Predictive Analytics

What is predictive (predictive) analytics?

Predictive or predictive analytics are primarily many methods of statistics, data analysis, and game theory that are used to analyze current and historical data/events to predict future data/events.

The most well-known way of using predictive analytics is the use of scoring models to assess a client's solvency in issuing loans with a bank. Any scoring model is based on historical data, and if in the past, any group of clients was convicted of late cancellation of loans, and you are similar in any characteristics to this group, then most likely they will refuse to issue loans to you.

However, these are not all areas where predictive analytics is applied, it can be used to develop products, to select a potential audience, to select the next product that you can offer to the client (Next Best Offer) and many others.

Related to predicative analytics is the concept of data mining, since predicative analytics uses partially similar methods. The central essence of predictive analytics is the task of determining a predictor or several predictors (parameters or entities that affect a predicted event). For example, insurance companies highlight predictors such as age, driving experience when determining the insurance premium. The set of these predictors forms a predictive analytics model that predicts a particular event in the future with some degree of probability.

Analysts Gartner believe that the further development of the global business analysis market will follow the path of active development of advanced (advanced) analytics, including predictive analysis, construction of simulators and variable models. The ability to build such models in 2013 in Gartner called 15 a mandatory block of corporate BI platforms.

Advanced analytics use statistics, descriptive and predictive data mining tools, simulators, and optimization tools. The ultimate goal of all these tools is to make decisions, solve business problems and identify opportunities for making the best forecasts, identifying processes, patterns and other patterns.

In order for the predictive analysis to be successful, Forrester recommends that the following stages be clearly followed: setting a goal, obtaining data from various sources, preparing data, creating a predictive model, evaluating the model, implementing the model, monitoring the effectiveness of the model.

Predictive Analysis Tools Implementation Diagram

Forrester Research, 2013

Applications

Unlike data discovery, predictive analytics tools are addressed to specialists, so they are not used so widely. According to 2012 data from Gartner, only 13% of BI users make extensive use of predictive analysis tools. Less than 3% use methods such as mathematical modeling, simulators and optimization.

Experts believe that one should not wait for mass implementations in this area, but the trend will gradually change. The reason for this is the emergence of the big data phenomenon, which is pushing organizations to find new means of information processing. Gartner believes that those companies that apply advanced analytics to big data will grow 20% faster than competitors.

According to Eric Siegel, an expert on predictive analysis, outlined in his eponymous book Predictive Analytics^[1], the scope of predictive analysis is actually very wide. He cites 10 of the most common examples:

• Direct Marketing: The challenge is to increase responses by integrating customer data from different web and social sources. Companies can determine the effectiveness of campaign promotions by dividing leads by segment, location, or delivery channel.
• Predictive ad targeting: Any advertiser wants to know which message is most effective. Advertising can be shown in the best way online based on the similarity of clicks, with customers only benefiting from submitting more relevant content.
• Identifying fraudulent schemes: Predictive analysis tools can minimize the use of fake insurance schemes by fraudsters, obtaining credit, and the like.
• Investment risk management: Predictive analysis tools allow you to assess the potential of a startup or other asset. The method can also be used by companies to select a partner, a candidate for purchase, or even a vendor.
• Customer retention: Predictive analysis allows you to calculate customer behavior, as well as take into account negative factors that affect their decisions.
• Recommendation services: Users can be recommended products or content based on previous views, interests or analysis of comments on Twitter.
• Education: Predictive analytics tools can be used to provide better teaching methods.
• Political campaigns: Simulating the voting process.
• Medical decision systems: Predictive analysis can, based on a variety of factors, identify patients' propensity to diabetes mellitus, asthma, and other lifestyle-related diseases.
• Insurance and mortgage lending: accurate determination of the reasonable amount of coverage in each insurance case.

Trade

Consumer demand forecasting and share planningFrom ^[2]

• Forecast daily consumer demand at the store/item level for 28 days
• Forecast of share demand
• Accounting for commodity substitution (cannibalization) during shares
• Accounting for price changes, seasonal change in demand
• Taking into account temperature and weather conditions by store cities, store sizes, etc.
• Forecasting for New Product Items, New Stores
• Taking into account the discoveries of competitors

Key Value Item Analysis

• Highlighting items that have a disproportionate impact on customer perception
• By identifying these commodity positions, the retailer can influence consumer perception by adapting its pricing strategy
• Using an aggressive pricing strategy, retailers can influence traffic, overall pricing, profitability, market share, etc.

