How Scorecards Can Be Used To Determine Customer Loyalty

The right customers are important for every business. Marketing to and serving customers who are not profitable removes your focus from your best customers and ensures they remain loyal to your business. Scorecards can help you identify and focus on your ideal customers by ranking your customers by the common criteria historically shown to be shared by your best customers. 

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Using Data Analytics to Predict Churn

According to this article by Frederick F. Reichheld in the Harvard Business Review, the average company loses about half its customers in a five-year period. When customers see a loss of value, they churn. The ultimate goal of a great loyalty strategy is to increase the perception of your solution’s value in the eyes of your customer, exactly when you need to. To achieve that, you need to be able to predict churn, many in the industry turn to leading (or lagging) indicators to indicate where efforts need to be focused.

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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 behavioral data and psychometrics.

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Shift happens: Top tips on Scorecard re-alignments

Principa employs a variety of best-practice credit scorecard building techniques including mathematical programming, regression modelling, optimal segmentation-seek genetic algorithms and reject inference parceling, amongst others. Through our credit risk scorecards businesses can look to improving their credit risk decisioning by 5-30%.    

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10 ways of combating application fraud (part 1)

New technology gives the promise of greater enablement. But some of the shrewdest entrepreneurs understand that opportunity comes from the unintended consequences of new technology. So, let us take digitalisation of the loan application process: the opening of digital channels has enabled lenders to service their customers 24/7 and through APIs integrate with a host of sophisticated services. However, the advent of the digital channel has meant more opportunity for fraud.  The question to ask is:

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The time is NOW for model validation and adjustment.

One of the major premises used in credit scoring is that “the future is like the past”. It’s usually a rational assumption and gives us a reasonable platform on which to build scorecards whether they be application scorecards, behavioural scores, collection scores or financial models.  That is reasonable until something unprecedented comes along.  You can read about this black swan event in our previous two blogs here and here

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10 ways the COVID-19 crisis will affect your credit models (PART 1)

One of the basic principles of credit scoring and modelling is that the “future is like the past”.  Whilst robust credit models may be calibrated on multiple time periods, this assumes that trends in the past represent what is going on today.  COVID-19 is a black swan event – meaning in the modern day it really is unprecedented.  If you have never come across the term black swan, or if you have but no idea the origin, I recommend taking two minutes to read its really interesting etymology.

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