Unlock the Benefits of Predictive Analytics: Accurate Predictions for Business Success - Essay Sample

Introduction

Predictive analytics is a science that applies analytical techniques like statistical modeling and machine language alongside historical data to foretell future results. The analytical method has proven to be highly proficient and precise to the extent where adopters have benefitted from it by saving money or earn revenue. Moreover, analytics in businesses is a necessity as the problems associated with transactions are increasingly becoming complex. Thus, the analytical models offer managers across a myriad of industries such as manufacturing, automotive, pharmaceuticals, aerospace, finance with remarkable outcomes as well as confidence in tackling uncertainty in businesses despite the presence of big data (Chahal, Jyoti, & Wirtz, 2019). In life insurance, predictive analytics has been applied in the evaluation of big data to make interpretations or identify significant correlations, and the use of these correlations to better foretell future events. However, due to the nature of the life insurance business, predictive analytics have not been applied as extensively as it involves other industries.

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The main challenge the insurers face is the low number of deaths compared to the policies issued, making it extremely challenging to model any significant statistical deviation in mortality. Thus, the insurers have used predictive analytics mostly in the prevention of fraud, which is under-utilization of the predictive design that has been employed successfully in transforming the customer experience in other industries resulting in the enrichment of the customer experience journey (Willis Towers Watson, 2019). Therefore, predictive analytics is important to enhancing customer experience taking into consideration that most of the customers in the life insurance industry interact with brokers. The customers only get the opportunity to interact with the insurers when they are processing claims. Hence, the insurers need to ensure that the customers' experience during this interaction is of the highest quality.

Purpose and Significance of the Study

This study strives to answer the question, "what is the impact of predictive analytics in the cultivation of customer experience in life insurance?" Thus, the purpose is to identify ways in which the analytical technique works and how best to benefit from the application in improving the customer experience for the profitability of life insurance providers. Therefore, the study will examine how the analytics is important forecasting customer needs, detect factors linked with flight risk, and knowing when to increase or reduce staff. Additionally, the examination will be conducted on the application of an anticipatory service model, maximizing on a superior pricing model, and tailoring customer's experience through real-time interaction with products.

Literature Review

Predictive Analytics

Machine learning and statistical modeling are some of the analytical techniques that are applied alongside historical data to make predictions regarding future outcomes in this data analytics group. The science of predictive analytics makes it simpler to generate precise future insights. Moreover, high caliber analytical tools and models can now be employed by any organization to gain future forecasts on trends and behaviors with the use of current data. The global market is growing extremely fast, generating an approximate income of US$10 billion by 2022 (Van Hasselt, 2019). The analytical method gains its strength from a variety of technologies and techniques that comprise statistical modeling, data mining, big data, and an assortment of arithmetical processes, and machine learning. Consequently, predictive analytics have proven to be very beneficial compared to traditional tools.

The analytical technique has proven to be extremely reliable and accurate to the point where adopters earn and save tons of money using this technology in comparison to traditional tools. For instance, retailers who have adopted the analytical tool enjoy the benefits of managing their shipping schedules, forecast inventory requirements, as well as configure store layouts for optimal sales generation. Otherwise, the explosion of the internet compounded by the transformation in information technology makes the application of predictive modeling inexhaustible (Chahal et al., 2019). In that context, the hospitality industry can foretell occupancy on any given day, an aspect that has enabled them to maximize revenue. The benefits seem to be endless as the technology is applicable in a variety of industries such as manufacturing, automotive, pharmaceuticals, aerospace, finance with remarkable outcomes. There are several predictive modeling techniques, but a few are generic, like neural networks, decision trees, and regression.

Neural Networks

Neural networks are a modeling technique designed to recognize patterns modeled after the human brain. These sets of algorithms interpret sensory data through clustering raw input, labeling, or machine perception. Numerical patterns that contains vectors recognized by these algorithms (Bornacelli, Gutierrez, & Pastrana, 2018). Hence, all kinds of data, such as time series, sound, text, or images, must be translated. Otherwise, the set of algorithms is vital in classification and clustering as they assist in grouping unlabeled data based on similarities as well as classifying dataset with labels. Furthermore, neural networks have proficiency in extracting attributes that are fed to other algorithms for classification and clustering. Thus, they constitute more astronomical machine-learning software comprising algorithms that reinforce learning, regression, and classification.

