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Predictive analytics tools:
a complete guide with examples
April 22, 2025
Predictive analytics tools use statistical modeling, data mining techniques, and machine learning to analyze current and historical business data and build accurate forecasts, helping companies define the probability of future or otherwise unknown events and make data-driven decisions accordingly.
Itransition offers professional predictive analytics consulting services to help businesses implement robust analytics solutions and enable proactive decision-making, driving favorable future outcomes.
Predictive analytics techniques
Predictive analytics tools leverage various techniques to handle tasks of different complexity. These are some of the most popular ones.
Regression analysis
Regression analysis is used to identify the relationships between independent (actions) and dependent (outcomes) variables, allowing organizations to perform scenario analysis, also known as ‘what-if’ analysis, to see how different factors impact the result.
Decision trees
This statistical model shows potential choices and the results they lead to. It is commonly used for predicting customer behavior, disease progression, and product cost-effectiveness, as well as understanding what factors influence a person’s preferences.
Neural networks
Neural networks are deep learning methods designed to mimic the human brain and recognize extremely complex patterns in data points. They’re used for big data processing and addressing tasks that traditional methods cannot solve, such as summarizing documents, recognizing faces, and classifying images.
Clustering models
Clustering models group data based on the shared attributes of data items and are commonly used for customer segmentation, search result grouping, medical imaging, and other tasks that require pattern recognition and similarity analysis.
Time series analysis
Time series models help analyze data in relation to time by capturing it at different intervals and predicting outcomes for the upcoming period. They are mostly used for handling time-dependent variables, such as seasonality or trends.
Random forests
Random forests combine and average the outputs of multiple decision trees to produce a single result. Thus, they can be used for the same tasks, like predicting customer behavior or employee turnover, but can additionally handle messy or inconsistent data, resulting in more precise predictions.
Top predictive analytics software
Power BI is a Microsoft-owned business intelligence tool offering predictive analytics features, data visualization capabilities, and the ability to create and apply machine learning prediction models.
Key features
- Has 200+ data source connectors to Excel files, databases (SQL Server database, Oracle database, IBM Db2 database, etc.), online services (SharePoint Online List, Dynamics 365 Business Central, Google Analytics, etc.), and others
- Provides native connectivity to Microsoft products, including Microsoft Fabric components, Power Platform services, and Azure data sources
- Can be deployed in cloud, on-premises, or hybrid environments
- Supports sentiment analysis, key phrase extraction, language detection, and image tagging through integration with Azure Cognitive Services
- Integrates with Azure Machine Learning to enable the creation of comprehensive machine-learning solutions
- Provisions visuals driven by artificial intelligence, including Anomaly Detection, Decomposition Tree, and Key Influencers
- Offers DAX formulas for advanced calculations and queries
- Uses Microsoft Entra ID for managing and storing user identities
- Supports data encryption at rest, object-level and row-level security based on user roles and permissions, and mobile device communication and application protection
- Can be used as a standalone software-as-a-service (SaaS) offering or as a component of Microsoft Fabric, which provides the AutoML feature to train, validate, and invoke machine learning (ML) models and generative AI capabilities to create report summaries, get content suggestions for a report, and ask questions about data
Pricing
- Power BI Pro and Power BI Premium Per User are priced per user/month, Power BI Embedded is billed based on capacity usage
- Free version available, enabling the creation of reports with Power BI Desktop
- One-month free trial as part of the Microsoft 365 suite
Amazon QuickSight is a cloud-based business intelligence tool offering machine learning capabilities to build, discover, and share meaningful insights.
