Businesses face all kinds of threats and risks which can affect their performance.
Some of these threats may be inevitable but businesses can identify these threats and fortify themselves against them through risk management.
The biggest threats an Australian business could face are from cybercrimes, a shift in the market and regulatory changes.
Risk management involves identifying and evaluating potential risks to the business and formulating strategies to utilise their resources to handle these risks. With a good risk management system in place, a business can increase its survivability.
To create a proper risk management system, businesses need data and lots of it; using this information to create a comprehensive strategy requires data analytics.
This blog post will highlight the ways data analytics can improve risk management for businesses.
Customer churn is a major problem for businesses; it refers to the rate at which customers eventually stop interacting with them.
Customer retention is vital for any business and every year a customer stays in a company, the more profits they’ll generate. It’s estimated that for financial businesses, even a 5% increase in customer retention can generate up to 25% in revenue.
There are many reasons why a customer would want to stop doing business with a company and businesses need to identify these potential reasons and create a risk strategy to keep customers with the company longer.
By analysing big data and predictive analytics, businesses can study historic data to look for potential causes of customer churn.
Analysing data regarding consumer behaviour, customer demographics and trends will help businesses find patterns between these sets of data, giving them insight into why customers tend to defect.
For businesses in the manufacturing industry, the flow of operations is vital. The biggest issue with managing operational risk is the huge amount of data required to identify the risks.
Fortunately, modern data analytics tools can easily find meaningful patterns in this data very quickly.
The quality of the materials used, production time, cost and the reliability of the suppliers all contribute to how efficiently the manufacturing process will go.
Data about the machines being used in the production process can be analysed to assess their reliability. Data about how many hours the machines are used every day, their schedules, and their maintenance times can provide insight into how these machines can be used efficiently and effectively, avoiding any breakdowns.
Analysing worker data will also help in developing an operational risk management strategy.
By analysing the working hours of workers, their productivity, the number of accidents that happen on the factory floor, manufacturing techniques and current workers benefits, businesses are able to reduce employee turnover through effective employee risk strategies using the data gathered.
A business’s working capital is a measure of a company’s efficiency and shows how stable the business is. The company’s many assets, like its building, equipment and inventory, all contribute to its working capital.
Risk management strategies need to be devised in order to make efficient use of the company’s working capital. To do this, data analytics can be used to identify the current efficiency of the business’s assets and compare them to its current liabilities.
Through data analysis, businesses can identify weaknesses in their current assets and the kinds of risks their liabilities impose on them. With this information, businesses will be able to use their assets more efficiently.
Data analytics also allows a business to predict how their current liabilities, like taxes, creditors and overhead expenses, will change over time or in reaction to other factors like laws and regulations. This will help the business plan out risk management strategies to handle these liabilities while keeping their working capital efficient.
With the help of data analytics, you can create a comprehensive risk management strategy to protect your business from many potential threats.
Using large amounts of data to help create a risk management plan may have been difficult in the past but with today’s efficient data analytics software, analysing relevant data for useful insights has never been easier.