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Corporations typically hire analysts, marketing and financial researchers, data modeling experts, and other such professionals to conduct manual work in adapting research to a continuously evolving economic and business environment.
Using human labor to decipher the complex economic system is both generally a costly method of R&D and, more importantly, it is largely characterized by a systematic error that is based on subjective human discretion and interpretation. Predictive analytics solves this problem by providing computationally accurate results and interpretations.
In this article, we will explain what predictive analytics is both in general and technical terms, reveal their applications to corporations, and conclude with an outlook on predictive analytics.
There are plenty of examples in the modern corporate world showing the power and influence of predictive-analytics applications for large corporations, and we wish to show how corporations can benefit from predictive analytics consulting.
Explanation of Predictive Analytics
In an age where data dominates everyday life, modern corporations are turning to predictive analytics consulting to solve real-world problems. Volumes and types of data are growing exponentially while there is a growing interest in using data to produce valuable business insights; computers are becoming faster and cheaper with time while a variety of user-friendly software is rapidly increasing; in this atmosphere of rapid technological change, businesses realize that being profitable does not just mean reducing costs.
Corporations need to adapt to tough economic conditions through systematic competitive differentiation and this can be done much better with the use of predictive analytics.
Predictive Analytics is the identification of likely outcomes or possible predictions in the future by using statistical methods and machine learning algorithms to conduct analysis based on historical data. The objective is to not merely identify trends based on past information, but rather to assess systematically what is likely to happen.
Potential Benefits of using Predictive Analytics
The term “predictive analytics” may sound like complex technology that is used only by big multinational corporations. In fact, predictive analytics can provide an edge to all corporations, no matter the firm’s size or business model. A sample of potential benefits includes, but are not limited to
Detecting fraud. Using multiple predictive analytics applications can improve, or even provide, pattern detection and catch criminal behavior. As cybersecurity is a growing concern, high-performance behavioral predictive analytics analyzes all behavioral patterns on a network in real-time to catch abnormalities that may indicate fraud, vulnerabilities and advanced persistent threats. Optimizing the safety of your systems and platform can not only reduce the legal work of punishing fraud cases, hire lesser analysts to conduct manual investigations, but predictive analytics also allows your firm to scale without human-labor limits and can provide an edge in your business and helping clients feel safe using your product and services.
Optimizing marketing campaigns. Want to ensure that your marketing campaign is targeting the right audience, bringing about the right message, and efficiently promoting an appropriate call-to-action? Predictive analytics uses historical purchasing behaviors and patterns, and determine and promote cross-sell opportunities. Predictive systems help corporations attract, retain and grow their most profitable customers.
Improving operations. Predictive Analytics can forecast the required supply and project demand inventory to effectively manage resources. Airlines use predictive models to set ticket prices over the short and long term. Hotels use predictive analytics to forecast the number of guests for any given night or season to maximize occupancy rates and increase sales revenue. Predictive analytics improves firms’ efficiency.
Reducing risk. At financial institutions, credit scores are used to assess a buyer’s likelihood of default for purchases of financial products and are a well-known example of predictive analytics applications. A credit score is a number generated by a predictive machine that incorporates all known data relevant to a person’s ‘creditworthiness’. Other such examples of risk-related uses include insurance claims and collections.
A Deeper Understanding of Predictive Analytics
We shall now explain what “predictive analytics” means since it should be now clear the benefits that can be derived as a result of being a user of predictive models. Predictive Analytics is a software or program that uses machine-learning models to determine patterns in historical data (i.e. train data) to predict new data (i.e. produce predictions).
Such predictions represent a range of probable outcomes of a target variable (e.g. sales revenue) based on the estimated significance of input data. There are three main kinds of predictive models, which are namely classification models, regression models, and neural networks.
Classification models separate data into different categories based on specified criteria for each category. For example, banks may classify customers who have applied for loans into different categories of the likelihood of default risk – say three categories of high-risk, medium-risk, and low-risk. In general, classification models will provide binary answers as to whether the variable of interest belongs to the specified category, thus producing results in the form of 0 or 1, with 1 being the event you are targeting.
Decision trees are an example of classification models that partition data into subsets based on categories of input variables. A decision tree looks like a tree with each branch representing a choice between several alternatives, and each leaf represents a classification or decision. This model analyzes the data to find the variable(s) that splits the data into logically separated groups that are the most different. Decision trees are popular because they are easy to understand and interpret. They also handle missing values well and are useful for preliminary variable selection.
Regression models determine trends in continuous data. That is, rather than trying to separate data into categories, regression models use all provided data to determine a continuous pattern. For example, regression models can determine the likely revenue coming from a customer segment over the next month by looking at historical sales revenues coming from this customer segment. Regression models attempt to estimate relationships among variables, and are intended for continuous data that can be assumed to follow a normal distribution, and finds key patterns in large data sets and find out how many specific factors can help determine such pattern(s). With linear regression, one independent variable is used to explain and/or predict the outcome of the variable of interest. Multiple regression uses two or more independent variables to predict the outcome of the variable of interest. With logistic regression, unknown variables of a discrete variable are predicted based on the known value of other variables. The response variable is categorical, meaning it can assume only a limited number of values. With binary logistic regression, a response variable has only two values such as 0 or 1. In multiple logistic regression, a response variable can have several levels, such as low, medium and high, or 1, 2 and 3.