Key features
- Connects to various cloud and on-premises data sources, including SaaS applications, databases, relational data stores, and native AWS services, such as Athena, S3, and Redshift
- Has expanded API capabilities for programmatic access to BI assets and visualizations
- Supports billion-row datasets, CSV, TSV, ELF, CLF, JSON, XLSX files, and files in Amazon S3
- Data visuals, customizable dashboards, and natural language querying (NLQ) capabilities can be embedded into third-party applications
- Driven by SPICE (Super-fast, Parallel, In-memory Calculation Engine) that supports thousands of users and offers high availability, fast and interactive analysis, and robust security
- Provides anomaly detection, forecasting, and NLQ capabilities
- Enables the creation of interactive dashboards with Amazon Q dashboard authoring capabilities
- Provides a mobile app for iOS and Android to interact with data from anywhere
- Comes with built-in security features like AWS CloudTrail auditing for tracking event history, single sign-on (SSO), federated users, groups, row-level security, data encryption at rest, granular permissions for AWS data access, role-based access control (RBAC), SAML, OpenID Connect, or AWS Directory Service for Microsoft Active Directory
- Сompliant with FedRAMP, HIPAA, PCI DSS, ISO, and SOC standards
- Supports email customization capabilities
Pricing
- QuickSight Standard Edition has annual and monthly plans
- QuickSight Enterprise Edition is priced based on user roles: Authors are licensed per user/month, Readers are charged either per user/month or through capacity-based pricing
- Free 30-day trial of Amazon QuickSight Enterprise Edition for up to four people
- Additional charges for Amazon Q in QuickSight (Generative BI capabilities), Pixel-perfect reports, SPICE, and threshold-based or automatic alerts enabled by ML–powered anomaly detection
Qlik Sense is a full-scale data analytics solution equipped with generative AI, predictive AI, and automated machine learning capabilities to provision insights and predictions.
Key features
- Has 140+ data source connectors and 600+ overall connectors
- Can be deployed in a private cloud, in Qlik’s hosted cloud, and on-premises
- Allows access to AutoML capabilities to automatically generate high-quality models, make predictions, and test business scenarios without having to code
- Supports self-service visualization, interactive dashboards, and search-based visual discovery
- Offers a Machine Learning API to integrate Qlik AutoML capabilities into custom workflows
- Has an integrated AI-powered assistant that interacts with users in natural language and understands 10 languages
- Sends automatic alerts to trigger human action
- Provides a mobile-friendly, responsive design
- Enables offline access to data
- Provides Qlik AutoML, a dedicated tool for automating predictive analytics in an AI-assisted, no-code environment
- Allows for building, publishing, and sharing reports in popular formats like Microsoft Office and PDFs
- Offers data encryption, role-based access control, and location-based permissions
- Complies with privacy regulations, including but not limited to the California CCPA, the EU and UK GDPR, Canada’s PIPEDA, Brazil’s LGPD, and Singapore’s PDPA
Pricing
- There are three pricing plans with an annual commitment and an unlimited amount of models to train
- 30-day free trial
- Free version is available for personal use
- Demo version of Qlik AutoML is available
SAP Analytics Cloud is a cloud-based data analytics platform that provides BI, predictive analytics, and data visualization capabilities.
Key features
- Connects to SAP Datasphere, public APIs, on-premise data sources, including SAP systems (S/4HANA, BW, etc.), and cloud applications, such as Google BigQuery, Google Drive, and OData Services
- Can be deployed in a public cloud, private cloud, and hybrid environment
- Provides data preparation, modeling, planning, and analytics capabilities with SAP Datasphere integration
- Includes a data storytelling feature to convert business insights into short video data stories with animation effects
- Incorporates Joule copilot, an AI feature that supports natural language processing, generates summaries, extracts data from emails and texts, and creates scripts for story augmentation and data model enrichment
- Comes with a Compass feature that enables Monte Carlo simulations to model and comprehend intricate risk probability scenarios, visualizing outcomes with clear probability distributions and dynamically updating models based on changing assumptions
- Offers the generative AI-powered Just Ask feature for natural language queries
- Provides pre-built KPIs, models, and data flows for industry-specific analytics, automated multi-step planning processes, and built-in collaboration features
- Secures data with predefined standard roles, custom roles, and object-type and individual object permissions
Pricing
- Offers three pricing options with contract duration between 12 and 60 months
- Demo version is available
- 30-day basic trial
Alteryx is an intuitive, AI-powered solution that incorporates several cloud and on-premises products for preparing and analyzing semi-structured and unstructured data, deploying ML models without coding, and automating analytics processes.
Key features
- Has 90+ data source connectors, including Google Analytics, Google BigQuery, Google Drive, Amazon S3, Salesforce, and Microsoft Dynamics, and APIs for custom connections
- Can be cloud-hosted, self-hosted (on-premises), and hybrid
- Supports the creation of ETL/ELT pipelines, in-database processing, and both traditional statistical models (count regression, neural network, and linear regression tools) and more advanced statistical methods (boosted model, decision trees, forest models, etc.)
