Making data-driven decisions in crisis times
Making business decisions is something organisations do every day, but it’s not always easy – especially in these uncertain times. As such, these decisions are often based on a combination of factors. On the one hand, companies will leverage data to help inform their decision-making. On the other hand they rely on their own experience or ‘gut feeling’.
Of course, relying solely on experience during the current crisis would be impossible. After all, we have never experienced a situation quite like this, so there is little experience to go by. However, decisions must be made in order to ensure the survival of organisations. The question is, in such an unprecedented situation, how can companies mitigate such a vast level of unknown risk? Finance departments play an important role here: using facts, figures and in-depth analysis to determine which risks are worth taking, which investments should be made and which should be avoided. Data is an essential part of the process.
The FinTech Barometer 2020, Onguard’s annual survey, shows that a quarter of companies consider themselves to be very data-driven. In other words, data is used to make decisions within these organisations. Over half of the organisations surveyed (53%) mainly use data for analysis, decision making and predictions. Yet we now see that being data-driven provides finance departments with a multitude of opportunities. As an example, credit information is becoming increasingly rich; and as such, can be leveraged to make predictions about consumer behaviour. Before a customer is accepted, insight into this data is very useful. Using this information, companies can predict the expected growth of the account, the chance of bankruptcy or the payment behaviour of the customer; and with these insights, finance professionals can then identify opportunities and risks more quickly, thus managing and strengthening their cash flow more effectively.
Predicting payment behaviour
Being data-driven also helps organisations make decisions with regard to their current customers. When a customer’s payment behaviour is linked to artificial intelligence, companies can identify possible problems early – or even predict them ahead of time. By recognising patterns, they can then prepare, and will be ready when customers start showing signs that they may not pay. The finance department can then proactively contact the customer about paying invoices to avoid arrears.
The role of the credit manager
Data provides guidance and insights, but what does this mean for credit managers?
Data is key to the smooth running of many business functions, especially as companies navigate the unprecedented situation caused by the pandemic. However, credit management is all about relationships. Data can provide insight into overall payment patterns, but it does not reflect the relationship with the customer. For example, a credit manager will know that when they call a certain customer once, payment will be made, but the data doesn’t have the emotional intelligence to pick up on individual variances like this. That’s why it is crucial not to abandon the customer experience entirely, but to combine it with a data-driven approach. Unfortunately, there is no data on what ‘goodwill’ will yield in the future, but there is a much higher chance of retaining a customer’s business if they are met with a good experience.
As a finance professional, it is therefore important to find a balance between data-driven insights and a personal relationship with the customer. Not only does this allow data to be used as a tool to limit risks, but it also enables companies to offer a more personalised customer experience. Ultimately, adding a personal touch will result in higher levels of satisfaction, and in turn, retain their business for the future.