August 2014 Newsletter


 

Data, big Data and Predictive Analytics changing the way we live, work and play

Understanding the Past and Present to Predict the Future

People often ask what the definition of Predictive Analytics is.  Predictive Analytics and modeling is a collection of mathematical techniques, algorithms used to determine the probability of future events, analyzing historical and current data to create statistical model'(s) to predict future outcomes. The model'(s) are validated and revised as additional data and new experiences are added and available.

Predictive models are increasingly accurate and useful as the volume of relevant data and different sources is introduced. In this vein, predictive modeling becomes more valuable over time.

How Various Industries Use Predictive Modeling to Improve Conditions

Here are examples of how differing industries use predictive models to change the way we work and live:

  • Law Enforcement 

Law enforcement agencies in Chicago, Memphis, New Orleans and many more are using predictive modeling to lower the rate and volume of crime in specific communities. Combining data on patrol patterns and level of law enforcement presence with data from past criminal activity such as location, dates, times and type of crime, law enforcement personnel can be dispatched to patrol specific communities at given times to decrease potential criminal activity.

  • Financial Services

Many financial institutions use Predictive modeling to identify the propensity to quit of a customer before they attrite and implement a set of actions including visits, telephone calls email and loyalty programs to positively impact the behavior of the customer.  These models are also used to identify the next product or service the customer is most likely to buy.  Companies are now using predictive models to identify the best candidate for any given position.  They are also applying these models to identify at risk of leaving employees.

  • Sports

Sport franchises whether in baseball, or football and stadium organizers are using predictive modeling to maximize profitability of each home game and event. Predictive models are been developed and implemented to determine best individuals for a position on the team and probability of success.  Also, the models are implemented to cover everything including available merchandise, promotional ticket prices and vendor location and variety of services can be optimized to maximize profitability. Even the rate and frequency of ticket sales, event time and day and opponents can be analyzed to create profitable venues.

  • Healthcare 

Healthcare organizations are using predictive modeling to assist in diagnosing patients and identifying risks associated with different types of care giving. Analyzing countless data such as family history, lifestyle, past illness and treatment, current medications and lab results, healthcare professionals are able to identify patients at greater risk of disease and re-admittance, leading to opportunities in preventative care and health education.

  • Retail 

Retailers large and small are using predictive modeling to manage inventory and all aspects of their supply chain: forecast, source, fulfillment, delivery and returns. Predictive modeling allows retailers to minimize out-of-stock occurrences and optimize inventory management and warehouse space. Data such as buyer personas, store and display locations, promotional pricing, associated merchandising and even the weather can be analyzed to predict sales volume and buyer behaviors.

  • Agriculture 

Farmers are using predictive modeling to manage and protect crops from planting through harvest. Decisions can be made on the use of fertilizers and pesticides by analyzing data such as weather, soil moisture, ground temperatures, fungus levels and previous production. Predictive modeling allows farmers to use fertilizers and pesticides at an ideal rate, volume and time to protect crops, as opposed to routine and ongoing use. This lowers the cost of crop production and provides a level of environmental protection.

 

Summary

As companies today are amassing an ever-increasing volume of big data, predictive modeling is being used to improve everything around us from the way we shop to the entertainment we enjoy, and the way we receive services and purchase products. Predictive models are being developed and applied in any customer relationship, whether a business to consumer or business to business.  The more we know about the past and present, the better we're able to predict the future to serve others, making faster and better informed decisions capable of reducing costs and increasing customer satisfaction.  In the business to business sector predictive models allow for the identification of those customers with a high propensity to quit, what product or service the customer is most likely to buy and at what price and allow for pricing optimization while maintaining desired retention rates. 

Predictive models are providing insights that are often leading to innovations and first-in-market opportunities that not only serve businesses and consumers, responding faster and more cost effectively to customer needs.  Those companies who embrace predictive Analytics see their competitiveness increase and create stronger customer satisfaction.

Contact us today for your no obligation assessment of how you increase your competitive positioning by developing and applying predictive analytic models.


To learn more, please visit us at www.tdtanalytics.com 
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