- Incorporates 300+ drag-and-drop building blocks for streamlined creation of analytical workflows, including accessing, preparing, and blending diverse datasets for analysis
- Features generative and conversational AI, data preparation, advanced analytics, and automated reporting capabilities
- Automates repetitive analytical tasks with AutoML capability, including data cleansing and preparation, feature engineering, and hyperparameter optimization
- Enables self-service analytics and provides tools for carrying out A/B testing experiments and data investigation tools to visualize data and create summary statistics
- Provides geospatial and location intelligence capabilities
- Allows for adding Python and R code into workflows and extracting unstructured text data from PDF docs and images
- Ensures the encryption of data at rest and in transit, role-based access, multi-factor authentication, and automated account lockout and is developed in line with corporate Software Development Lifecycle (SDLC) policy, which includes performing security architecture reviews, automated security scans, and external penetration testing
- GDPR-, CCPA-, and FIPS-compliant, ISO 27001- and SOC2 Type II certified
Pricing
- Flexible pricing options, including separate cloud and Designer Desktop editions, with other products’ pricing being available upon request
- 30-day free trial of desktop and cloud solutions
SAS Viya is a cloud-based, AI-driven data analytics and management platform that unites several components, including SAS Visual Analytics, SAS Visual Statistics, SAS for Machine Learning and Deep Learning, SAS Visual Forecasting, and SAS Visual Text Mining to generate automated forecasts and visualizations based on data.
Key features
- Provides in-built data connectors to cloud data platforms, such as Azure Synapse, AWS Redshift, Google BigQuery, Snowflake, Databricks, and relational and nonrelational databases, including Oracle, Teradata, and MongoDB
- Compatible with open-source software and supports open big data file formats, including Apache Orc and Apache Parquet, and languages like SQL, R, Python, and Jupyter
- Offers public REST APIs for integration with external applications
- Comes with options for moving refined data to third-party data stores, including bulk loading, automatic partitioning, and direct control over processing
- Cloud-native, supporting virtual private cloud and public cloud deployments, with the ability to be launched on-premises
- Reports and visualizations can be embedded into corporate portals, websites, and mobile apps
- Offers a Cloud Data Exchange feature to securely copy data from an on-premises data store to a cloud-based instance of SAS Viya for use in SAS Viya applications
- Integrates with major large language models like GPT-4 and Gemini Pro, Microsoft Office products (PowerPoint, Excel, Word), and Azure OpenAI
- Provides Viya Copilot, a generative AI virtual assistant, for code generation, data cleaning, data exploration, and business planning, as well as packaged AI models, deep learning capabilities, and features for detecting algorithmic bias, auditing decisions, and monitoring models
- Enables visual data exploration and discovery through outlier detection, data filtering, visualizations, predictive outputs, and segmentation capabilities
- Supports clustering, decision trees, linear regression, logistic regression, and other analytical techniques
- Safeguards data with role-based access, user authentication, content authorization, and object-level and data-level security
Pricing
- Offers four packages for various needs, with forecasting features being available within Advanced, Enterprise, and Programming tiers
- Free 14-day trial
- Demo is available upon request
Tableau is business intelligence and data visualization software. It includes advanced analytics tools, allowing users to run predictive analyses within a visual analytics interface.
Key features
- Has 100+ data source connectors to databases, spreadsheets, and cloud services
- Can be deployed in the cloud and on-premises
- Data, visualizations, and dashboards can be embedded into third-party tools
- Supports complex calculations and rich time-series analysis to explore seasonality and trends, sample data, and run predictive analysis like forecasting
- Enables filtering, highlighting, cohort calculations, and outlier analysis
- Provides drag-and-drop functionality, automated clustering, dashboards, interactive reports, and Story Points for building a narrative with data
- Integrates with R, MATLAB, and Python to support advanced analytics and custom machine-learning models
- Comes with the Tableau Agent feature for easier data preparation, description generation, and the creation of calculations and visualizations via conversational prompts
- Supports industry-standard authentication methods, has built-in user identity service, provides fine-grained control over the data, content, and objects with roles and permissions, and encrypts data transmissions with SSL/TLS
- Meets SOX, SOC, and ISAE industry compliance standards
Pricing
- Offers three packages with varying capabilities, including Tableau, Enterprise, and Tableau+
- 14-day Tableau Desktop free trial period with complete functionality
- Free Tableau Public version
- Free one-year license for students and teachers
Oracle Analytics is a comprehensive platform with embedded AI and ML, as well as data integration, visualization, and broad analytics capabilities, from no-code, one-click analytics to customizable algorithms trained for specific use cases, to cater to both business users and data engineers.