Classification: Humans need to transfer their knowledge to the dataset to enable neural networks to understand the correlation between the data and the labels, an aspect referred to as supervised learning as all tasks are dependent on labeled datasets (Bornacelli et al., 2018). For instance, neural networks can help in the detection of faces, recognition of gestures in the video, classify spam text, transcribe speech to text, and recognize objects within images, among many other functions.

Clustering: Through grouping, neural networks can detect similarities. Thus, through clustering, neural networks conduct unsupervised learning where deep learning does not need labels to identify comparisons. Datasets with no labels form the bulk of global data; hence, they can generate exact models as the law of machine learning asserts that an algorithm can produce accurate results with more data (Bornacelli et al., 2018). Thus, through clustering, neural networks can detect anomalies and significant trends such as fraud. They can as well compare images, documents, or sounds too similar substances.

Decision trees

Decision trees can be described as structures that resemble the flowcharts or tree-like graph and are used in machine learning to exhibit an algorithm that has conditional control reports only. In machine learning, decision trees cover a wide area that consists of regression and classification (Bornacelli et al., 2018). In the analysis of decisions, decision trees represent explicitly and visually resolutions and decision making. In comparison to other algorithms, tree-based learning algorithms are highly regarded and used extensively in supervised learning methods. Moreover, they empower predictive models with high precision, simplicity in interpretation, and stability (Bornacelli et al., 2018).

In contrast to linear models, these can be used to map non-linear correlations perfectly well as they are adaptable at resolving any difficulty, is it regression or classification. Some of the terminologies highly associated with decision trees consist of:

• Root Node: This is split into two or more homogeneous sets as they represent a section of a sample or entire populations.
• Terminal/Leaf Node: A node in a decision tree that carries no child nodes.
• Parent and Child Node: When a node is split, it gives rise to sub-nodes referred to as child nodes while the node becomes the parent node.
• Decision Node: When the child node splits, it gives rise to sub-nodes known as decision nodes.
• Sub-Tree/Branch: This is a section of a whole tree.
• Pruning: This is the act of removing sub-nodes from a decision node.
• Splitting: This is the act of separating a node into sub-nodes.

Regression

Prediction of continuous values is mostly applicable bythe use of regression models, which are a form of predictive modeling technique that looks into the correlation of independent and dependent variables. Otherwise, regression models are vital as they are used to reveal the intensity of an impression of several independent variables on a dependent variable (Bornacelli et al., 2018). There are several types of regression models that have their benefits and drawbacks, even though logistic and linear regressions are the initial modeling algorithms learned in data science and machine language. The two models are renowned for their inherent simplicity as they are uncomplicated; hence, easy to interpret (Bornacelli et al., 2018). However, the simplicity of the two models has a myriad of setbacks, making them unattractive to users. Examples of regression models include:

• Linear Regression: This regression model determines a correlation between a dependent variable and one or more independent variables using a regression line known as the best fit straight line. The regression is represented by the formula y' = a + bx.
• Logistic Regression: This model applies mostly when the dependent variable is a binary type (Bornacelli et al., 2018). Thus, the regression model is suitable for finding the probability of event = failure and event = success as it employs a non-linear log transformation. The equation is represented as
• Polynomial Regression: In this model, the power of the independent variable is greater than 1; hence, the best fit line is not straight but rather a curve that fits the plotting. Polynomial regression is best described as follows:
• Stepwise Regression: The model is applicable in situations where there are several independent variables and aims at maximizing the strength of the forecast with the least number of predictor variables (Bornacelli et al., 2018). The technique is appropriate in handling data sets of higher dimensionality. This regression, s represents the dependent variable's standard deviations while the independent variable is represented by j, as shown below.
• Ridge Regression: When the independent variables are extremely correlated (multicollinearity), the ridge regression model is applied.
• Lasso Regression: This model is similar to the ridge regression model as it reprimands the entire scope of the regression coefficients (Bornacelli et al., 2018). Furthermore, the model can reduce the variability and refine the precision of the linear regression techniques.
• ElasticNet Regression: This model is the integration of the...