With the big data, machine learning and AI, near infinite cloud storage and compute resources modern companies have at their disposal, the future of data analytics appears quite certain. Indeed, 59% of the surveyed organizations are already moving forward with the use of advanced and predictive analytics by either investing in in-house expertise or by relying on professional predictive analytics consultancy, the Global State of Enterprise survey by MicroStrategy reveals. In this blog post, we’ll overview predictive analytics benefits as well as share the eight best tools to start the journey and factors you need to consider while choosing between them.
What is predictive analytics software?
Predictive analytics tools are various tools used to extract information from current and historical datasets to build predictive models that help companies define the probability of some future or otherwise unknown event happening. Today, predictive analytics software is commonly referred to as data science and machine learning tools, although sometimes business intelligence and big data software are added to the list.
Predictive analytics and its techniques
Within the data analytics maturity ladder, predictive analytics is between diagnostic analytics, which reveals the causes of certain business outcomes, and prescriptive analytics, which suggests the optimal course of action to take. Predictive analytics helps mine historical and current data for trends and patterns to forecast the probability of certain events or outcomes with the help of statistical modeling and machine learning.
Types of predictive analytics models
To make predictions as accurate as possible, companies employ a whole range of predictive analytics techniques, which they choose based on their specific business needs and requirements. The most common types of predictive analytics models include:
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Regression analysis Used to identify the relationships among variables. For example, it can be used to identify how increased advertising spending influences monthly sales or how excess weight in a person can influence the probability of having a thromboembolism of the pulmonary artery. |
Decision trees Used for predicting the class or value of the target variable based on the decision rules derived from the training data. Such models are used to predict how likely a customer is to pay off their loan, identify at-risk patients for healthcare prioritization, assess expansion opportunities, identify prospective customers, etc. |
Neural networks Used to establish complex data relationships between the inputs and outputs of the explored dataset. This predictive analytics model type is used in image recognition, recommendation engines, stock market prediction, etc. |
Predictive analytics benefits
Organizations are adopting predictive analytics to solve complicated business tasks and uncover new opportunities. To display the real value of predictive analytics adoption, we’ve listed its most common use cases across industries:
- Improve customer experience. Marketing departments employ predictive analytics to identify target customer segments and predict customer behavior. Based on the derived insights, they can allocate the efforts efficiently, for example, towards designing effective customer loyalty programs, developing retention strategies, etc.
- Optimize marketing efforts. With predictive analytics capabilities on hand, retailers can identify how shoppers navigate brick-and-mortar stores and plan merchandise accordingly to boost sales. Predictive analytics engines also serve to dynamically optimize prices, forecast the demand for particular products and product bundles, predict the effectiveness of promotional activities, and facilitate upselling and cross-selling.
- Identify profitable customers and predict sales. Sales representatives rely on predictive modeling for lead scoring to determine which customers to reach out to in the first place. They also run models to calculate customer lifetime value and forecast sales for sales strategy planning and budget allocation.
- Improve human resources management. Regardless of the industry, companies can implement HR predictive analytics engines for head-hunting, analyzing communications with candidates, and keeping track of current employees' performance.
- Increase productivity of production processes. Manufacturing companies run predictive analytics models to identify factors of poor quality or production failures and come up with a solution.
- Balance supply and demand. Various industries embrace predictive analytics capabilities to optimize warehouse and logistic operations by forecasting demand and planning shipment and order fulfillment accordingly.
- Reduce risks. Financial institutes adopt predictive analytics for predicting fraudulent transactions, determining a credit score, forecasting market behavior, automating wealth management with AI, and predicting customer attrition.
- Predict clinical risk. Healthcare organizations turn to predictive analytics for the early detection of deteriorating patients and identification of patients at risk of chronic diseases.
Do you have a predictive analytics project in mind?
