Applying predictive analytics to operational and historical data can help organizations reap significant long term benefits. Core predictive modeling methods are based on crunching historical data, i.e. using the past experiences of an organization to predict the future. Predictive analytics focuses on helping organizations to gain intelligence that can be useful when making current and futuristic decisions; particularly with regards to quick decisions that need to be made immediately and pertain to large volumes of data.
With the application of predictive analytics companies stand to gain benefits in three key areas:
- Minimizing risk; e.g. companies are better able to identify risks in areas such as loan and credit origination
- Identifying fraud; e.g. identifying fraud in insurance claims
- Pursuing new opportunities.
Furthermore, predictive analytics increases operational efficiency by:
- Making it easier to run campaigns, promotions and offers by indicating patterns and predictive models.
- Helping identify customers who may not be satisfied as well as indicating the underlying cause that may result in the organization losing such a customer.
- Improving sales forecasting and profitability
- Identifying emerging trends in the market
While predictive analytics has many benefits, one must bear in mind that it does not predict the future with precision; it just helps identify patterns and trends that forecast likely futuristic outcomes through statistical modeling and machine learning. Also, it is important to remember that the probabilities of future outcomes that come forth through predictive analytics are not fool proof or error-free. Even if predictive analytics seems to favor a promotional idea, it is not necessary that the particular idea generates the most footfalls per dollar spent. When modeled with accuracy, predictive analytics does however allow for increased effectiveness and also results in better decisions and investments by the management.
Disclaimer: The views expressed here are solely those of the author in his private capacity and do not in any way represent the views of Systems Limited, or any other entity related to Systems Limited.