Are Predictive Models Useful in Business?

More and more companies are turning to predictive analytics to optimize their processes, achieve better business results and increase their market share.

Monday, July 19, 2021

Organizations use internal predictive analytics to forecast trends, understand and predict customer behavior, improve performance and drive strategic decision making.

You may be interested in: "What are predictive models and what are they used for?"

Some of the most common internal uses of predictive analytics are:

Fraud Detection: Help to reduce and eliminate losses generated by fraudulent activities before they occur, obtaining greater accuracy and better performance. With predictive models it is possible to analyze all the actions of a network in real time to detect anomalies that may indicate fraud, vulnerabilities and threats.

Marketing: Improve relationships with customers, anticipating their behavior, to promote sales opportunities, attracting, retaining and identifying the most profitable customers.

Operations: Help forecast inventory needs and manage the organization's resources to optimize expenses and costs.

Risk: Credit scoring, for example, is used to assess the likelihood that a potential home, car or insurance buyer will default on payment. They are also used in other related processes, such as in claims and collections.

Customer Loss: Predict which customers, when and why they will terminate their relationship with the organization. Predictive models are fundamental in this area, since the cost of losing a very good customer can be much higher than the investment in tools to detect these situations in time and prevent them from happening.

Sales Forecasting: analyzes previous history, seasonality, market events, etc. to provide a realistic prediction of the demand for a product or service. It can be applied in the short, medium or long term.

Finance: translating a set of hypotheses about the behavior of markets or agents into numerical predictions, these predictive models are used to support companies in decision-making processes about investments or returns.

Also read: "Data Science: Most used models in business."

At PREDIK Data-Driven we develop all kinds of predictive models for our clients. Learn more about our approach.



More on this topic

Big Data for Fraud Prevention

September 2021

Big data analytics is an effective solution for identifying behavioral patterns and establishing strategies to help detect and prevent fraud in various business sectors.

Most companies are not aware of the information they have and how to leverage, analyze and understand it, which can result in the loss of a large amount of potentially useful data by normalizing fraud and other criminal activities in their processes and make them difficult to prevent and detect.

Supply Chains Predictive Analytic benefits

September 2021

Unlike historical analytics, predictive supply chain analytics allows you to anticipate and prepare for the future, taking out the conjectures planning processes and improving decision making.

Predictive supply chain analytics use advanced technological tools such as machine learning, geomarketing, data mining that enables organizations to identify hidden patterns, understand market trends, identify demand, establish pricing strategies, achieve a high return on investment, optimize and reduce inventory costs.

Predictive Modeling: The New Real Estate Journey

August 2021

Predictive analytics has transformed the real estate industry due to its powerful ability to deliver fast and accurate actionable insights. This has largely come about through the advent of Big Data and Geographic Information Systems (GIS) that harness the intrinsic power of real estate data.

Predictive analytics tools take this analysis to the next level to predict future outcomes based on how past and present events occurred. Consumer demographics, housing trends and property price history are some of the areas where predictive analytics represent a huge opportunity for the industry.

What are Predictive Models and What are they Used for?

July 2021

Predictive models are statistical tools that use machine learning supported by Big Data mining to predict and forecast likely future outcomes with the help of historical and existing data by inputting multiple parameters.

They can be used to predict virtually anything containing existing data, in every sector imaginable, from ratings of any program, a customer's next purchase, credit risks, decision making among others.

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