Neural networks are sophisticated methods that are able to model extremely complex relationships as they are both powerful and flexible. The power comes in their ability to handle nonlinear relationships in data, which is increasingly common as we collect more data. They are often used to confirm findings from simple techniques like regression and decision trees. Neural networks are based on pattern recognition and some artificially intelligent processes that graphically “model” parameters. They work well when no mathematical formula is known that relates inputs to outputs, the prediction is more important than explanation, or there is a lot of training data. Artificial neural networks were originally developed by researchers who were trying to mimic the neurophysiology of the human brain. Other popular predictive models include
Bayesian analysis. Bayesian methods treat parameters as random variables and define probability as "degrees of belief" (that is, the probability of an event is the degree to which you believe the event is true). When performing a Bayesian analysis you begin with a prior belief regarding the probability distribution of an unknown parameter. After learning information from data you have, you change or update your belief about the unknown parameter.
Ensemble models. Ensemble models are produced by training several similar models and combining their results to improve accuracy, reduce bias, reduce variance and identify the best model to use with new data.
Gradient Boosting. This tool is a boosting approach that resamples your data set several times to generate results that form a weighted average of the resampled data set. Like decision trees, boosting makes no assumptions about the distribution of the data. Boosting is less prone to overfitting the data than a single decision tree, and if a decision tree fits the data fairly well, then boosting often improves the fit. (Overfitting data means you are using too many variables and the model is too complex. Underfitting means the opposite – not enough variables and the model is too simple. Both reduce prediction accuracy.)
Incremental response (also called net lift or uplift models). These model the change in probability caused by an action. They are widely used to reduce churn and to discover the effects of different marketing programs.
K-nearest neighbor (knn). This is a nonparametric method for classification and regression that predicts an object’s values or class memberships based on the k-closest training examples.
Memory-based reasoning. Memory-based reasoning is a k-nearest neighbor technique for categorizing or predicting observations.
Partial least squares. This flexible statistical technique can be applied to data of any shape. It models relationships between inputs and outputs even when the inputs are correlated and noisy, there are multiple outputs, or there are more inputs than observations. The method of partial least squares looks for factors that explain both response and predictor variations.
Principal component analysis. The purpose of principal component analysis is to derive a small number of independent linear combinations (principal components) of a set of variables that retain as much of the information in the original variables as possible.
Support vector machine. This supervised machine learning technique uses associated learning algorithms to analyze data and recognize patterns. It can be used for both classification and regression.
Time series of data mining. Time series data is time-stamped and collected over time at a particular interval (sales in a month, calls per day, web visits per hour, etc.). Time series data mining combines traditional data mining and forecasting techniques. Data mining tools such as sampling, clustering, and decision trees are applied to data collected over time with the goal of improving predictions.
How to use Predictive Analytics
Step 1: The first thing you need to get started using predictive analytics is a problem to solve. What do you want to know about the future based on the past? What do you want to understand and predict? You’ll also want to consider what will be done with the predictions. What decisions will be driven by insights? What actions will be taken?
Second, you’ll need data. In today’s world, that means data from a lot of places. Transactional systems, data collected by sensors, third-party information, call center notes, weblogs, etc. You’ll need a data wrangler, or someone with data management experience, to help you cleanse and prep the data for analysis.
To prepare the data for a predictive modeling exercise also requires someone who understands both the data and the business problem. How you define your target is essential to how you can interpret the outcome. (Data preparation is considered one of the most time-consuming aspects of the analysis process. So be prepared for that.)
Step 2: After that, the predictive model building begins. Increasingly easy-to-use software means more people can build analytical models. But you’ll still likely need some sort of data analyst who can help you refine your models and come up with the best performer. And then you might need someone in IT who can help deploy your models. That means putting the models to work on your chosen data – and that’s where you get your results.
Step 3: Predictive modeling requires a team approach. You need people who understand the business problem to be solved. Someone who knows how to prepare data for analysis. Someone who can build and refine the models. Someone in IT to ensure that you have the right analytics infrastructure for model building and deployment. And an executive sponsor can help make your analytic hopes a reality.
Applications of Predictive Analytics For Large Companies
Now that we have both a general and technical understanding of what predictive analytics is, we shall explain a sample of business applications. Which business application of predictive analytics is best for your firm is a strategic question, and this depends on the type of business process for machine-learning predictive automation.
The table below lists different kinds of business applications. The first column names the business process of interest for automation, and the second column states the variable which is to be predicted.