Best predictive analytics software
| Tool | Best for | Deployment | Pricing | Free trial | Website |
|---|---|---|---|---|---|
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Alteryx Analytic Process Automation Platform |
Self-service predictive analytics across all departments | Alteryx cloud and on premises |
Per user, per year Designer - $5,195/user/year Intelligence Suite - $2,300/user/year |
Available | See more |
|
Azure Machine Learning |
Automating ML workflows for data scientists, ML engineers, and application developers with cost control and visibility |
Cloud, on premises, hybrid, multi-cloud |
Tied to resource usage Free tier available |
Available | See more |
|
AWS SageMaker |
Running ML workflows for developers and data scientists in the AWS cloud within a managed infrastructure | AWS cloud |
Tied to resource usage Free tier available |
Available | See more |
|
H2O Driverless AI |
Automating various ML workflows for data scientists, ML engineers, and business analysts with rich explainability functionality | Cloud and on premises |
Pricing for the enterprise version on request Free version available |
Available | See more |
|
IBM SPSS |
Automating ML workflows for individual users or groups of users with different levels of expertise across the enterprise | Cloud, on premises, hybrid |
Per user, per month IBM SPSS Statistics – from $99/user/month IBM SPSS Modeler – from $499/user/month |
Available | See more |
|
RapidMiner Data Science Platform |
End-to-end augmented data science lifecycle management | Cloud and on premises |
Pricing for the commercial version on request Free version available |
Available | See more |
|
SAS Advanced Analytics |
Automating the entire analytics lifecycle at enterprise grade, accelerated ML models operationalization | Public cloud, private cloud, on premises | On request | Available | See more |
|
TIBCO Data Science |
Production and execution of ML on edge devices for asset-centric organizations | Cloud and on premises | On request | Available | See more |
Choosing the optimal predictive analytics software is an effort-intensive endeavor, which requires comprehensive analysis and domain expertise. To help you accurately evaluate potential predictive analytics software, we reviewed the top predictive analytics software vendors down below, listing them in alphabetical order.
Alteryx
Alteryx’s main product is the APA platform, a unified solution, which serves to automate analytics, ML and data science processes and offers self-service predictive analytics capabilities across all departments.
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Company |
Alteryx |
|
Product |
Key product: Alteryx Analytic Process Automation (APA) Platform Supporting products: Alteryx Designer, Alteryx Intelligence Suite, Alteryx Server, Alteryx Connect, Alteryx Promote |
|
Key features |
|
|
Deployment |
Alteryx cloud and on premises |
|
Pricing |
Designer - $5,195/user/year Intelligence Suite - $2,300/user/year Pricing for other products and services is available upon direct request. Free trial available |
Azure Machine Learning
Azure Machine Learning is Microsoft’s cloud service that supports the end-to-end predictive analytics lifecycle and automates workflows for data scientists, ML engineers and application developers.
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Company |
Microsoft Azure |
|
Product |
Key product: Azure Machine Learning Supporting products: Azure Synapse Analytics, Azure Arc, Azure SQL Database, Azure Storage Blobs, Azure App Service, Power BI, etc. |
|
Key features |
|
|
Deployment |
Cloud, on premises, hybrid, multi-cloud |
|
Pricing |
No surcharge for Machine Learning Service, you pay only for computing and storage resources used. Free trial available |
AWS SageMaker
Amazon SageMaker is a cloud machine learning platform for data scientists, developers, and business analysts to accelerate ML model development with a fully managed infrastructure and tools as well as support for major ML frameworks, toolkits, and programming languages.
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Company |
Amazon Web Services |
|
Product |
AWS SageMaker |
|
Key features |
|
|
Deployment |
AWS Cloud |
|
Pricing |
Pricing is tied to resource usage. Free trial available |
H2O Driverless AI
A data science platform to satisfy the needs of various employees, including data scientists, ML engineers, and business analysts.
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Company |
H2O.ai |
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Product |
Key product: H2o Driverless AI (commercial enterprise version) Supporting products: H2O 3 (open source), H2O Sparkling Water (for Spark Integration), H2O AutoML for ML, H2O Wave |
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Key features |
|
|
Deployment |
On premises and cloud |
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Pricing |
Pricing for the enterprise version is upon direct request to the vendor. Free version available Free trial available |
IBM SPSS
IBM offers a suite of predictive analytics tools to be run both in the cloud and on premises. The suite includes SPSS Statistics, which helps users perform complex statistical analysis and SPSS Modeler used for building predictive models.
