Tip: Increase Efficiency and Effectiveness With a Decision Matrix

Many credit managers err by trying to treat all customers and transactions the same. While running all transactions through the same administrative process may appear to be a simple, straightforward solution, it is in fact a serious blunder in terms of efficiency, risk mitigation, and cost.

Most A/R portfolios contain both apples and oranges in terms of customer and transaction types. Treating everyone the same will result in the accounts at both ends of the spectrum getting more or less attention than they deserve, and that will negatively impact your decision process and ultimately your performance.

Applying the 80/20 Rule

The key to addressing this situation requires paying homage to the 80/20 rule. Because 80 percent of your firm's revenues come from 20 percent of your customers (key accounts), it will prove beneficial to spend more time and money evaluating the creditworthiness of these accounts.

The reverse is also true: less time and money should be spent clearing credit for the 80 percent of your customers that account for only 20 percent of your customers (sub-critical accounts). The simple truth of the matter is that a bad decision on an account in the top tier will be of much greater consequence than on one from the lower tier.

In reality, you may need to further differentiate your customer base. The top 5 percent of accounts in terms of customer sales will often benefit from separate review protocols more than the customers that fall in the 5 to 20 percent range.

Likewise, those accounts that fall between 20 and 40 percent in terms of sales volume can be sufficiently dissimilar from the bottom 60 percent of customers so as to also require different approval standards.

Accounting for Gross Profit Margins: Another key factor that bears consideration is your selling margins. The higher your margins, the more lenient you can be in granting credit. By the same token, tight margins require much more careful consideration because a single loss requires a much greater amount of low-margin sales to make up for the loss than if higher margins were involved.

On the surface, this appears straightforward, and it is if you are the rare firm that enjoys similar margins across your entire product line. Most companies, however, must deal with considerable variance in margins. If this is your situation, you will need to develop a multi-tiered approach for credit approvals based on the margins of the products each customer is expected to purchase.

Define Your Situation with a Matrix

Building a decision matrix is a terrific way to get a handle on all the nuances that affect your firm's credit decision process. It will also provide a framework for defining decision parameters consistent with the customer's volume and profit margin characteristics.

The accompanying table provides a straightforward sample of the rationale that can be applied to your credit decision process. In many cases, you will want to add additional distinctions. Between key accounts and sub-critical accounts, you might also add standard accounts or high-risk accounts. If your selling margins are of sufficient range, you might also add an average margin category.

The important thing is to set up the matrix in such a way that the grid describes your customer base and gross profit margin environment. From there it is a simple matter to assign appropriate decision parameters to each cell in the matrix.

Adding Scoring to the Mix

Credit scores can add another level of sophistication, and hence differentiation to your decision process. Even if you merely assign a risk rating to your customers ranging from one to three (where one equals low and three stands for high), you can begin to segment your decision-making parameters. The table on the right illustrates how credit limit parameters might be set based on the account type, gross profit margin, and risk.

While you won't want to go overboard and make your credit decision environment too complicated, credit scores can be used to not only set credit limit parameters, but also to either automate the decision-making process within specific parameters or recommend additional credit checking if not all elements fall within your pre-set parameters.

For example, consistent with the parameters reflected on the above table, any customer with a credit score that falls into the level 1 or 2 risk categories could automatically be granted credit up to $5000. Above that amount, ordering a full credit report would be all that is necessary to grant credit up to the limits indicated in the table. If there is a need to exceed those limits, other information or security could be required.

Conclusion

The idea of building a decision matrix is to lay out in a consistent, logical fashion the required standards for making credit decisions. This will not only help you set policy but also take steps to reduce the approval cycle by taking less time to make decisions because you are basing them on sufficient but not excessive amounts of data. Doing so also helps reduce your credit information costs. The idea is to collect only enough data to make a good decision. When you treat every customer and transaction the same, it is very likely your cost per decision, and therefore your overall credit information costs will be too high.

Experienced credit executives recognize that upwards of 80 percent of the credit decisions they make (depending on their industry, of course) can be safely made without extensive analysis. The idea is to create a framework where the "sure thing" can be separated from the "needs a closer look." Creating a decision matrix will help you do just that.

By making credit scores an initial part of the equation, you enhance your ability to automate a substantial number of decisions and determine the next step required in your credit investigation for those situations that require further review. By themselves, credit scores cannot tell you what to do. Only when they are incorporated within a well thought out approval process, such as can be described within a credit decision matrix, can scores help you make better decisions, faster and with less cost.