What is Predictive Analytics?
Predictive analytics involves leveraging statistical methods and modelling techniques to foresee future outcomes and performances. By analysing both present and historical data patterns, predictive analytics aims to determine the likelihood of these patterns reoccurring.
This strategic approach empowers businesses and investors to strategically allocate resources, capitalising on potential future events. Moreover, predictive analysis contributes to enhancing operational efficiencies and mitigating risks.
How Predictive Analytics Works
Predictive analytics is a technological approach aimed at forecasting unknown future events or outcomes. It leverages various methodologies such as artificial intelligence (AI), data mining, machine learning, modelling, and statistical analysis. For example, data mining involves sifting through extensive datasets to identify patterns, while text analysis does the same with large blocks of textual content.
Predictive models can be used in diverse fields, including weather forecasting, video game development, speech-to-text translation, customer service, and investment portfolio strategies. These models rely on descriptive statistical analyses of existing data to predict future trends and patterns.
Businesses benefit significantly from predictive analytics, using it to optimise inventory management, design effective marketing strategies, and forecast sales trends. In competitive industries like healthcare and retail, predictive analytics becomes a critical tool for survival. Moreover, investors and financial experts harness this technology to tailor investment portfolios and mitigate risks.
By uncovering relationships, patterns, and structures within data, predictive models draw conclusions regarding how alterations in the underlying processes might impact outcomes. These models build upon descriptive analyses of historical data, determining the likelihood of future outcomes based on current or anticipated conditions.
Uses of Predictive Analytics
Predictive analytics serves as a pivotal decision-making tool across various industries.
Forecasting
Forecasting holds importance in manufacturing as it facilitates the efficient use of resources within a supply chain. Key components of the supply chain, such as inventory management and shop floor operations, rely on precise forecasts for functioning.
The process commonly involves predictive modelling, employed to refine and enhance the quality of data used in these forecasts. Through modelling, the system can handle a broader range of data sources, including inputs from customer-facing operations, resulting in more reliable and accurate forecasts.
Credit scoring
Credit scoring extensively uses predictive analytics. When an individual or business seeks credit, their credit history and the credit records of comparable borrowers are analysed to forecast the likelihood that the applicant might default on any extended credit.
Underwriting
Data and predictive analytics are pivotal in the underwriting process. Insurance firms analyse policy applicants to assess the probability of future claim payouts, drawing from the current risk pool of similar policyholders and past claim events. Actuaries regularly employ predictive models that analyse applicant characteristics vis-à-vis historical data on past policyholders and claim settlements.
Marketing
Professionals in the marketing industry analyse consumer responses to the broader economy to strategise new campaigns. By leveraging demographic changes, they assess whether the existing product lineup will appeal to consumers and drive purchases.
On the other hand, active traders employ diverse metrics rooted in historical events to guide their decisions on buying or selling securities. Using tools like moving averages, bands, and breakpoints derived from past data, they forecast future price movements in the market.
Fraud Detection
Fraud Detection in financial services involves leveraging predictive analytics to analyse transactions, trends, and behavioural patterns. Any unusual or suspicious activity triggers an investigation by the institution to identify potential fraudulent behaviour. This involves analysing inter-account activities and transaction timing to uncover potential irregularities.
Supply Chain
Supply chain analytics involves the prediction and management of inventory levels and pricing strategies. Using historical data and statistical models, predictive analytics within the supply chain forecast future performance, demand patterns, and potential disruptions.
This proactive approach helps businesses in identifying and mitigating risks, optimising resources and processes, and enhancing decision-making. These measures enable companies to anticipate available materials at any given time and preemptively address potential shortages.
Human Resources
Human resources uses predictive analytics to improve different processes, including predicting future workforce demands and necessary skill sets, as well as examining employee data to identify which influences high turnover rates.
Predictive analytics also evaluates an employee’s performance, skills, and preferences to anticipate their career trajectory, aiding in career development planning. Moreover, it assists in forecasting diversity and inclusion initiatives.
Predictive Analytical Models
Three commonly employed techniques in predictive analytics include decision trees, regression, and neural networks. Further details about each of these are provided below.
Decision Trees
For those seeking insights into what influences decision-making, exploring decision trees can be immensely beneficial. This model organises data into distinct segments determined by specific variables, like price or market capitalisation. Resembling a tree with branches and leaves, decision trees visually display available choices through branches and individual decisions via leaves.
Renowned for simplicity, decision trees are easy to comprehend and analyse. Moreover, they prove highly advantageous when quick decision-making is needed.