Optimization of regular and promotional prices

• Best Price Recommendations
• Accounting of restrictions on calculation, margin, turnover, deliveries, etc.
• Price elasticity calculation
• Time Price Recommendations to Accelerate Sales
• Direct Customer Offers
• Multichannel Sales

Customer segmentation

• Behavioral and marketing segmentation,
• Targeted marketing campaigns
• Analysis of the consumer basket,
• Product Recommendations
• Cross-selling and up-selling,
• Best Alternative Strategy,
• Preventing customer outflows by calculating consumer risk

Identify groups of buyers with similar behavioral characteristics by multivariate data analysis
Customer Segmentation, Behavioral Targeting, Churn Prevention

• Increase of share conversion by forming target groups (segments) of buyers for directed shares
• Improve profitability by recommending rebate levels for different customer target groups
• Increase loyalty by early identification of buyers with the greatest likelihood of leaving and subsequent actions (shares)

Predictive Analytics in Manufacturing

• Analysis and forecasting of impact of factors on product parameters
• Equipment Failure Prediction - Transition from Routine Service to Condition Service
• Forecast product production and energy and resource consumption
• Online proactive alerts for future emergency situations

For industrial enterprises where processing and understanding of a huge amount of data is required and there are high risks in decision-making, predictive analytics is of particular importance^[3].

Process flow data are not always used effectively, while they can be used to optimize operational processes and improve production performance. Optimization can be performed on any type of production with a serious level of automation, organized collection and long-term storage of information. To do this, intelligent systems are successfully used that can analyze the state of the process in real time, predict the further progress of the process, determine the level of optimality and, if necessary, change the control parameters or give recommendations to the manager. To solve these problems, the tools machine learning create a predictive mathematical model of the technological process. It analyzes the input parameters, in real time gives a forecast of the progress of the process and proposals for its optimization. This model is combined with, and PCS MES ERP systems enterprises.

Another challenge for predictive algorithms is equipment maintenance and repair. Basically, enterprises use basic control mechanisms provided by equipment manufacturers. But the potential of these funds is limited, since they do not allow analyzing additional factors affecting the condition of the equipment and predicting the critical situation in advance. Thus, maintenance personnel receive a lot of data, but do not know how this data is related to each other. As a result, the reaction from repair services follows only after equipment failure, which leads to downtime, and, therefore, additional costs. Predictive analytics with machine learning and artificial intelligence tools conducts continuous analysis of big data, performs visualization of data on the state of equipment at the moment and predicts scenarios of equipment failures. As a result, unscheduled downtime is reduced, maintenance work is optimized, maintenance time is reduced, and the management personnel receives an in-depth analysis of the causes of equipment failures.

Global Market

2024: Global Predictive Analytics Market Size Exceeded $10 Billion for the Year

In 2024, costs in the global predictive analytics market reached $10.15 billion. This corresponds to an increase of about 10% compared to 2023, when expenses were estimated at $9.2 billion. The industry is showing a stable positive trend, as stated in the Market Research Future review presented in mid-February 2025.

It is noted that in a rapidly changing business environment, organizations are increasingly relying on data to manage decision-making processes. Companies understand the importance of using analytics to improve operational efficiency, optimize resource allocation, and improve customer experience. In addition, the volume of information generated, which comes from a variety of sources, is growing rapidly. Against this background, the need for advanced analytical tools is increasing. Predictive analytics allows businesses to forecast future trends as well as customer needs based on historical metrics. This approach provides a competitive advantage, which ultimately contributes to revenue growth.

An important driver of the industry is technological advances. The introduction of artificial intelligence and machine learning allows you to process and analyze huge amounts of data with high speed. AI algorithms improve the accuracy of forecasts, as well as make it possible to make important decisions in almost real time. Companies are increasingly using AI tools to improve operations, automate routine tasks and reduce the burden on employees.

The trend towards personalized service is also having a positive impact on the industry. Using predictive analytics, companies can better understand trends in customer interactions, allowing them to develop customized marketing strategies and tailor services to market demands. For example, healthcare organizations can optimize patient care, and financial institutions can make personal offers. As a result, the focus on improving customer satisfaction encourages organizations to invest heavily in advanced analytics products. In addition, big data analytics help boost cybersecurity by identifying suspicious activities.

