Predictive Analytics Drive Business Strategy: A Guide to Modeling Techniques for Making Well-Informed Decisions

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

Many businesses fail because of their inability to forecast future outcomes. 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, data-driven decisions, rather than merely reacting in response to events that have already taken place.

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

Predictive Analytics Modeling Techniques

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

Predictive analytics can be applied to both structured and unstructured data and works by training a model to predict values for new data based on a set of variables. The model then identifies relationships and patterns among 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 within large data sets to predict outcomes – helps to prepare data for analysis. Once the data has been mined, predictive modeling is the process of creating and testing different predictive analytics models. When the model has been trained and evaluated, it can be reused in the future to answer new questions about similar data.

The most common predictive analytics modeling techniques include:

• Decision trees
• Linear regression
• Multiple regression
• Logistic regression
• Neural networks
• Time series
• Random forest
• Boosting

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 several decisions followed by different chances of occurrence. Each branch of the decision tree is a possible decision between two or more options, whereas each leaf is a classification (yes or no).

Linear regression – this predictive analytics modeling technique is used when the target variable is continuous and the dependent variable is continuous or a mixture of continuous and categorical, and the relationship between both the independent and dependent variables is linear. If multiple 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 the multiple regression techniques is to model the linear relationship between the 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 either 0 or 1) or dichotomous.

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

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

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

Boosting – is a modeling technique that combines multiple simple models to generate the final output and uses the concept of ensemble learning. Each model that runs dictates what features the next model will focus on, and as the name suggests, one is learning from another which in turn boosts the learning.

Predictive Analytics in the Real World

 Any industry can use predictive analytics to forecast outcomes and use insights to drive its operation's strategies. Here are a few real-world examples of how healthcare organizations, marketing teams, and meteorologists use predictive analytics:

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

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

Using predictive analytics, marketing teams can gain a better understanding of customers and campaigns' performance. They can observe how consumers react to their campaigns, what's working and what's not, and use these insights to create and launch advertising that will lead to an increase in future sales.

Content creation is another marketing area in which predictive analytics is extremely useful. For example, Netflix and Spotify use predictive analytics to come up with relevant series/song recommendations that they predict their users would enjoy.

Predictive analytics in the retail industry helps retailers understand customer behavior and shopping patterns 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, rainfall, and wind speed based on reliable data. In the past, it was possible to forecast the weather a day or two ahead, but today, weather forecasting is possible weeks and potentially months in advance. Satellites monitoring the land and atmosphere accumulate data about the current conditions and through atmospheric processes, predictive analytics algorithms predict what weather to expect.

Predictive analytics is a rapidly growing analytics instrument that fuels businesses' analytical journey. If you haven't added predictive analytics capabilities into your organization, now is the time to incorporate these useful tools to make well-informed, intelligent decisions, develop unique and successful strategies, and discover new and smarter opportunities for business growth.

Businesses that take advantage of predictive analytics can save time and money by addressing issues before they occur, discovering 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 SVP of Developer Tools at Infragistics and developer of Reveal embedded analytics software. Jason has written tech articles for various pubs, speaks at national conferences, and has authored/co-authored 10 books on software/development. His expertise in development further extends to ensure data and analytics are displayed in innovative and customer-driven 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 in product design, open-source, and changes in data analytics and business intelligence over the past 30 years.