Predictive analytics make your marketing campaigns more customer-oriented.

Predictive analytics is information technology that produces a predictive score for each customer or other organizational element. Predictive analytics optimizes marketing campaigns and website behavior to increase customer responses, conversions and clicks, and to decrease churn. Each customer’s predictive score informs actions to be taken with that customer — data science just doesn’t get more actionable than that.

Customer Targeting

Predictive analytics make your marketing campaigns more customer-oriented. Predictive analytics helps you in target marketing. By creating an effective predictive model that ranks the customers in your database according to who is most likely to buy, subscribe, or meet some other organizational goal, you have the potential to increase the return on your marketing investment. Specifically, predictive analytics for marketing can:

  • Increase profitability
  • Increase your conversion ratio
  • Increase customer satisfaction by reducing unwanted contact
  • Increase operational efficiencies
  • Learn what works (or doesn’t) in each marketing campaign

Risk Management

One of most effective risk management philosophies is to work smarter, not harder, implementing holistic tools, such as predictive analytics to ensure it is minimized. Predictive modeling is an effective tool that addresses the needs of many industries – turning hundreds of thousands of data points into tangible data that can predict anything from consumer demands to credit scoring and anything in between. Challenging traditional personnel management practices, predictive modeling shines a light on the psychology behind today’s work force.

Predictive modeling can help managers focus on causation rather than correlation. When an incident occurs, many managers tend to put emphasis on what happened, not why it happened. As a result, they often work to fix the correlating issue rather than addressing the root cause.

Demand Prediction

Demand forecasting is the area of predictive analytics dedicated to understanding consumer demand for goods or services. That understanding is harnessed and used to forecast consumer demand. Knowledge of how demand will fluctuate enables the supplier to keep the right amount of stock on hand. If demand is underestimated, sales can be lost due to the lack of supply of goods. If demand is overestimated, the supplier is left with a surplus that can also be a financial drain. Understanding demand makes a company more competitive in the marketplace. Understanding demand and the ability to accurately predict it is imperative for efficient manufacturers, suppliers, and retailers. To be able to meet consumers’ needs, appropriate forecasting models are vital. Although no forecasting model is flawless, unnecessary costs stemming from too much or too little supply can often be avoided using data mining methods. Using these techniques, a business is better prepared to meet the actual demands of its customers.

Health Care

Predictive analytics is rapidly becoming one of the most-discussed, perhaps most-hyped topic in healthcare analytics. Machine learning is a well-studied discipline with a long history of success in many industries. In healthcare industries, prediction is most useful when that knowledge can be transferred into action. Healthcare companies can utilize predictive analytics for improving patient care, chronic disease management, hospital administration and supply chain efficiencies. The opportunity that currently exists for health care systems is to define what “predictive analytics” means to them and how can it be used most effectively to make improvements within their system.

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