Why is predictive analytics important?


For the uninitiated, Predictive Analytics is a term that describes using the past data to develop informed guesses about future outcomes. Many business corporations have been using this advanced tool for many years to assess risk and detect frauds. Many organizations across some industries are learning that there is a massive amount of data customers leave behind when networking with their company - be it online, in-store or both. Predictive analytics helps in extracting this valuable information from the enterprise data to develop predictive models that can advance business performance through the effective delivery of products and services.

In this article we have listed top reasons why predictive analytics is important.

More applications

Sales and marketing applications of predictive analytics also include scoring leads to target sales resources, product recommendations to increase cross-sell, behavior-based targeting to maximize online advertisement clicks, market research survey analysis, scoring sales channels and other B2B applications, triaging collections, email targeting, increased fundraising profit by scoring donors, and targeting higher lifetime value (LTV) customers for acquisition, upsell and high-tier customer care.

Develop customer retention strategies

Predictive analytics offers a unique competitive advantage to a variety of customer-facing activities. Also, it gives the required knowledge to target customers at the right moment. Developing customer retention strategies is the main significance for businesses even though churn modeling may be the most widespread business application of predictive analytics. Also, you can effectively spend your marketing dollars by predictively scoring customers based on their next probable action.

Advancing core business capability competitively

The next step to growth beyond increased sales is to improve your company’s core offering and how it is delivered. This could mean different things across different industries, but at its core, it focuses on using predictive analytics to optimize your approach to market. In the field of insurance, this could mean more effectively driving selection and pricing decisions by accurately identifying customers who pose a greater risk of submitting higher aggregate claims. Additional examples in other industries include predicting inventory demand, predicting health risk for proactive healthcare and predicting likely or swing voters for optimization of political marketing campaigns.

Improving Business Processes

Companies use predictive models to manage resources and forecast inventory. For example, Airlines use predictive Analysis to set ticket prices. As well, Hotels predict the number of guests to maximize occupancy and boost revenue. Predictive analytics allows organizations to operate more efficiently. Moreover Fraud investigators can look into only a set number of cases each week. With predictive analytics, you can use your company’s past experience with fraud cases to score transactions according to their level of risk.

India is also advancing in this field as there are many predictive analytics solutions in india. There are many firms which are considered as best predictive analytics firms in india. In conclusion we can say that with a growth in big data and the evolving nature of Business Intelligence, predictive analytics can offer valuable insights for organizations. Predictive analytics is a smart way to add more insight and clarity into your business decisions. Even though it may take a lot of time to collect useful data and devise a plan to sort through it, when you see the results of what it can do, it will be worth it.

Why is predictive analytics important? Why is predictive analytics important? Reviewed by saahil on 21 January Rating: 5

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