Among the main applications of predictive analytics tools are risk management, fraud detection, supply chain management, customer data analytics and predictive service. In 2024, the first of these segments provided revenue at the level of $2.5 billion, the second - $1.8 billion. Supply chain management funds generated approximately $2 billion. Client data analytics accounted for $2.7 billion, predictive service - about $1.2 billion. North America became the leader geographically in 2024 with a cost of $4.5 billion. Asia-Pacific region brought $2.9 billion, Europe - $2.5 billion. Significant market players are named:

• MicroStrategy;
• IBM;
• Salesforce;
• ThoughtSpot;
• Oracle;
• Deloitte;
• Qlik;
• SAS Institute;
• Alteryx;
• Palantir Technologies;
• Informatica;
• Tibco Software;
• Tableau;
• Microsoft;
• SAP.

Market Research Future analysts believe that in the future, the CAGR in the market under consideration will be 10.36%. As a result, by 2035, costs on a global scale could increase to $30 billion.^[4]

2017 Transparency Market Research Forecast for 2019

The predictive analytics market will reach $6.5 billion by 2019, according to Transparency Market Research^[5] November 2013. According to analysts at this company, market growth is driven by drivers such as increased demand for user analytics and intelligent software for information security and fraud protection. A separate note is the rapid development of the cloud solutions segment for predictive analysis^[6]

For comparison, according to the results of 2012, according to the same company, the global market for systems for predictive analysis amounted to $2.08 billion, and its average annual growth in the period from 2013 to 2019 will be 17.8%.

The most in demand is predictive analytics in industries working with end consumers, such as banking and financial services, insurance, the public sector, pharmaceuticals, telecom and IT, retail. These segments accounted for 71.8% of implementation in 2012. During the forecast period, the maximum dose of projects will be in the banking sector, financial services, insurance. However, the fastest number of projects will grow in retail and in production.

Analysts note that an increase in cases of fraud, non-payments, threats of non-compliance with numerous rules and regulations force businesses to increasingly turn to predictive analysis in order to build futuristic models that allow taking preventive measures in relation to adverse events.

Different types of software such as user analytics systems, information security analytics and campaign management accounted for about 50% of the predictive analytics market in 2012. These solutions are used to optimize organizational processes in sales and marketing, customer and channel management, financial and risk management, and so on.

Among regional markets, North America will be the largest market for predictive analysis systems, and here the demand for forecast solutions will come from companies that are actively solving big data issues. That is why all key vendors of big data solutions, including SAS Institute, SAP, Oracle, IBM, Microsoft, Teradata and Tableau Software, will soon be leased to predictive analytics .

At the same time, the market remains largely divided between the largest players: the top five suppliers accounted for 80% of the market volume in 2012. Other notable players include Fair Isaac, Tibco, Information Builders, Alteryx, Qlik (QlikTech) and MicroStrategy.

Predictive Analytics Systems

Forrester 2017

Forrester 2013

Open source predictive analysis systems:

• KNIME
• Orange
• Python
• R
• RapidMiner
• Weka

Commercial Predictive Analysis Systems:

• SAP BusinessObjects Predictive Analysis
• SAP Predictive Maintenance and Service
• SAP InfiniteInsight (formerly KXEN)
• SAS Rapid Predictive Modeler
• IBM Predictive Insights
• IBM Smarter Analytics Predictive Asset Maintenance and Quality for Smart Factory (IBM PMQ Solution)

• Tibco Spotfire - TIBCO
• Angoss KnowledgeSTUDIO
• Mathematica
• MathWorks MATLAB
• Oracle Data Mining (ODM)
• Oracle Retail Predictive Application Server
• Pervasive
• Statistica
• IBM SPSS Decision Management and IBM SPSS Modeler

• Loginom Analytical Platform
• Eglitec Digital Business Intelligence Platform

Big Data Predictive Analysis Market Leaders

According to the Forrester^[7], Q1 2013 for 2013, the leaders in the predicative analysis market in big SAS SAP IBM data are, and. Expertise is also quite Tibco strong, Oracle StatSoft and KXEN, while promising vendors are Angoss, Revolution Analytics and Salford Systems.

See also

Business Intelligence, BI (Global Market)

Global BI Market Trends

Business Intelligence (Russian market)

CPM (Global Market)

Big Data (Big Data) Global Market

Big Data - Catalog of systems and projects

Self-Service BI

Data visualization

Cloud/SaaS BI

Open Source BI

Notes