Apply Smart Customer Segmentation to Increase Cash Flow & Collaboration
Believe it or not, there are still credit professionals whose idea of portfolio management is a spreadsheet of aging receivables. That method provides limited insight – while accounts receivable data shows which accounts are paying on time and which pay late, it takes further refinement and analysis to understand the underlying trends in customer payment behavior.
It’s important to understand and manage your risk exposure across the entire portfolio – not just individual accounts. One way to practice portfolio management and understand risk holistically is through customer segmentation analysis.
Customer segmentation involves categorizing the portfolio by industry, location, revenue, account size, and number of employees to reveal where risk and opportunity live within the portfolio. Those patterns can then provide key measurable data points for more predictive credit risk management. Taking a portfolio approach to risk management gives credit professionals a better fix on the accounts, in order to develop strategies for better serving segments that present the best opportunities. Not only that, you can work to maximize performance in all customer segments, even seemingly risky segments.
Customer segmentation analysis can lead to several tangible improvements in credit risk management: reduced days sales outstanding (DSO) and bad debt, stronger credit policies, and improved internal communication and cooperation across teams. Here’s how:
Successful Segmentation Strategy: Five Critical Elements
- Proactively perform account reviews and prioritize collections to optimize cash flow and reduce DSO.
- Build a collaborative relationship with sales and marketing to grow the business.
- Strategic adjustments to credit and collections policies that can increase cash flow.
- Manage and validate the bad-debt reserve to maximize profitability.
- Share insights internally for improved risk management.
Deteriorating customer credit levels can be a challenge if too much time is spent on credit holds. Conventional wisdom says that the longer it takes customers to pay, the less likely they will pay at all. Late payments have a negative impact on cash flow. So, from a collections perspective, improving DSO is a priority. On the credit side, assessing risk trends and predicting the likelihood of late payment from customers within certain segments is a priority.
Credit managers typically segment risk by sales channels, with different risk profiles in each segment depending on the risk tolerance of the business. When evaluating creditworthiness, it’s helpful to segment by high, medium, and low risk by combining your data with a third-party provider to understand the likelihood of a delinquent payment. Segmenting those customers who take the longest to pay or owe the most allows you to target the riskiest as well as the lowest-risk customers. Devising segments for industries that show a greater risk/distress propensity should also help with gaining efficiencies across the business. Don’t forget, should you uncover underutilized credit lines, upsell and cross-sell opportunities can also arise during this process.
Sales teams who are responsible for a particular portfolio segment are inherently interested its makeup. What anomalies present themselves? What behavior trends? Which accounts have looser credit limits and therefore are low-risk targets? Sharing these insights and collaborating with sales helps to build what’s probably the most critical cross-functional relationship for driving growth.
For the marketer, assigning key data points such as credit scores to each customer is critical, because observed, anomalous customer behaviors naturally prompt direct questions. For instance, marketing may consider focusing on larger businesses as a credit opportunity. However, segmentation analysis shows many of those large businesses are bleeding cash. This leads to important questions: What are you under- or over-utilizing in this segment? Why are you focused on big accounts? Why is this company so vulnerable to risk?
When customer segmentation is based on average days to pay, you may see one set of accounts paying 31 to 60 days after invoicing. Then you may see a considerable number pay 61 to 90 days after invoicing. Segmentation could reveal that those late payers are your largest customers, and large companies tend to pay more slowly and try to dictate payment terms.
This works equally well for assessing risk among vendors. For example, by segmenting vendors by industry, you might find that software providers consistently pay late because they wait for systems to be fully implemented before delivering payment. Segmentation allows you to share this insight with your accounts payable counterparts. In another scenario, a company may have a decent number of strategic accounts it deems essential. They’re big, so they can often dictate terms.
Imagine you have a high-risk customer – let’s say a retailer. The macro view showcases how the current nature of the retail industry generally presents more risk. However, this particular retailer is thriving with e-commerce. You can, therefore, propose that this retailer is not as risky as other retailers who are still dependent on brick-and-mortar sales.
With segmentation, you can better understand payment behaviors of customers and vendors by identifying behavior patterns. In turn, your company’s credit policies and collections policies may need to be amended to best manage risk for certain segments to improve outcomes.
If your company uses a conventional bad-debt provisioning process where 10% on all accounts that pay late is reserved, this doesn’t necessarily account for deeper historical behavior. Simply reserving that much currency can negatively impact expenses, especially when only certain customers – say your biggest – pay late only occasionally. Through segmentation, you can see that the dollars actually at risk could be considerably lower than when the surface data was examined. When you rely on risk-based analytics, you may conclude that such accounts are much more likely to present a very low risk. Therefore, you could be reserving 1%, not 10% as the traditional approach might dictate.
Risk by Bad Debt Reserve
Sharing the results of your labor is key to influencing change across the business. By socializing the data with management in other departments through customer segmentation models, the credit team can help others understand macro and micro views of the business. Leaders want actionable reports, so customization is critical. When segmentation produces a point of view that the company’s finance leader can act on, your segmentation strategy has paid off.
This article was originally published in 2017.