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Decision-making in times of crisis: should they be based on gut feeling or data?

Decision-making in times of crisis: should they be based on gut feeling or data? 27

By Marieke Saeij, CEO at Onguard

Decision-making is a crucial part of every business and is something organisations do each and every day. That said, it is not always easy – particularly in uncertain times such as these. As such, businesses decisions are often based on a combination of factors. In some instances, companies will leverage data to help inform their decision-making, but at times, they will rely on their own experience or ‘gut feeling’. However, there are some occasions, such as the current crisis, in which relying solely on previous experience would be impossible given that nobody has faced a situation quite like this before.

Yet, even in midst of a global pandemic, the need to make business decisions remains and to some extent they may be even more crucial to ensure the survival of organisations. Therefore, in a wildly unprecedented situation, how can companies mitigate such vast levels of unknown risk? This is where finance departments can play a vital role: using facts, figures, and in-depth analysis to determine which risks are worth taking, which investments should be made, and which should be avoided. An essential part of this process is data.

Data-driven decisions

The amount of data businesses have at their disposal is growing exponentially with IDC predicting that the Global Datasphere will grow from 33 zettabytes in 2018 to 175 zettabytes by 2025. This growing volume of data can be of immense value for organisations – helping them to gather the insights needed to make informed busines decisions. According to Onguard’s FinTech Barometer 2020, a quarter of companies consider themselves to be very data-driven – meaning that 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.

This data-driven approach to decision-making can offer finance departments a multitude of opportunities. For example, credit information on customers is becoming increasingly rich and, as such, can be leveraged to make more accurate and informed predictions about consumer behaviour. This can provide businesses with extremely useful insight before they accept a customer, enabling them to predict the expected growth of the account, the chance of bankruptcy or the payment behaviour of the customer. Thanks to these insights, finance professionals can then identify opportunities and risks more quickly and make appropriate decisions on that basis. In turn, this will enable them to manage and strengthen their cash flow more effectively.

Detecting deviations in payment behaviour

Marieke Saeij

Marieke Saeij

As well as helping businesses to make informed decisions about prospective customers, being data-driven also helps them to make decisions regarding their existing customers. By feeding customer payment data into solutions that make use of artificial intelligence, companies can identify possible problems early – or even predict them ahead of time. As such solutions recognise patterns in behaviour, organisations can then foresee potential problems and better prepare so that they are ready to take the appropriate action when customers start showing signs that they may not pay. For instance, when it has been identified that the customer has deviated from their normal payment patterns, the finance department can proactively contact the customer about paying their invoices to avoid getting into arrears.

The role of the credit manager

While data certainly provides guidance and insights, what does it 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 COVID-19 pandemic. However, credit management is all about relationships and although data can provide insight into overall payment patterns, 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 data doesn’t have the emotional intelligence to pick up on individual variances like this. After all, customer relationships could be soured if the personal approach is lost altogether and decisions are purely automated based on data.

For finance professionals, it is therefore important to find a balance between using data-driven insights and maintaining a personal relationship with the customer. Not only does this hybrid approach allow data to be used as a tool to limit risks, but it also enables companies to offer a more individual customer experience. Unfortunately, there is no data on what ‘goodwill’ will yield in the future, but ultimately, adding a personal touch to tasks such as credit management will result in higher levels of customer satisfaction, and in turn, help to retain their business for the future.

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