Problem Description

The ability to target and maintain the right customers is considered to be the first analytics and data science problem in most companies. Customer retention is considered to be a corporate priority that companies often allocate a lot of resources. Government agencies and companies are yearnings for innovative solutions that can optimize operations. Modern corporations require predictive analytics to solve real-world problems in an era that is characterized by massive availability of data. There is an exponential growth of data in terms of volumes and types (Gandomi & Haider, 2015).

Consequently, there is a growing need for producing valuable business insights from the data. Profitability in the current atmosphere of rapid technological changes does not just entail reducing cost, but also extracting insight from data. Modern corporations have to adapt to the hard economic conditions by embracing systematic competitive differentiation that can be attained through predictive analytics. Companies are in urgent need of knowing the intent of customers, what they want to buy, their likes, and factors that drive them to a particular store. Predictive analytics can assist in forecasting the intent of customers. Predictive analytics involves identifying the possible predictions in the future through the use of statistical methods and machine learning algorithms in the analysis of historical data (Waller & Fawcett, 2013). There are four key issues that corporations can address using predictive analytics. They include fraud detection, optimizing marketing campaigns, improving operations, and reducing risks.

Proposed Solutions

Predictive analytics refers to the extraction of information from existing sets of data with the aim of determining patterns and predicting future outcomes and trends. It focuses on forecasting future probabilities. Predictive models are applied in business in the analysis of current data and historical facts to have a better understanding of customer, products, and partners. The models also assist in identifying potential risks and opportunities for a company. Predictive analytics uses several techniques such as data mining, statistical model, and machine learning to help the analyst in making future business forecasts (Larose, 2015).  Predictive analytics uses a large volume of real-time customer data in predicting future events. Organizations can use big data to shift from a historical perspective to a forward-looking view that focuses on addressing the unique needs of customers and overcoming challenges (Waller & Fawcett, 2013).

Predictive analytics can be used in fraud detection using multiple applications to improve, provide pattern detection and catch criminal behavior. Cybersecurity has emerged to be a growing concern that requires a high-performance behavioral predictive analytics to analyze real-time patterns of behavior on a network with the aim of catching abnormalities that may be an indication of fraud, vulnerabilities, and advanced persistent threats. Optimizing safety across systems and platforms results in the following benefits: reduces the legal work involved in punishing fraud cases; hiring lesser analyst to perform manual investigations; allows companies to scale without limitations of human-labor; provides a competitive edge; helps client feel safe when using products and services of the company (Gandomi & Haider, 2015).

Marketing campaigns can be optimized using predictive models. The use of supervised machine learning techniques involving classification and prediction algorithms ensure that marketing campaigns target the right audience, portray the right message, trigger appropriate call-to-action. Predictive analytics is used in determining and promoting cross-sell opportunities through the use of historical purchasing behaviors and patterns. Companies can be capable of attracting, retaining and growing the most profitable customers through the use of a predictive system (Waller & Fawcett, 2013).

Companies can use predictive models in improving operations. The models assist in forecasting required supply and project demand inventory with the aim of managing resources effectively. Airlines use predictive models in setting prices over the short and long term. Predictive analytics is employed in the hotel sector to forecast the number of guests for any specific night/season with the aim of maximizing the rates of occupancy and increasing sale revenue. The efficiency of a firm can be enhanced through the use of predictive analytics (Gandomi & Haider, 2015).

Predictive analytics has emerged to be of great importance in the reduction of risk for most companies. Financial institution uses the credit score in assessing the likelihood of a buyer to default for purchases of commercial products. Predictive machine generates a credit score by incorporating all the known relevant data about an individual (Gandomi & Haider, 2015).

The performance of the proposed solutions

Predictive analytics is used in determining patterns in historical data and predicting new data using machine learning models. The predictions are defined by a range of probable outcomes of a target variable from the estimated significance of input data. The three main types of predictive models include classification, regression, and neural networks. Classification models are used in separating data into different categories based on particular criteria for each group. In the banking sector, customers who have applied for loans may be put into various categories based on the likelihood of default risk such as low-risk, high-risk and medium,-risk (Larose, 2015). Classification systems are used in providing binary answers as to whether a particular variable belongs to a specific category. It yields an outcome in the form of 0 or 1, whereby 1 being the target event. An excellent example of classification models used in partitioning data into subsets based on input variable categories is the Decision Tree. A decision tree has a tree-like look with branches that represent a choice between several alternatives. Each leaf represents a classification or a decision. The model analyses data with the aim of finding variables that logically splits data into separated groups. The decision tree has emerged to be a popular algorithm since it is easy to understand and interpret. It is useful in handling missing values and preliminary selection of variable (Ratner, 2017).

