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Our lives are constantly changing and marked by life events. This is part of life and is often what connects us. Some events are happy like graduation from college, a new addition to the family or traveling to a great destination. Some are difficult like moving home and others may be traumatic like illness, death or divorce. Each life event has associated emotions and often carries immediate and future financial needs.
Gone are the days when customers remain loyal to a single financial institution. Many financial institutions don’t even expect to meet their customers face to face. If they are to remain relevant, it is imperative that they detect, classify, understand and predict which events a customer is about to undergo or has recently undergone. That ability enhances the organisation’s view of the customer. It enables a deeper understanding of their needs, complements the product and advisory capability of the institution, and improves the relevance of next-best offers and conversations.
In fact it is increasingly the expectation of customers that the company they do business with will understand their journey, needs, and preferences.
One of the way to identify life’s events is to look at the customers behavioural pattern and infer events from them. This analysis was performed to identify significant variations in the customer’s spending patterns.
The visualisation shows the significant change in customer’s average weekly spending. Each line on the graph represents a customer’s spending time series. The spike or fall in the weekly spending can potentially signify a life event, such as possible school fees, likely new dependents, and significant purchases. Such variations can also be used as one of the features in the propensity model to predict possible cases of defaulting on payments.
Teradata Aster Analytics was used to integrate and process rich transactional accounts and credit card spending data for 2016. The Change Point Detection Function (CPD) was used to examine customers’ spending. CPD functions detect the change points in a stochastic process or time series. Most customers have 1 to 6 points of significant changes. The number of significant changes ranges from 1 to 10 and is represented left to right in the graph. Far fewer have more than 8 points, the tail of the graph.
For the purpose of the art visualization, the data was smoothed with the LOESS method and the visualisation was produced by the plot stream package in R studio.
The Art of Analytics
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