Predictive analytics techniques and applications 

The distinction between predictive modeling and predictive analytics is subtle. It’s so subtle, in fact, that the two terms tend to be used synonymously, but they are not the same. While the former is concerned with creating models for forecasting, the latter encompasses a much broader scope, extracting insights and integrating predictive data into business operations and strategy. This more-encompassing approach is essential for companies looking to thrive in today's uncertain business circumstances. 

A key aspect of predicitve analytics is the application of artificially intelligent solutions. Coupled with the latest AI and ML tools, this predictive intelligence empowers companies to discover patterns and relationships in even the largest, most complex data sets, as well as automate many aspects of predictive analytics. 

The importance of predictive analytics is anchored in its ability to mine historical data, identify patterns, and predict future trends, thus allowing your business to approach the future with enhanced visibility and assurance. This carries with it clear advantages for your organization. 

Improved decision making

As the name suggests, at the core of predictive analysis is its ability to predict. With a clearer view of historical data backed by AI, ML, and other forms of predictive intelligence you gain the reliable, accurate information needed to evaluate and anticipate future circumstances and customer actions. This displaces much of the guesswork involved in creating a business strategy, while also effectively eliminating human-introduced errors in your data. The end result is a better foundation for making vital decisions.

Enhanced risk management

Predictive analytics allows organizations to identify potential risks before they become real problems, giving companies more breathing room to formulate proactive mitigation strategies. From credit risks in the financial sector to supply chain disruptions in manufacturing, predictive models pinpoint vulnerabilities and devise counteractive measures, safeguarding operations against unforeseen threats.

More-accurate threat detection

Predictive analytics acts as a sentinel against cybercriminals and other threat actors, identifying anomalous patterns and potential intrusions early so that they can be quickly resolved. Such proactive threat detection enables organizations to fortify their cybersecurity defenses, safeguarding data and digital assets against potential breaches.

Clearer customer insights

Predictive analytics also unravels customer behaviors, preferences, and trends, producing a clear understanding of who your buyers are and what actions they might take in specific situations. By analyzing historical customer data, your business can anticipate future buying behaviors, enabling you to tailor your marketing strategies, enhance your customer experience, and foster loyalty by aligning your offerings with the customers’ expectations.

Increased efficiency

In an increasingly competitive business environment, predictive analytics aids in identifying operational bottlenecks, forecasting demand, optimizing supply chains, and streamlining processes, thereby bolstering operational efficiency and enhancing the organization’s overall productivity.

Greater competitive advantage

Further, predictive analytics provides a distinct edge to organizations, enabling them to foresee trends, anticipate customer needs, and innovate proactively. Businesses can craft personalized customer experiences, optimize pricing strategies, and take corrective actions ahead of market changes, thereby doing more than simply keeping up with competitors.

While the two concepts (predictive modeling and predictive analytics) may not be the same, modeling is still an essential part of the predictive analysis process. The engine at the heart of predictive analytics is a mathematical model that processes and applies data to current and future scenarios, making analysis and forecasting possible.

This process begins with predictive models employing historical data and known results as a foundation on which future predictions will be built. These models are trained to discern patterns and relationships within the data, establishing a sturdy framework capable of predicting values or events relevant to new data. Essentially, these models can anticipate and interpret potential future events based on the observed and learned patterns.

Predictive analytics models

Generally speaking, predictive models fall into one of two categories: 

• Regression models (such as those used to predict revenue from a specific customer) focus on forecasting a continuous outcome, delivering a numeric prediction derived from the analysis of input variables. 
  

• Classification models (such as those used to evaluate credit risks or segment customers for marketing campaigns) evaluate complex sets of known data and predict the correct label or category into which new data should be classified. 

More precisely, predictive models may target very specific kinds of forecasting. Among the most widely used models are the following:

• Forecast model 
  The forecast model in predictive analytics is used to scrutinize patterns, fluctuations, and correlations within past data to predict future points. Especially pertinent in finance, retail, and weather prediction, forecast models rely on multiple input parameters to aid in anticipating sales, revenue, demand, and other vital metrics, thereby steering strategic planning and resource allocation. 
  

• Clustering model 
  Clustering models operate by identifying and grouping data points that share similar characteristics—without being explicitly programmed on what to look for. Clustering models are pivotal in market segmentation, where customer data is grouped into segments sharing similar purchasing behavior or preferences. This facilitates targeted marketing and product development strategies. 
  

• Time-series model 
  Time-series models analyze data points collected or recorded at a regular time interval. The crux of these models lies in understanding the underlying internal structures (such as trends, seasonal variations, and cyclic behaviors) within the time-sequenced data. From forecasting stock prices to predicting energy demand, time series models offer invaluable insights by deciphering patterns evolving over time and predicting future values in the series. 
  

• Outliers model 
  The outliers model spotlights anomalies, uncovering data points that deviate significantly from the established norm. By identifying these deviations, these models assist in detecting fraud, network breaches, and system failures. In contexts such as credit card transactions, an outliers model can flag unusual spending patterns, thereby initiating fraud alerts and safeguarding financial assets.

Predictive algorithms

At the center of every predictive model is a predictive algorithm that defines the mathematical process on which the model is built. In most cases, these algorithms are based on either machine learning or deep learning technologies. 

Machine learning vs. deep learning

Machine learning encompasses algorithms that deal with structured, tabular data, providing a spectrum of both linear and nonlinear algorithms—each catering to varied computational efficiency and problem complexity. ML is notable for its application in scenarios where relationships between data points are either direct and proportional (called linear algorithms, which tend to train more quickly) or that involve more complex, non-proportional interactions (known as nonlinear algorithms, which are usually better optimized).

Deep learning is actually a subset of machine learning that is capable of delving deeper into data analysis, specifically excelling in managing unstructured data (such as images, text, video, and audio) through layered neural networks. 

