A guide to modeling techniques for making informed decisions

Predictive Analytics Drives Business Strategy: A Guide to Modeling Techniques for Making Informed Decisions

Predictive analytics is making huge strides as modern businesses strive to make data-driven decisions

Many businesses fail due to their inability to predict future results. However, the most successful organizations recognize the strategic value of generating actionable insights from their data.

With the help of predictive analytics, businesses can see into the future and make informed decisions, data-driven decisionsrather than simply reacting in response to events that have already taken place.

By analyzing past and present data, as well as industry trends, predictive analytics enables organizations to improve their operations and performance by examining areas for improvement and ways to reduce risk and prevent fraudulent behavior. This brings us to the question; What is the best statistical technique to use in your business analytics journey?

Predictive analytics modeling techniques

Predictive analytics encompasses a variety of statistical techniques such as data mining, data modeling, AI, and machine learning to identify trends and patterns that may reappear in the future.

Predictive analytics can be applied to both structured and unstructured data and works by training a model to predict the values ​​of new data based on a set of variables. The model then identifies the relationships and patterns between these variables and provides a score based on what it was trained to look for.

Data mining — which is the process of finding anomalies, patterns, and correlations in large data sets to predict outcomes — helps prepare data for analysis. Once the data is extracted, predictive modeling is the process of building and testing different models for predictive analytics. When the model has been trained and evaluated, it can be reused in the future to answer new questions on similar data.

The most common predictive analytics modeling techniques include:

  • Decision trees
  • Linear regression
  • Multiple regression
  • Logistic regression
  • Neural networks
  • Time series
  • random forest
  • Booster

Decision trees – one of the most popular predictive analytics techniques that identify how one decision leads to the next. Decision tree techniques use branching to visually represent multiple decisions followed by different chances of occurrence. Each branch of the decision tree is a possible decision between two or more options, while each leaf is a classification (yes or no).

Linear regression – this predictive analytical modeling technique is used when the target variable is continuous and the dependent variable is continuous or a mixture of continuous and category, and the relationship between the independent and dependent variables is linear. If several independent variables have an effect on an outcome, multiple regression is more appropriate.

Multiple regression – uses several explanatory variables to predict the outcome of a response variable. The goal of multiple regression techniques is to model the linear relationship between explanatory (independent) variables and response (dependent) variables.

Logistic regression – an even more complex form of regression that does not require a linear relationship between the target and the dependent variables, logistic regression is used when the dependent variable is binary (assumes a value of 0 or 1) or dichotomous.

Neural networks – are advanced predictive analytics techniques used to determine the accuracy of information obtained from regression models and decision trees. They are composed of a set of algorithms modeled after the human brain and designed to recognize patterns in data sets.

Time series – this predictive analytics modeling technique is used to predict a future response based on response history. It can help users understand and predict the behavior of dynamical systems from experimental or observational data.

random forest – is one of the simplest and most accurate predictive analytics techniques that uses an ensemble learning method for classification and regression. It works by building a multitude of decision trees at training time and outputting the class which is the mode of the classes (classification) or the mean prediction (regression) of the individual trees. Decision trees in random forests have no interaction with each other and are executed in parallel.

Booster – is a modeling technique that combines several simple models to generate the final result and uses the concept of ensemble learning. Each model that runs dictates which features the next model will focus on and, as the name suggests, one learns from the other, which in turn drives learning.

Predictive analytics in the real world

Any industry can use predictive analytics to predict outcomes and use insights to drive strategies for its operations. Here are some real-world examples of how healthcare organizations, marketing teams, and meteorologists are using predictive analytics:

Predictive analytics in healthcare is intended to be applied to all aspects of patient care and operations management. It is used by healthcare professionals to find opportunities to make more effective and efficient operational and clinical decisions, predict trends, and even manage the spread of disease.

For example, predictive analytics can identify patients with cardiovascular disease who have the highest likelihood of hospitalization based on age, coexisting chronic conditions, and medication compliance. This allows doctors to identify early interventions and prevent complications.

With predictive analytics, marketing teams can better understand customer and campaign performance. They can observe how consumers react to their campaigns, what works and what doesn’t, and use this information to create and launch advertisements that will lead to increased future sales.

Content creation is another marketing area where predictive analytics is extremely useful. For example, Netflix and Spotify use predictive analytics to offer recommendations of relevant series/songs that they believe their users will enjoy.

Predictive analytics in the retail industry helps retailers understand customer behavior and shopping habits, which can be used to optimize operations – design, layout, marketing and merchandising.

Another example of predictive analytics is weather forecasting, which is the scientific prediction of the state of atmospheric conditions such as temperature, humidity, dew point, precipitation, and wind speed based on data reliable. In the past it was possible to predict the weather a day or two in advance, but today weather forecasting is possible weeks and potentially months in advance. Satellites monitoring the earth and atmosphere accumulate data about current conditions, and through atmospheric processes, predictive analytics algorithms predict what to expect.

Predictive analytics is a rapidly growing analytics tool that powers the analytics journey of businesses. If you haven’t added predictive analytics capabilities to your organization, now is the time to integrate these useful tools to make informed and intelligent decisions, develop unique and effective strategies, and discover new, smarter opportunities for growth of your business.

Businesses that leverage predictive analytics can save time and money by solving problems before they arise, uncovering more opportunities to reduce risk, and gaining a competitive advantage.

Author:

By Jason Beres, SVP of Developer Tools at Infragistics

Innovation expert Jason Beres is Senior Vice President of Developer Tools at Infragistics and developer of Reveal embedded analysis software. Jason has written technical articles for various pubs, speaks at national conferences, and is the author/co-author of 10 software/development books. His development expertise extends further to ensure that data and analytics are displayed in innovative, customer-focused ways on modern web and mobile platforms. Jason is an expert on technology issues such as the software testing process, data-driven teams, customer input into product design, open source, and changes in data analytics and computing decision making over the past 30 years.

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