redit lenders use data analytics to assess potential clients and determine affordability. However, many credit lenders and debt collection companies fail to apply the same practice when dealing with defaulting clients. In my first blog post, I’ll cover the important role that data analytics can play in collections operations and solutions.
Let’s think Big. Let’s think Big Data.
The world’s greatest innovators asked big “What If” questions and answered them in an even bigger way:
- WHAT IF bicycles held the secret to human flight? – Wilbur and Orville Wright Story
- WHAT IF a single car could change the greatest spectacle in racing? – Dan Gurney Story
- WHAT IF everyone could have their own personal computer? – Apple Computer Story
Since the start of the big “data” bang, the utilisation of data within the credit risk environment has played a pivotal role in understanding our customers’ behaviour and financial means. As a result of the National Credit Act 34 of 2005 (the “NCA”) and the National Credit Amendment Act 19 of 2014 (“Amendment Act”), a mandatory obligation was put on credit providers to assess lenders’ affordability. Assess equals Analyse.
We learned that all customers are unique and therefore need to be treated differently (different affordability) and we utilise both external, as well as internal, data to decide how we “treat” our customers.
Have we lost this practice of assessing our customers when they enter the collections lifecycle?
Collections operations have historically revolved around quantity, as oppose to quality. What do we mean with quantity vs. quality?
- Working as many matters as possible during a day
- Making as many calls as possible during a day
- Getting the highest repayment arrangement possible
But what if we could increase operational effectiveness AND financial returns? What if we utilise our data within collections and recoveries to drive our strategy and efficiency?
Let’s ask ourselves:
- Are all the debtors the same? No!
- Are the debtors still in the same position as when they applied for credit? No!
So, why do we still treat all debtors the same? Why do we treat them based on their original “position”? And do we take into consideration the debtor’s history with us when treating them?
And if we analyse our debtors:
- Do we continuously deploy analytical advancements?
- How do we link analytics to a true step change in performance outcomes?
- Do we make the tough decisions in backing our analytics, such as excluding an indicated portion of our portfolio, if the analytics tells us it would be unprofitable to pursue?
- Do we truly champion / challenge ourselves regularly?
- Does analytics go beyond credit strategies and also support dialler strategies?
- Is our analytics the foundation of “real time” campaign management?
What is the benefit of advancing analytics from static analytics to dynamic machine learning? (But more of this in our blog post on How to build a Data-Driven Collections Strategy).
What is collection analytics?
It’s the process of applying data analytics to your customer (debtor) data to better understand behaviour and characteristics to enable your business to maximise your collection yields from each debtor.
How can data analytics help collections operations?
As all debtors are not the same, so is no credit provider or collector the same. For that reason it is vital to analyse (assess) debtors, utilising both internal and external data.
Understanding your data, means understanding your strategy and empowering your staff.
“Without big data analytics, companies are blind and deaf, wandering out onto the Web like deer on a freeway.” Geoffrey Moore, Author of “Crossing the Chasm” and “Inside the Tornado”
What is the expected step change in performance we observe by introducing analytics?
Can you do your own debtor analytics without costly analytical resources or software? YES! Learn how in my blog post How to build a Data-Driven Collections Strategy.