Key features
- Has 40 out-of-the-box native connectors, including Oracle and non-Oracle sources, such as Oracle Autonomous Database, Enterprise Performance Management, Google Big Query, Salesforce, and Amazon Redshift
- Can be deployed in the Oracle-managed cloud, in the private cloud, on-premises, and in the hybrid environment
- Analytics content can be embedded into other applications and web pages
- Provides Java Database Connectivity for diverse data sources and integration with data lakes, such as Oracle Cloud, Azure, AWS, Google Cloud
- Facilitates self-service data preparation, anomaly detection with data quality insights, and visual dataflow creation to transform, merge, and enrich data
- Includes a shared semantic model, as well as nontechnical business terms, predefined hierarchies, consistent calculations, and metrics to enable users to leverage familiar and universal terminology when exploring and analyzing data
- Powered by ML, enabling users to discover patterns and insights and build custom predictive models for specific business tasks, such as predicting employee attrition and sales forecasting
- Supports Python, R, and SQL for machine learning modeling, solution deployment, and in-database data preparation and exploration
- Sustains natural language dialogs through NLP and NLG capabilities to allow business users to query data verbally and get understandable narratives and explanations in human language
- Localized into 28 languages
- Provides direct query and caching options and works on an in-memory engine to maintain robust performance
- Offers a mobile app, enabling real-time data analysis and sending alerts
- Comes with a code-free, drag-and-drop interface
- Incorporates multi-layered security features, such as cloud authentication, role-based and row-level security, and native integration with federated identity management systems
Pricing
- Offers user-based and OCPU-based pricing, with separate Oracle Analytics Server pricing and the ability to “bring your own license” (BYOL)
- 30-day Oracle Cloud free access with $300 of free credit is available
- The ability to use Always Free services for an unlimited period of time
Need help with choosing the right predictive analytics tool?
Predictive analytics software use cases
Marketing analytics
Marketing departments employ predictive analytics to forecast the effectiveness of promotional activities and anticipate the demand for particular products and product bundles. Based on these insights, they develop marketing strategies, choose the best promotional channels to connect with customers, and optimize pricing decisions to maximize sales.
Talent management
Companies implement predictive analytics for HR tasks, such as headhunting, using past hiring data to identify characteristics of successful candidates and improve candidate targeting, reducing recruiting costs. Predictive analytics also helps detect key risk factors of employee attrition and forecast employee turnover to develop proactive retention strategies.
Financial analytics
Financial services companies adopt predictive analytics for determining a client’s credit score for loan approval based on their payment and credit history, debt levels, and financial statements. Predictive analytics also assists with forecasting the risk of losses due to changes in market conditions. With sales and expense analysis and cash flow forecasting, financial institutions can effectively plan resource allocation.
Equipment maintenance
Various industries embrace predictive analytics tools to monitor equipment parameters, such as equipment temperature, vibrations, and sounds. Using this information, they can conclude whether a malfunction is about to occur, assess the need for human intervention, and schedule routine maintenance, preventing costly equipment outages.
Medical diagnosis & treatment
Predictive analytics in healthcare is used to detect early signs of dangerous conditions, identify patients at risk of chronic diseases, complications, or relapse, and predict individual responses to medications or treatment plans. With these insights, clinicians can improve overall care quality and minimize reliance on trial-and-error approaches.
Supply chain optimization
With predictive analytics capabilities, businesses can forecast the efficiency of supply chain operations, anticipate the demand, and detect potential disruptions. Relying on predictive insights, companies can accurately manage product inventory, avoiding overstocking or stockouts, set pricing strategies, and plan shipments and order fulfillment.
