytical overlay, can have a successful and profitable outcome. We discuss the value of a customer and how to retain and revive them. What is the true value of losing a customer?

The Power of Prediction

 

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