Predictive analytics has been widely associated with helping marketing and sales come up with products that your company’s customers will want to buy by looking at their purchase history along with other data. In other areas, predictive analysis is also used. But because of its predictive nature, most finance executives think that predictive analysis has no place in their world. They reason that forecast and guesses do not have a place in their world where facts and numbers are kings.
That is just wrong!
Here are some scenarios when predictive analysis helps your finance department.
Predictive analytics can help you optimize inventory.
When dealing with inventory, you need to know the net margin of the different products that you have. All of the items in your inventory have costs, and there are associated costs should you run out of stock for a particular item. Predictive analytics can help you identify which of your products can give you the best profit while also considering inventory costs and lost opportunity costs in the event of a stock out.
Today, you can easily update your sales forecasts in real-time using data straight from the POS checkouts. With predictive analysis, you can dynamically change the prices to get the maximum profit out of your merchandise and also optimize routing and the volume of merchandise you keep.
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Predictive analytics can help you keep your employees.
Predictive analytics can help you determine which of your current employees are likely to resign voluntarily from your company using data such as the age, the timing and even the salary details of those who have left in the past. You now have a list of employees who are most likely to resign for one reason or another. If you see a valuable staff member or key personnel in the list, you could intervene and try to keep that employee happy. This will save you the cost of recruiting and training new employees as well as the cost associated with lost productivity and morale.
Predictive analytics allows you to know and target your most profitable customers.
You can take your data and use discriminant analysis in order to identify customer segments that are most profitable for you. Then basing on the results of your discriminant analysis, you can use predictive analytics to know other segments, which might be profitable for you as well. Furthermore, you can also determine how to market and sell to these segments.
These are just some examples of how finance could use predictive analytics to do their work and help other departments as well. It shows how predictive analytics can help the company become more profitable, cut costs and increase sales and revenues. And while there are other use cases of predictive analytics for finance, these are enough to show you that predictive analytics is a very useful tool for every employee in your company.
Thinking of enjoying the many advantages of predictive analytics? Call Four Cornerstone today and find out which solutions and platforms are great for predictive analytics and allow your employees to do more for you.
Photo courtesy of eric.delcroix.