Cybersecurity management
Predictive analytics tools help flag suspicious user activities, such as fraudulent transactions, corporate spying, or repeated attempts to access sensitive information, relying on patterns and trends from past cyber incidents. By forecasting and alerting about potential threats and vulnerabilities, these software solutions help companies adopt a proactive approach, such as implementing additional security measures or blocking suspicious traffic.
Customer analytics
Predictive analytics software can be used to calculate customer lifetime value and identify the most profitable clients based on the analysis of customer demographics, sentiment, and behavior. Equipped with this information, businesses can plan more effective upselling/cross-selling offers, develop customer loyalty programs and retention strategies to prevent churn, and allocate budgets efficiently.
8%
increase in the visitors-to-buyers conversion rate
Retail BI platform with AI & computer vision
Itransition delivered a centralized BI platform with an AI-powered recommendation engine to help an ecommerce company predict user behavior, anticipate consumer interest in the product, and provide personalized customer experiences.
7x
overspending reduction
Benchmark dashboards for ad campaign optimization
Itransition’s team developed forecasting and monitoring solutions for a digital media company to track advertising campaign performance and predict their outcomes. The forecasting solution helps the sales team model advertising campaigns, calculate deviations from the set benchmarks, and generate accurate predictions. The monitoring solution, in turn, is aimed at tracking ad impressions and generating optimization recommendations.
10x
faster data processing
Pharmaceutical data analytics suite
Itransition provided a pharmaceutical market analytics firm with a number of data management and analytics solutions, including a new BI platform with a forecasting capability and a KPI management platform. These solutions help make accurate predictions and recommendations and indicate potential risks, highlighting areas that require attention, and providing visual summaries.
Predictive analytics solution development framework
The process of implementing a predictive analytics solution generally encompasses several steps, from collecting business requirements to ensuring the system is fine-tuned for specific users, data, and tasks. Yet, the activities that need to be performed can vary from one project to another, depending on the specific needs, existing technical environment, and use case.
1
Requirements definition
Identifying the problem the company wants to solve with predictive analysis, including its goals, expectations, and needs
Examining the existing technological environment
Establishing a set of functional and non-functional requirements for the solution
2
Data exploration & organization
Analyzing customer-owned and public data sources
Exploring current data management workflows
Specifying the solution’s evaluation criteria
Preprocessing data, such as cleansing, transforming, and annotating
3
Solution design
Designing a solution architecture
Settling on the implementation strategy, predictive analytics algorithms and techniques, and tech stack
Determining the project’s timeline and budget
4
Solution development
Developing predictive models and training them on the prepared data
Engineering the solution in accordance with the chosen implementation approach
5
Deployment & integration
Checking and validating the solution’s accuracy
Deploying the solution into production
Integrating the solution into the enterprise infrastructure
6
Support & maintenance
Monitoring the solution’s accuracy and performance
Optimizing and retraining the solution based on the user feedback and as new data emerge
Tuning system performance under increasing loads
Benefits of employing predictive analytics tools
Operational efficiency
Predictive analytics tools deliver insights about future events so that users make proactive rather than reactive decisions, which allows them to minimize resource waste, downtimes, and delays.
Data democratization
Predictive analytics tools with intuitive interfaces, drag-and-drop functionality, and pre-built reusable and customizable dashboard templates make forecasting accessible to non-technical users, allowing them to forecast customer churn, employee attrition, equipment malfunction, and more.
Risk reduction
Predictive analytics tools enable more precise identification and evaluation of possible risks, from data breaches to default risks, to help companies develop and implement effective strategies and mitigate those threats, prevent financial losses, and ultimately make better decisions.
Error minimization
Predictive analytics algorithms generate accurate forecasts by analyzing input data, minimizing the risk of human bias, errors, or overlooked critical insights. They effectively identify patterns and relationships, such as sudden surges in high-value online purchases, access from unusual geographic locations, abnormal lab results, or unexplained increases in power consumption.
Implement a predictive analytics solution to derive actionable insights from raw data
Predictive analytics software selection: best practices
The process of choosing predictive analytics software varies from company to company so there is no one-size-fits-all approach. Here are some best practices to keep in mind when selecting a predictive analytics tool to make sure the chosen solution supports your organization’s growth.
