How can predictive analytics help finance managers
The promise of big data and predictive analytics on data sets belonging to organizations is huge. Businesses can come out with customer-specific consumption patterns, spending behaviors, likes, dislikes, requirements, and improvements desired.

That is quite an extensive area of study for any business manager. Predictive analytics finance can help you do just that with the help of tools.

How to study behaviors with the help of predictive analytics in finance?

• Predictive analytics is now being extensively used by businesses to gain insight into various financial aspects of businesses. Most of these aspects revolve around the purchase behavior of customers.
• The purchase behavior of customers is guided by several factors. You can have a group of customers going for low-value products while others going for high-value products.
• While some customers purchase only for their children or parents, others may purchase for the whole family. There are still others who spend for themselves only. You can get all these data from the store data, credit card data and online data. This vast amount of data can throw up very detailed insight into the behavior of customer groups.

What are the main components of predictive analytics in finance?

• The main components of a successful predictive analytics attempt are data, statistics, and assumptions. You need coherent data that throws up some light. Random data in huge terabyte sizes does not serve any purpose.
• Once the data set is ready you need tools to analyze this set. This can be done with the help of statistical techniques of varying complexities.
• The statistical methods are very important since the outcome depends on the right analytics. You also need to assume different factors like absence of seasonal influences on customer purchase decisions, an absence of interference with any competitive product, and the like.

How can predictive analytics help in online revenue generation?

• The ecommerce industry is booming. Customer behavior in online portals has definite patterns that indicate a purchase or abandonment. A huge amount of data is generated as customers navigate through online shopping portals.
• By using predictive analytics finance on such datasets, finance managers can come up with some defined navigational paths which may lead to a purchase or a cart abandonment. By sifting through the data thrown by all online customers and visitors, business and finance managers can get an idea about the sales figure. This can help them project a value for online sales revenue. This is a very helpful feature of predictive analytics. Businesses make use of such analytics quite often. In fact, this helps them in planning their inventory and supply chain efficiently.

Can predictive analytics help in bringing down expenses?

• Yes, with the help of predictive analysis you can bring down some costs. Predictive analysis can throw up insight about under-performances in infrastructure including manpower.
• Once the zone of under-performance is detected, you can rectify it and bring down the cost of the associated process. This is used in industries such as healthcare. If predictive analytics shows that the rate of readmission of patients has become high in a certain hospital, the Medicare payment for the same is reduced.
• This puts a question mark on the credentials of the hospital. The hospital authorities and doctors then strive harder and make sure that patients recover completely before being released. This obviously brings down readmission expenses not only for the hospital but also for the patient.

What effect does predictive analytics have on supply chain efficiency?

• To improve supply chain efficiency, business managers use predictive analytics finance on data from the POS systems. This may be available from online purchases, in-store purchase or mobile purchases. Using predictive analytics tools for this data, finance managers can predict where products are likely to sell quickly and where they are likely to move at a slow pace.
• This is very helpful for the business since the managers can plan their inventory accordingly. This helps in reducing cost. You can also get an idea about the most popular products. So keep the inventory adequately supplied with the most popular products while trimming down the products that move slow.

How can predictive analytics alert customers in utility industries?
In utility industries such as electricity distribution companies, predictive analytics finance can be used to project customer bills. If the projected bills are found to be too high by previous standards, the utility provider can alert the customers about high bills. This can enhance customer satisfaction as well as make payments easy for customers.