We end off every year with a roundup of our most popular blogs – the blogs that you should not miss out on! This year is no different, and we’ll be putting together four roundups this year, one for each of our main topics.
2018 has been a great year in collections and recoveries, and we wrote a multitude of blogs on a variety of related topics. In this blog, we list the most popular blogs within the overarching Collections and Recoveries topic on our website. If you haven’t yet read these blogs, be sure to read them before the end of the year!
5 Most Popular Collections and Recoveries Blogs
Results from a recent survey conducted in the EU, suggests that while looking for a new solution to support operations, customer centres looking to lead the charge should select a system that is flexible enough to accommodate both traditional and new channels to create a truly multi-channel contact centre environment.
What does this mean for improving collections in an environment that seems to be deteriorating more and more?
South African credit providers are heavily reliant on debit orders as a payment channel. There are two broad types of debit orders that we have: standard debit orders, and early debit orders. The early debit orders are further broken down into two types – being Non-Authenticated Early Debit Orders (NAEDO) and Authenticated Early Debit Orders (AEDO).
The difference between the two broad types is really when the debit order is processed during the day. Standard debit orders are processed during the late ‘window’, so sometime during the afternoon, and early debit orders are processed very early in the morning. There is the added enhancement of early debit orders being able to track the customer’s account for available funds for up to 32 days.
So as a credit provider why is it important to now care a lot more as to how these two types of debit orders perform? Well, what if you weren’t allowed to use the early ‘window’ anymore?
Machine learning, for all its cool applications, is at its core the generation of predictive models using advanced algorithms that learn from data. If we have enough reliable and stable data to feed it, we can build models and make predictions on just about anything. If you are new to machine learning, read more on What is Machine Learning?
In a collections operation, having enough data shouldn’t be a problem. But knowing how to use the data at your disposal to increase your collection yields is the challenge. In this blog, we discuss how machine learning can help improve your debt collection process.
When looking at the collections cascade and its diminishing returns, we always emphasise the responsibility factor relating to each metric. Who is responsible for these areas, and how do we monitor the accuracy of these metric results?
Meet Vusi, a call centre agent, with call centre agent struggles. He needs to keep his customers engaged while he shifts between various systems to find the information he needs to meet his manager’s expectations of consistent, successful call outcomes. And he never really knows how he is performing against his targets…
With Principa’s Agent X call centre virtual assistant, those struggles are a thing of the past.