There are many other kinds of applications of predictive analytics, including collections, supply chain optimization, human resource decision support for recruitment and human capital retention, and market research survey analysis.
Which Industries use Predictive Analytics?
Any industry can use predictive analytics to optimize their operations, increase revenue and reduce risks. Examples of industries that use predictive analytics include:
Banking and Financial Services
The financial industry, with huge amounts of data and money at stake, has long embraced predictive analytics to detect and reduce fraud, measure credit risk, maximize cross-sell/up-sell opportunities and retain valuable customers. Commonwealth Bank uses analytics to predict the likelihood of fraud activity for any given transaction before it is authorized – within 40 milliseconds of the transaction initiation.
Since the now infamous study that showed men who buy diapers often buy beer at the same time, retailers everywhere are using predictive analytics to determine which products to stock, the effectiveness of promotional events and which offers are most appropriate for consumers. Staples analyzes consumer behavior to provide a complete picture of their customers, and realized a 137 percent ROI.
Oil, Gas and Utilities
Whether it is predicting equipment failures and future resource needs, mitigating safety and reliability risks, or improving overall performance, the energy industry has embraced predictive analytics with vigor. Salt River Project is the second-largest public power utility in the US and one of Arizona's largest water suppliers. Analyses of machine sensor data predict when power-generating turbines need maintenance.
Governments and the Public Sector
Governments have been key players in the advancement of computer technologies. The US Census Bureau has been analyzing data to understand population trends for decades. Governments now use predictive analytics like many other industries – to improve service and performance; detect and prevent fraud; and better understand consumer behavior. They are also using predictive analytics to enhance cybersecurity.
In addition to detecting claims fraud, the health insurance industry is taking steps to identify patients most at risk of chronic disease and find what interventions are best. Express Scripts, a large pharmacy benefits company, uses analytics to identify those not adhering to prescribed treatments, resulting in a savings of $1,500 to $9,000 per patient.
For manufacturers, it is very important to identify factors leading to reduced 5 quality and production failures, as well as to optimize parts, service resources and distribution. Lenovo is just one manufacturer that has used predictive analytics to better understand warranty claims – an initiative that led to a 10 to 15 percent reduction in warranty costs.
Success Story in the Wealth Management Industry
“99% of actively managed US equity funds underperform… [and] failed to beat the S&P 500 over the past 10 years”, according to an article in the Financial Times.
According to data from BarclayHedge, research has shown that portfolio performance has been decaying from an average annual return of 17% to about 3% over an 18-year period from 1997 to 2015. While some mutual funds and other traditional investment firms boast consistent portfolio returns between 5-8% annually, a recent emergence in firms specializing in artificially-intelligent or machine-learning algorithms are averaging much higher returns.
According to EurekaHedge, a database that provide industry information about hedge funds globally, AI machine-learning hedge (i.e. hedge funds that integrate predictive analytics into their investment portfolio strategies), “… have outperformed [traditional investment firms] since 2010, delivering annualised returns of 8.44% over this period compared with 2.62%, 1.62% and 4.27% for [systematic funds], trend-followers and the average global hedge fund respectively.
For the very volatile year ending 2016, AI/machine learning hedge funds are up 5.01%, ahead of the average global hedge fund which gained 4.48% and their peers…” It is known among many modern investment firms that machine-learning approaches solve problems associated with emotional and systematic error because of human-manual investment modeling.
Summary & Takeaways
Many industries to date have been implementing predictive analytics applications to optimize business processes. Some benefits of using predictive analytics include:
- Detecting fraud. By being able to computationally determine suspicious behavioral patterns in real-time, your firm can fight criminal behavior and helping clients feel safe using your product and services.
- Optimizing marketing campaigns to ensure that your campaign is targeting the right audience and producing the results which were intended by your firm.
- Improving operations. Predictive Analytics can forecast required supply and demand to effectively manage resources and hence improve operational efficiency.
- Reducing risk. Predictive Analytics helps to systematically reduce risk by using quantitative methods to produce risk rankings. Predictive Analytics in fact has a wide range of applications; a sample of industries that currently use predictive models include financial institutions, retail firms, oil, gas and utilities companies, governments and public firms, health insurance companies, and manufacturing firms. In fact, firms in the wealth management industry that integrated predictive analytics into their portfolios have consistently outperformed their peers by two if not almost three times!
If you are a company that would like to develop in-house products and services, and would like to integrate predictive analytics into your products and services, here is a table of machine-learning platforms that can provide such integration capabilities:
Each provide different kinds of predictive analytics but a good suggestion is to solicit professional advise from a data scientist who can explain to you which kind of predictive analytics is most suited to your particular products and services. You may hence wish to solicit consultant services from one or a few of either 1QBit or RS Consulting. Alternatively, if you want a professional to work with you one-on-one, Expert360 is what you need to hire a consultant to get the job done. Start your journey by hiring freelancers here, or apply to become an Expert360 consultant.
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