Choose the tool in line with your needs | Today, the predictive analytics market offers both generic solutions applicable across all industries and industry- or case-specific tools developed to address common problems faced by users in specific domains. While general-purpose solutions can be applicable for diverse use cases, they can lack specific functionality or fail to meet compliance requirements applicable to industries like healthcare or financial services. |
|---|---|
Evaluate the tool’s accuracy, functional fit & performance | Before committing to a particular platform, check whether it provides performance metrics like F1 score, confusion matrix, and confidence score to be able to monitor software performance and accuracy. Read reviews on reputable sources and explore demos and free trials to run experiments on sample datasets and assess the tool usability and whether it fits your scenarios. After purchasing, continue monitoring system performance and results, retraining the model with more data to maintain its accuracy and eliminate bias. |
Ensure software scalability | Make sure the solution seamlessly scales to accommodate a growing user base and data increase. It should also integrate with new data sources, support AutoML, and support more advanced algorithms, such as neural networks, if it’s required to analyze more complex data. |
Incorporate human oversight | To prevent bias and incorrect outcomes, the predictive analytics solution should come with a human-in-the-loop feedback capability. By incorporating human input and feedback into the ML pipeline, such as for labeling data, evaluating the performance of machine learning models, and helping the model learn and improve, you enhance the accuracy, reliability, and adaptability of ML-based systems. |
Itransition’s services to unlock predictive analytics capabilities
At Itransition, we provide end-to-end predictive analytics consulting and implementation services, helping you anticipate consumer trends and demand, detect risks and fraudulent activities, optimize supply chain and inventory management, and identify new growth opportunities.
Predictive analytics consulting services
We assist companies with creating a comprehensive strategy for implementing advanced analytics or optimizing an existing predictive analytics solution and guide them throughout the whole process, from tech stack selection to solution development and support.
- Eliciting business goals, pain points, and workflows
- Predictive analytics solution conceptualization, tech stack selection, and architecture design
- Developing a roadmap for implementing/modernizing the predictive analytics solution
Predictive analytics implementation
Our team handles every step of the predictive analytics implementation process while ensuring data accuracy, security, governance, and software compatibility with existing IT infrastructure and workflows.
- Designing predictive analytics solution architecture
- Front-end and back-end development/Platform configuration and customization
- Solution integration, testing, and deployment
- Assisting with change management and user onboarding
- Monitoring and maintaining predictive models on request
Implement the best predictive analytics tools for enhanced decision-making
Predictive analytics software adoption can be tricky for many organizations. According to the Thrive in the Digital Era with AI Lifecycle Synergies research conducted by IDC, 50% of ML and AI initiatives fail. The reasons behind these numbers, along with the data quality challenges, lack of skilled personnel, and software costs, is the inability to choose an optimal tech stack.
To tackle these issues, it’s essential to partner with a reliable predictive analytics implementation company to augment the team with skilled professionals who can conceptualize and implement the solution and ensure its adoption by both data scientists and business users. Itransition provides comprehensive data analytics services, helping companies across different domains to take advantage of predictive analytics tools.
FAQs
Predictive analytics is a branch of advanced analytics that entails the use of historical and current data, statistical algorithms, data mining techniques, and machine learning algorithms to predict future trends and events, their likelihood, and potential impact.
While predictive analysis is aimed at forecasting future events, prescriptive analytics is used to analyze data to determine an optimal course of action for a company to achieve its goals.
Predictive analytics software is a platform with advanced analytics capabilities that mines and analyzes data and uses predictive analytics models, such as logistic and linear regression models, neural networks, and decision trees, to identify patterns, anticipate behaviors, and forecast upcoming events or opportunities.
A predictive analytics platform should provide native compatibility and APIs to connect to various data sources, data visualization tools, strong data management features, and robust security features. A good predictive analytics tool enables users to automate the generation, visualization, and deployment of forecast models at scale while offering features to experiment with the data, depending on the intended user’s technical expertise.
The process of developing a predictive analytics solution typically begins with identifying a problem that you need to solve with predictive analytics. Then, you need to ensure you have relevant and complete data to address your business needs, choose the right software solutions to cater to users of all skill levels, and establish processes for sharing and using insights. As an IT development services provider since 1998, we can walk you through the entire process stress-free.
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