1. Data access
To build accurate, up-to-date, comparable scores on customers, you need access to the right data, in sufficient quantities, and from all the right places. Some of that data is going to come from your own systems, showing buying patterns, payment histories, customer satisfaction reports, and so on. But that is only part of the picture needed to develop a customer score. It is vital that you also tap into reputable third-party sources. For instance, at Allianz Trade, we grade companies around the world by tracking and enriching many thousands of data sources, as well as engaging directly with buyers themselves as part of our core trade credit insurance business. The need for independent third-party guidance is especially important when you are trying to allocate a score to first-time buyers or those from countries or regions unfamiliar to your organization – a situation that’s particularly common in e-commerce settings.
2. Scoring criteria
A rounded picture that informs a customer score needs to be based on a broad mix of attributes. Those might include a customer’s bank information, debt history, and financials. But it might also consider sector- and country-specific risks they face, details of their trading patterns with other companies, and much more besides. And that takes expertise – both international and local.
3. Scoring model development
Customer scoring models are developed to support different decision points. By weighting criteria, they can be designed to, for example, rank the most loyal and the most problematic customers, the best payers and the companies that are always late, the companies where after-sales service is easy, and those that are high-maintenance. A simple example is the Recency, Frequency, Monetary (RFM) model. This allocates a score of 1 to 5 to each of the three attributes, based on a customer’s buying patterns, to produce an overall value. A model for a credit risk score, on the other hand, might include up to 15 variables.
4. Scoring and segmentation
By assigning each a score, customers can be segmented into groups that reflect their current situation and help you predict their behavior. That might, for example, show you the level of risk your organization faces when selling to a specific customer and so influence the credit terms you deem appropriate.
5. Decision making and action
With a score flagged up for each customer, decision-makers can act with new levels of speed and confidence. And when informed by insight from third-party experts, that decision-making can drive low-risk sales growth among both existing and new buyers.