Companies are increasingly exploring new ways to analyse and leverage the data they collect. Trended data describes the analysis of a dataset over time, as opposed to a snapshot at a single moment. This method can paint a more comprehensive picture of any patterns or trends that may develop for debtors or consumers – informing future decision-making.  

What does trended data mean? 

Clean data logging is critical to modern business management. Astute companies must collect a wide array of information from consumers, suppliers, creditors and more. Often, this data is viewed at single points in time – like a freeze frame. ‘Trended data’ instead involves observing a dataset over a length of time – more like a film. 

Using trended data solutions adds a predictive layer to the analysis. For example, you might record all debtor data over a financial quarter. Not only will you see the position of a customer at a single point in time, but also their previous position and how it correlates to similar entities. This practice may improve the accuracy of your business’ predictions as you acquire more information.  

What are the characteristics of trended data? 

In maths terms, trended data adds an x-axis (time) to the number plane. Say that you record data for the same group of customers monthly. Trended data solutions connect those data points over multiple months, forming what’s known as a ‘trendline’. Not every trendline shows a pattern of behaviour – some entities’ data will jump up or down without much apparent rhyme or reason. However, often they do reveal a potential trajectory. If a customer’s data goes steadily down month-on-month (expressed by a negative trendline), they are considered more likely to continue in that direction.  

So long as the information provided is accurate and well-kept, trended data solutions increase in usefulness the longer they are in place. For example, you may observe consistent seasonal fluctuations if the data spans several years. Generally speaking, the higher the volume of clean, regular data over time, the better for the company.  

How does trended data relate to credit score? 

With ample trended data analysis, a business can develop sophisticated modelling for debtor behaviour. It may allow it to distinguish between entities that, on the surface, are in the same credit score position but trending in opposite directions. Or the analytics may reveal that while the credit-risk exposure to the business is manageable right now, too many debtors are trending towards default simultaneously. Solutions that leverage extensive subsets of trended data, such as CreditorWatch’s RiskScore and DebtorLogic, may more readily identify these patterns to inform decisions.  

For example, let’s say two debtors to your business both have, at a single point in time, a RiskScore of 612 – indicating a ‘low-risk’ B1 rating. At first glance, they appear to be the same type of debtor. However, the trended historic data for those entities may reveal that one has abruptly descended from a previously higher score, whereas the other is on the way up. With this information in mind, you may focus more of your collections efforts on the downward-trending business.   

In simple terms: the more sophisticated the trended data you have (coupled with the right tools to analyse it), the more accurate your predictions. That isn’t to say that you’ll foresee every outcome – the world is still full of surprises. The business will, however, have greater clarity than it otherwise would. Decision-makers can reinforce their position with reliable information instead of relying on intuition alone.