Financial institutions use big data to gain a deeper understanding of their customers: their financial history, preferences, and behaviour. In a digital-first world, data acts as the footprints in the snow—showing who did what, and when. This is precisely why data is at the heart of 21st-century finance:
- An estimated 1.145 trillion Megabyte (MB) per day of data is created globally
- The Big Data Analytics in the banking market is expected to grow at a Compound Annual Growth Rate (CAGR) of roughly 22.97% from now until 2026
- According to Accenture, the growth of data in banking is “unstoppable”
In this blog post, we briefly touch upon the main types of data before examining one specific use case: how it helps you optimise your dunning process and maximise your debt recovery goals.
Types of data
Let’s briefly examine the two main types of data: categorical (qualitative) and numerical (quantitative).
- Categorical (qualitative): Categorical data refers to any instances where data can be ordered and put into categories. There are two main types of categorical data: nominal and ordinal.
Nominal data is when there is no natural order between the categories—they are different yet equal. For example, gender, eye colour, religious beliefs, and communication channels are all nominal data. Ordinal data is a bit different—it can be put into an order. Exam grades (A, B or C), educational level, and socio-economic status are good examples of ordinal data.
- Numerical (quantitative): Numerical data relates to data that’s put into numbers, not categories. There are two types of numerical data: discrete and continuous.
With discrete, the data has to take certain specific values—age would be a good example. In collections, the number of instalment plans scheduled and the amount of money owed by customers is another example of discrete data. On the other hand, continuous data can take any value on a continuous scale (like email open rate or repayment rate).
Why is data so important for your debt collection department?
Collections departments need to know their customers in as much detail as possible. This enables them to work out how customers are behaving and, just as importantly, how their department can guide them through the collections process. There are 3 specific benefits to using data in collections.
1. Understand your customers
Every past-due customer thinks and behaves differently. This is why one-size-fits-all collections approaches don’t work as well as tailored strategies. Customers like to be treated as an individual—and this is where data comes in.
Data reveals what customers like and dislike: the messaging they engage with, the channels they use, types of debts they have and whether they were able to use your repayment landing page successfully. Harness your customers’ data before analysing it to find out key insights. For example, if you know in advance from big data that a few customers have different types of loans at the same time, you can customise your messaging and even help them prioritise their debts for repayment.
Once you have these insights, you can segment customers, implement different strategies and create tailored messaging. By serving each segment with the strategy that will work best, you can optimise your dunning approach going forward.
2. Track performance
Thanks to data, you can keep an eye on your collections performance in real-time. Instantly see how well your messages are working by analysing your click-through rate. If customers aren’t clicking through to your repayment landing page, you might need to send out another message with a slightly different tone or even switch to a different communication channel.
Or if the landing page click-through rate is high but the overall repayment rate is low, you can then check other data such as ‘Payment attempted’, ‘Pay Later Clicked’ and ‘Callback Requested’ to examine which processes need further improvement.
3. Improve your debt collection process
Once you understand your customers in greater detail, and you track your performance on an ongoing basis, you’ll be able to optimise your debt collection process. With a clear overview of your customers’ data, it’s easy to notice where further adjustments and changes to your dunning process are needed. For instance, if you notice that digital communication usage and repayment rates are increasing dramatically, you know that it’s time to replace traditional dunning manual operations with a more modern debt collection approach.
Top data management tips for collections teams
We’ve examined which types of data are important and outlined how data can improve your collections approach. Now, we’re going to dig a little deeper, providing three key tips to help you effectively harness, analyse, and use your customer data at scale.
1. Adopt a centralised data management platform
Silos—when data is spread in different parts throughout a company—prevent organisations from generating the full value from their data. They can’t see a unified picture of their customer. Instead, they only see small snippets or scattered information here and there .
With a centralised data management platform, however, all collections-required data is stored in one single place. Agents can see a complete view of their customers’ preferences, behaviour, financial context and types of debts they have.
Therefore, it’s essential that all debt collection departments use an all-in-one collections and recovery platform. It greatly reduces employees’ time and effort digging into various systems when looking for further information.
2. Train your employees and promote data hygiene
Data on its own won’t improve your collections strategy. To create as much value as possible from your customers’ data, you need to know what to look for, how to store it, how to analyse it, and how to put these insights into action. This is fairly easy to do—but it still requires training.
You also need to make sure that your employees are aware of all necessary privacy regulations (like the General Data Protection Regulation (GDPR)). This will ensure they don’t make any costly mistakes that may lead to fines or even lawsuits.
The right collections management software providers display all necessary data and present it in a visual dashboard, making data analysis as straightforward as possible. Better still, the software has been created while keeping the latest data privacy and security considerations in mind.
3. Designate certain team members to handle sensitive data
Personal data—especially financial and demographic data—is highly sensitive. Therefore, it should be shared with as few people as possible. That’s why you should consider designating certain team members to deal with core information. Don’t automatically share your customers’ data with everybody in your organisation. Instead, work with a collections management software that allows you to easily control who can see which piece of data and when.
Pick the right tool for your debt business
Big data is the future of finance. Consumers are creating more and more data with every passing day, so your collections department needs to implement the right tools as quickly as possible. This will ensure that they can store, analyse, and put this data to good use—while avoiding any costly privacy violations.
Ready to get started? Feel free to contact us to learn more about our next generation data management capabilities for your collections business.