Predictive Analytics – For making smart decisions with data

Predicting the future is not an easy task. Predictive analytics is the process of condensing large volumes of data into information that humans can understand and use easily. It is an area of statistics that deals with extracting information from data, analyzing individual cases, and using it to predict trends and behavior patterns of some relevant characteristic.

It uses data, statistical algorithms, and machine learning techniques to predict future outcomes based on historical data. Its goal is to provide the best assessment of what will happen in the future through the creation of predictive systems and models. Predictive web analytics helps predict the statistical probabilities of future events online.

Statistical techniques involved in predictive analytics include artificial intelligence, machine learning, data modeling, deep learning algorithms, data mining, neural networking, statistical modeling, and other data technology. These techniques extract information from large, unstructured databases and allow the creation of effective data-based decisions/recommendation systems.

These techniques provide decision-making tools to managers and executives to determine strategies to upsell, forecast sales and revenue, optimize manufacturing processes, and develop new products.

Predictive models are used to discover potential opportunities and risks in historical and transactional data. They analyze existing data and trends to better understand customer responses and potential product purchases. Predictive models can be used in fraud detection, process optimization, cost reduction, anticipation of market trends, and the discovery and innovation of new business opportunities.

Predictive analytics software has several applications including analytical Customer Relationship Management (CRM), clinical decision support systems, collection analytics, cross-selling, customer retention, direct marketing, fraud detection, predicting outcomes of legal decisions, product or economy-level prediction, project risk management, and underwriting, among others.

All applications of predictive analytics involve three important parts: Data, Statistical Modeling, and Assumptions. The potential of every predictive model strongly depends on the quality of the historical data it processes for analysis. Statistical modeling uses various statistical techniques, ranging from basic to complex functions, for the derivation of meaning, insight, and inference of data. The conclusions drawn from collected and analyzed data usually assume the future will follow a pattern related to past trends.

Many organizations are using predictive analytics to solve difficult problems and uncover new opportunities. These include detecting fraud and preventing criminal behavior, promoting cross-sell opportunities by optimizing marketing campaigns, improving operations by managing resources, and reducing business risks by using credit scores to assess a buyer’s preferred purchases. A credit score is a number provided by a predictive model that integrates all data relevant to a person’s creditworthiness.

Few Benefits of Predictive Analytics:

The primary benefits of predictive analytics are seen in customer intelligence and asset management. Predictive analytics can influence customers in purchasing decisions through the use of marketing messages/advertisements. Industrial automation companies can use predictive analytics to reduce the risk of asset failure and downtime.

Predictive analytics can be used by organizations to gain a competitive advantage through customer loyalty and reward programs. It helps businesses form a precise and in-depth picture about their customers, the products they are most interested in, and what they want in the future.

Predictive analytics help identify new revenue-generating opportunities, optimize production processes, assess the market for new products, forecast inventory needs for production, and improve other operational efficiencies. It addresses safety concerns regarding business operations and thus lead to an increase in the level of regulatory compliance. Predictive analytics process data more effectively by using AI algorithms and IoT devices, hence providing better insights in less time than statistical algorithms.

Trends of predictive analytics:

Ease of use – Predictive models are easy to use. This technology minimizes the complexity of the model building process and the data preparation process via the user interface. Due to this, opportunities are created for predictive analytics to reach a wider audience such as targeted marketing professionals.

Geo spatial data use on the rise – The use of geospatial data is increasing with predictive analytics. Location-based data is also used in conjunction with predictive modeling to target customers based on where they are and their behavior in response to specific offers.

To Predict Failures : Predictive models are used in business processes to predict operational failures.

Limitations of predictive analytics

For predictive analytics, it is important to have a clear definition of the concept that is to be predicted. Additionally, training data obtained from past trends must be stable over the observed time period because otherwise, it may not be useful for accurately predicting future test data. In contrast to predictive analytics, which helps improve the effectiveness of future decisions, descriptive analytics provides insights about what has happened in the past. Descriptive analytics receives information from past events while predictive analytics predicts the future based on obtained historical data. Descriptive and predictive analytics are collectively known as ‘knowledge discovery in data’ (KDD).