Types of predictive algorithms

Just as there are several predictive models that play key roles in predictive analysis, there are a number of predictive algorithms that can be categorized into the following types:

• Linear regression model 
  Although somewhat limited in terms of scope and application, the linear regression model is useful for predicting a dependent variable based on one or multiple independent variables. Despite its limitations, it is still widely utilized due to its simplicity and efficiency in predicting certain outcomes and forecasting possible trends. This is the most basic form of predictive analysis. 
  

• Generalized linear model (GLM) 
  A flexible generalization of ordinary linear regression models, GLMs allow for response variables with error distribution models other than a normal distribution. This allows the predictive model to narrow down the variables at play, zeroing in on the most important factors and more accurately defining their impact. GLMs combine a specific probability distribution function, a linear predictor, and a link function, catering to various types of data distribution and establishing a relationship between the mean of the dependent variable and the predictors.  
  

• Random forest 
  Random forest thrives in handling both regression and classification tasks, providing insights into the importance of different features in making predictions. As such, it is perhaps the most popular algorithm in use in predictive analytics. Random Forest operates as an ensemble learning method, wherein multiple decision trees are constructed during training, and output is averaged (for regression problems) or mode is taken (for classification problems) to improve predictive accuracy and control over-fitting.  
  

• Gradient boosted model 
  Gradient boosting involves building sequential decision trees where each subsequent tree aims to correct the errors of its predecessor (usually applied to regression and classification problems). This algorithm excels in predictive accuracy by combining the predictive power of multiple base estimators. Although accurate and reliable, these algorithms may require careful parameter tuning to be effective. 
  

• K-means 
  K-means is an iterative clustering algorithm, strategically partitioning datasets into 'K clusters,' where each data point belongs to the cluster with the nearest mean. This algorithm is particularly valuable in segmenting data into distinct, non-overlapping subgroups where members share common traits. K-means is widely utilized in market segmentation, document clustering, and image segmentation due to its simplicity and efficiency. 
  

• Prophet 
  Developed by Facebook, Prophet is designed for forecasting using daily observations that display patterns on different time scales, and is effective at handling missing data and outliers. Primarily used for time-series forecasting of data exhibiting strong seasonal patterns, Prophet adapts to data with regular and irregular patterns, providing precise forecasts.

Although every data-analysis journey is different, the process usually follows a similar sequence of steps:

1. Identifying the problem 
   The inaugural step in predictive analytics is pinpointing the problem or question you seek to address. This involves defining what you aim to predict or understand based on historical data, and discerning how the subsequent predictions will fuel informed decision-making within the organization. 
   

1. Collecting the data 
   Armed with a clearly defined problem, your next phase involves gathering pertinent data. In the digital age, this implies gathering information from a range of sources, such as transactional systems, sensor data, third-party information, and weblogs. The depth and breadth of the data being collated should be expansive enough to give the predictive model a deep well of information from which to draw accurate and reliable conclusions. 

1. Preparing the data 
   Once the data is in hand, the most critical phase of data preparation begins. This involves a meticulous process of cleansing and organizing the data, which is frequently cited as one of the most time-intensive aspects of the predictive analytics process. Utilizing the expertise of a data management professional (sometimes called a "data wrangler") helps ensure that the data is not only free from inconsistencies and inaccuracies but is also appropriately structured for subsequent analysis. 

1. Building the predictive model 
   With the cleaned and organized data serving as your foundation, the construction of the predictive model can begin. Model-building software greatly simplifies this step, but you may still wish to involve an experienced analyst in refining your models and identifying the ones that best align with the identified problem and available data. 

1. Interpreting the data and applying the learnings 
   The final step in this process is not just about extracting insights from the predictive model but also ensuring that these insights can be applied to help inform actionable strategies. This involves interpreting the data, understanding the predictions, and subsequently, crafting strategies that apply these learnings in a manner that propels the organization toward its established objectives.

The ability to predict future events using available data is an opportunity that is simply too good to pass up, which is why predictive analytics is used to inform strategies and enhance processes in organizations across every industry and geographic location. 

Although this is certainly not an exhaustive list, the following industries show how even the most varied businesses can benefit from intelligent predictive analytics:

• Banking and financial services 
  Banks leverage predictive models to assess credit risks, devise investment strategies, detect fraudulent activities, and personalize customer engagement—enhancing both financial stability and customer satisfaction. 
  

• Retail 
  For retail, predictive analytics helps retailers forecast demand, optimize pricing, personalize marketing strategies, manage inventory, and enhance customer experiences, aligning offerings with consumer expectations and market trends. 
  

• Energy and utilities 
  In the energy sector, predictive analytics fuels optimized operations and strategic planning. It facilitates precise demand forecasting, enables predictive maintenance of infrastructures, aids in risk management, and is useful in devising energy trading and pricing strategies. This helps ensure sustainability and profitability in an extremely dynamic market. 
  

• Government  
  Governments deploy predictive analytics to enhance public services and policy planning. Through predictive models, governments can optimize resource allocation, enhance public safety using predictive policing, streamline public health initiatives, and formulate policies that are in sync with future socio-economic trends. 

• Health care 
  In healthcare, predictive analytics fortifies patient care and operational efficiency. It enables healthcare providers to predict disease outbreaks, optimize patient flow, enhance patient care with personalized treatment plans and monitoring, and manage resources effectively, ensuring a balance between optimal healthcare delivery and operational sustainability.

• Manufacturing 
  Predictive analytics propels operational efficiency and quality assurance in manufacturing. It facilitates precise demand forecasting, predictive machinery maintenance, reliable supply chain management, and compliance with quality standards, thus reducing downtimes and ensuring smooth operational flows.