Linear regression is employed in determining trends in continuous data. Unlike the classification model that separates data into categories, regression models are useful in providing data for identifying a continuous pattern. Regression models can be used in determining the possible revenues from a particular customer segment over a specified period by focusing on historical sales revenues derived from the customer segment. The models are used in estimating the relationships among variables and focuses on continuous data that conforms to the assumption of a normal distribution ( Siegel, 2013). It focuses on finding key patterns in large sets of data and specific factors that can assist in determining such patterns. In linear regression, one independent variable is used in predicting the outcome variable. Two or more predictor variables are used in multiple regression in predicting the outcome variable. Logistic regression entails the use of unknown variables of a discrete variable in predicting the outcome variable by known value of other variables. The response variable is categorical since it assumes a limited number of values. A response variable in binary logistic regression has only two values (0 or 1). A response variable in multiple logistic regression can have several levels like high, medium, and low or 1, 2, 3 (Ratner, 2017).

Neural models are employed in complex non-linear data. They are sophisticated methods that are capable of modeling extremely complex relationships powerfully and flexible. The power comes about due to the ability to handle non-linear relationships which commonly occurs as more data is collected. Neural models are used in confirming findings from simple models such as decision trees and regression. Neural networks function by pattern recognition and artificially intelligent processes that are capable of graphically modeling parameters (Ratner, 2017). The approach works best when there is no known mathematical formula relating to inputs and outputs, in case a lot of training data is required, or when a prediction is considered more essential than explanations. Researchers developed artificial neural networks by mimicking the neurophysiology of the human brain. Other predictive models include Bayesian analysis, gradient boosting, incremental response, K-nearest neighbor (Knn), memory-based reasoning, Partial least squares, principal component analysis, support vector machine, and time series data mining (Kelleher, Mac Namee & D’Arcy,2015).

Comments on the solutions

Several steps are considered crucial in the use of predictive analytics. The first step is defining the problem that needs to be solved. Secondly, the process requires data, and someone with data management experience to assist in cleaning and preparing data for analysis. Preparing data for predictive analysis always requires someone with domain knowledge in a particular industry to help in extracting insight from data. The interpretation of income is determined by how well the target of analysis is defined. One of the most tedious aspects of the analysis process is considered to be data preparation ( Siegel, 2013). Predictive modeling begins after data preparation. A data analyst is required to assist in refining the models, and an IT expert is needed to help in deploying the model. The third step is deployment which entails putting the model to work on chosen data to get results. A team approach is crucial in predictive modeling since different expertise is required to realize success.

Predictive analytics can be used maximizing profitability and productive in industries. Corporations can employ predictive analytics in optimizing their operations, increasing revenue and reducing risks. The industries that use predictive models include the banking and financial sector, retail sector, oil, gas and utility sector, manufacturing, health insurance, and government and the public sector. Predictive analytics is of great importance in the financial industry due to the enormous amounts of data and money at stake. The industry uses predictive models in detecting and reducing fraud, measuring credit risks, retaining valuable customers, and maximizing cross-sell/up-sell (Ratner, 2017). The Commonwealth Bank uses analytics in predicting the probable occurrence of fraud activity for any given transaction before its authorization within 40 seconds of initiating the transaction. Retailers use predictive analytics to determine the type of products to stock and the effectiveness of promotional campaigns. Predictive analytics has been embraced by the energy sector to predict equipment failures, safety mitigation, reliability risks, future resources needs, or improving overall performance.

Predictive analytics assist governments in detecting and preventing fraud, improving service and performance, and having a better understanding of consumer behavior. Analytics is used in enhancing cybersecurity. Predictive models help the insurance sector in detecting claims fraud, as well as identifying patients who are at most risk of chronic diseases and finding the most suitable interventions. Predictive analytics is useful in the manufacturing sectors in determining factors that lead to production failures and reduced quality. Many industries are currently implementing predictive analytics applications with the aim of optimizing business operations.

References

Larose, D. T. (2015). Data mining and predictive analytics. John Wiley & Sons.

Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77-84.

Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144.

Ratner, B. (2017). Statistical and machine-learning data mining: Techniques for better predictive modeling and analysis of big data. Chapman and Hall/CRC.

Siegel, E. (2013). Predictive analytics: The power to predict who will click, buy, lie, or die (p. 148). Hoboken: Wiley.

Kelleher, J. D., Mac Namee, B., & D’Arcy, A. (2015). Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies. MIT Press.

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