Account volumes are increasing. Business is up.
Which is great! And…not so great.
Pandemic-era stimulus money is over, and credit usage is waaaaay up, along with interest rates, inflation, and delinquencies. Oh, and federal student loan payments are back.
So there are more delinquent accounts, but you don't have more resources to manage them.
The problem: You can’t afford to chase accounts that can’t afford to pay.
Why account prioritization is so important right now
A surge in volume can increase costs and strain resources.
But a solid account prioritization strategy lets you focus on the most valuable and, even more importantly in terms of resources, recoverable accounts.
By prioritizing accounts, you can allocate your resources more efficiently, ensuring efforts are concentrated where you have the highest potential for successful collections.
Prioritizing accounts also allows your team to adopt a more personalized approach when engaging with customers. By understanding the specific needs and circumstances of each account, you can tailor your communication strategies, fostering better relationships and, again, increasing the chances of recovery.
Common account prioritization models
You’ve probably based your current account prioritization strategy on one of the three most common models:
- Age-based prioritization: This model prioritizes accounts based on their age, focusing on the newest accounts first. The hope here is that newer accounts have a higher likelihood of recovery and keeps your business compliant with regulations.
The flip side is that focusing only on the age of accounts ignores the most important question for prioritization - how likely the customer is to repay the account.
- Balance-based prioritization: This model generally prioritizes accounts with larger balances. Here, your company is aiming to get the biggest recoveries with the least amount of resources (isn’t that always the goal?).
But as with age-based prioritization models, balance-based prioritization neglects the core issue of whether the account is likely to pay.
- Scoring models: These models assign a score to each account based on various factors such as payment history, credit scores or bureau reports, and account activity.
Businesses with the analytical resources to develop more sophisticated models often create their own mix of scores, which allows for more complexity.
The risk here is that the data that goes into these models is often limited - as in payment history or account activity - outdated - as in bureau data - or incomplete - all of the above.
What really matters in account prioritization
As you’ve seen, none of the primary existing methods for prioritizing your accounts gives you exactly what you need.
Because only one thing really matters when you’re choosing which accounts you want to prioritize and how to approach them - whether or not they’re going to pay.
And even the most sophisticated models haven’t been able to tell you that, because they rely on information that can’t accurately predict that. Even payment and account history or third-party data are educated guesses at best.
The best modeling data is going to have two attributes:
- It’s current
- It comes straight from the customer
After all, you have interactions with customers all the time - they tell you things like:
- What’s happening in their financial lives (e.g. getting a promotion or getting laid off)
- What other events impact their ability to pay (e.g. new baby, hospitalization)
- Whether they want to pay (e.g. “I’ll pay you next month”, “I’m not giving you a dime”)
Manually analyzing every customer conversation for this information is impossible, which is why models up until now haven’t incorporated fresh data straight from your customers like this.
But AI has opened up our ability to extract and analyze enormous amounts of data. And that means opening up better ways to predict the likelihood that accounts will pay, a tool that can - and we’re not exaggerating here - revolutionize your account prioritization strategy.
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