Data fuels Blue Book scores—inputs are analyzed within a scoring model through a proprietary algorithm. Trade experience surveys, which ask companies to rate experiences with their business partners, are a primary source of data.
Another key data input, and also used as a seller’s record of its trading experiences with customers, is accounts receivable (A/R) aging files. This data has been instrumental in our scores, improving their predictive abilities to quickly identify emerging trends.
The aggregate of this data results in a highly predictive score. Good, consistent trade experiences and solid A/R aging data = a good score.
Contributing data
Blue Book provides complimentary access to two risk management tools for sharing monthly A/R aging files: 1) our online tool, appropriately named AR Reports, and 2) our Accounts Receivable Analysis Report, electronically delivered upon each file contributed.
It should be mentioned that all data contributions are submitted in the strictest confidence. Trade experience survey data is viewable via our Business Reports and online Trade Activity summary feature, but A/R aging data is only accessible by those who share it.
Sharing A/R aging files is an efficient and cost-effective way to report trade experiences. Submitting this information benefits you, your customers, and all other contributors. And, when companies share their A/R aging files, they can also receive deep discounts on Trading Assistance.
Better data equals better decisions—if you’d like to become an A/R contributor and gain access to this exclusive data, please contact a Blue Book representative to get started.
Are all scores created equal?
Scores are an indication of risk at the present moment and based on data inputs. But, if data inputs are limited, scores are more susceptible to volatility, while scores based on deep data generally have greater stability.
For example, companies will ask Blue Book why or how their score is lower than another. It can be perplexing to an established, well-performing business to be scored lower than a lesser-tenured business with scarce data and experiences.
But it happens, and here’s one example of how: a newer business starts out paying its bills within 15 days and does so with great consistency for the better part of a year. This scenario will always produce a very strong, low-risk score output.
However, because its foundation and payment history is not deep, if there are changes—such as 30-day or 45-day pay—the score change could be more pronounced than for a company with a more extensive data set.
The more tenured company may have a lower but still low-risk score, however, its deep data set and length of tenure will likely show some historical data variability due to it having more real-world business experiences the younger company has not endured.
Here’s another way to look at it: if you compare a company with 20 years of industry tenure, ample data, and a score of 875 to a two-year-old company with a score of 925, though the scores have 50 points between them, the risk to sell is low for each based on the score. If the two-year-old company hits a “speed bump” for the first time, its score will no longer be 925.
Pay attention to score changes
Score direction is important to monitor. An ascending score, even if lower by standards, may have lesser risk because of its data path, while a descending score, though higher than the other, may present greater risk due to its data path.
The word “may” is important here—trading risk is what a score says it is—but it’s important to note a company’s score path, along with other available or current data that might not be factored into the score yet.
This is an excerpt from the Credit and Finance department from the Novembe/December issue of Produce Blueprints magazine. To read the whole issue, click here: https://www.producebluebook.com/#november-2024-produce-blueprints-magazine/1/