Regression
This model is extensively employed in statistical analysis, particularly when seeking patterns within extensive datasets that exhibit a linear relationship among the inputs. It operates with a formula presenting the relationship among all the identified inputs within the dataset.
Neural Networks
Neural networks were conceived to emulate the human brain’s functionality for predictive analytics. This model harnesses artificial intelligence (AI) and pattern recognition to navigate complex data relationships. It proves invaluable when faced with multiple challenges such as handling extensive datasets, lacking a specific formula to establish input-output relationships, or prioritising predictions over explanatory analysis.
Cluster Models
Clustering refers to the technique of grouping together data with comparable attributes. Take, for instance, a major online retailer such as Amazon. Amazon has the capability to cluster sales according to the volume purchased or group them based on the average account age of its consumers. By categorising data into cohesive groups based on common characteristics, analysts can potentially uncover additional traits that identify patterns or behaviours.
Time Series Modelling
At times, data is interconnected with time, and particular predictive analytics hinge on understanding the correlation between events and their timing. Such models evaluate inputs at defined intervals like daily, weekly, or monthly cycles.
Subsequently, analytical models identify seasonality, trends, or behavioural patterns associated with timing. This predictive model proves valuable in forecasting peak periods for customer service demands or predicting specific sales occurrences.
Benefits of Predictive Analytics
Predictive analysis offers many advantages. It serves as a valuable tool when predictions need to be made where conventional solutions are lacking.
For investors, financial experts, and business leaders, predictive models are instrumental in mitigating risks. For instance, an investor and their advisor can leverage specific models to curate an investment portfolio that minimises risk factors, considering variables like age, capital, and objectives.
Employing predictive models significantly impacts cost reduction. Businesses can forecast the potential success or failure of a product pre-launch. Moreover, predictive techniques allow them to allocate resources to improve production, aiding in optimising the manufacturing process.
Criticism of Predictive Analytics
Using predictive analytics has faced criticism and, in certain instances, legal constraints owing to the perceived inequalities in its results. Typically, these concerns arise from predictive models that exhibit statistical bias against particular racial or ethnic groups, particularly in domains like credit scoring, home lending, employment, or assessments of criminal behaviour.
An example is, now deemed illegal, redlining in home lending by financial institutions. Regardless of the accuracy of the predictions generated by such analytics, their application is generally disapproved of. As a result, datasets that explicitly incorporate factors such as an individual’s race are increasingly excluded from predictive analytics practices.
What is Machine Learning?
Machine learning refers to the ability of a computer program to autonomously learn and adjust to new information without requiring direct human intervention. This branch of artificial intelligence (AI) ensures that a computer’s inherent algorithms remain updated, enabling adaptability despite fluctuations in the global economy.
Predictive Analytics vs. Machine Learning
There’s a misunderstanding regarding the distinction between predictive analytics and machine learning. Predictive analytics involves assessing past data to comprehend potential future events. It encompasses different statistical methodologies such as machine learning, predictive modelling, and data mining, using historical and current statistics to forecast forthcoming outcomes.
Machine learning constitutes a subset of computer science. Arthur Samuel, an eminent figure in computer gaming and artificial intelligence, defined it in 1959 as “the programming of a digital computer to behave in a way which, if done by human beings or animals, would be described as involving the process of learning.”
How Businesses can use Predictive Analytics
As highlighted earlier, predictive analysis holds significant potential across various applications. Businesses can leverage predictive models to advance their objectives and enhance their operational efficiency. These models are commonly employed to improve customer service strategies and outreach efforts.
Business executives and owners can harness such statistical analysis to understand customer behaviour. For instance, a business proprietor can use predictive techniques to discern and target loyal customers who might be at risk of switching to a competitor.
Predictive analytics assumes a pivotal role in advertising and marketing. Companies use these models to determine which customers are more likely to positively engage with marketing and sales campaigns. By targeting specific customer segments that exhibit positive responsiveness, business owners can save money, steering away from generalised campaigns.
An Example of Predictive Analytics
Data collection plays a pivotal role in the operations of companies like Netflix. By gathering information on customer behaviour and historical viewing habits, Netflix tailors recommendations to align with individual preferences. This personalised approach provides tailored suggestions based on your interests.
Summary
The objective of predictive analytics is to forecast future events and use these forecasts to enhance decision-making processes. Its applications span across various industries like finance, healthcare, marketing, and retail. Predictive analytics employs diverse methodologies such as decision trees, regression, and neural networks.
DISCLAIMER: This article is for informational purposes only and is not meant as official business advice. AVANTE PARTNERS does not condone, endorse, or disparage predictive analytics as a business practice, and has no ties with any company mentioned in this article.