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Company |
IBM |
|
Product |
IBM SPSS Statistics IBM SPSS Modeler |
|
Key features |
IBM SPSS Modeler is a visual data science and ML solution to speed up operational tasks for data scientists and conduct analysis regardless of where the data is located, its size, or whether it is structured or unstructured.
IBM SPSS Statistics is a comprehensive solution supporting the entire analytical lifecycle from data preparation to analysis and reporting.
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|
Deployment |
Cloud, on premise, hybrid |
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Pricing |
IBM SPSS Modeler is also available as part of IBM Watson Studio. Free trials available |
RapidMiner Data Science Platform
RapidMiner is a data science software platform aimed to meet the needs of data scientists and tech-savvy business users for data preparation, machine learning development, predictive analytics, and text mining.
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Company |
RapidMiner |
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Product |
Key Product: RapidMiner Data Science Platform Supporting products: RapidMiner AI Hub, RapidMiner Go, RapidMiner Notebooks, RapidMiner AI Cloud, RapidMiner Model Ops, etc. |
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Key features |
|
|
Deployment |
On premises, cloud |
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Pricing |
The pricing of the commercial version is available upon direct request. Free version available Free trial available |
SAS Advanced Analytics
SAS offers a set of integrated predictive analytics solutions designed for the needs of all types of users – from business managers and business analysts to data stewards and data scientists. The tools support the whole predictive modeling life cycle from data preparation to ML model operationalization.
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Company |
SAS |
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Product |
Key product: SAS Visual Data Mining and Machine Learning Supporting products:
|
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Key features |
|
|
Deployment |
Public cloud, private cloud, on premises |
|
Pricing |
Pricing is available upon direct request. Free trials available |
TIBCO Data Science
TIBCO Data Science integrates the capabilities of TIBCO Statistica, TIBCO Spotfire Data Science, TIBCO Spotfire Statistics Services, and TIBCO Enterprise Runtime for R to support the end-to-end machine learning lifecycle and automate the steps involved, from data ingestion and preparation to model deployment, monitoring, and governance.
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Company |
TIBCO |
|
Product |
TIBCO Data Science |
|
Key features |
|
|
Deployment |
Cloud, on premises |
|
Pricing |
Pricing is available upon direct request. Free trial available |
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Predictive analytics software selection: best practices
The process of choosing predictive analytics software is pretty much the same as for any other piece of software – too complex for generalization and does not have a one-size-fits-all approach. Here are some aspects you need to clear up when selecting predictive analytics tools:
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Use cases Today, the predictive analytics market offers both generic solutions applicable across all industries and industry- or case-specific tools with a well-crafted set of capabilities. Therefore, to avoid reinventing the wheel, you need to carefully explore if any of the existing tools have the functionality you need. |
Software users You need to establish whether the solution will be used by well-seasoned data scientists, business users, or both. The first set are usually looking for the functionality to augment data discovery, preparation, and model development, while the latter seek a more automated solution that helps reduce the time and expertise required to tune and build predictive models from scratch. |
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Solution scalability You need to make sure the solution is seamlessly scaled to accommodate a user base increase and allow for the advancement of existing analytics capabilities as soon as the need arises. |
Deployment flexibility Every year, more and more cloud solutions with a subscription-based pricing model are being offered, which could be relevant for companies which lack in-house resources or the budget to set up and maintain full-scale systems, create predictive models, scale efficiently, etc. |
Integration flexibility You need to ascertain if the solution would fit well into your existing technology environment, integrated with the relevant data sources as well as business applications and systems. |
An afterword
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. We have shared only a number of factors to start with when making the choice, however, the list is not exhaustive and each particular case requires careful examination from professional consultants.
