Predictive analytics is a statistical and data mining solution that consists of numerous algorithms and methodologies that are used for both structured as well as unstructured data to extract business insights. It offers flexible, scalable, and advanced solutions to help users make better informed business decisions. Predictive analytics software helps industries in understanding the customer perception by providing a competitive market edge and the ability to orchestrate business decisions rapidly.
The predictive analytics software vendors are placed into 4 categories based on their performance and reviews in each criterion: “visionary leaders,” “innovators,” “dynamic differentiators,” and “emerging companies". Among all the Predictive Analytics Software vendors, the top 25 have been evaluated, including IBM SPSS Modeler, SAS Advanced Analytics, SAP Business Objects, Information Builders WebFocus, Knime AG, Agileone Cortex, Oracle Advanced Analytics, Angoss Knowledge Studio and Good Data.
Vendors who fall into this category receive high scores for most of the evaluation criteria. They have strong and established product portfolios and a very strong market presence. They provide mature and reputable data integration tools and also have strong business strategies. The visionary leaders in the predictive analytics software space include IBM SPSS Modeler, SAS Advanced Analytics, SAP Business Objects, FICO Decision Management Suite, Tableau Software, RapidMiner Studio, Oracle Advanced Analytics, and Angoss Knowledge Studio
Greenwave Axon Predict, Domino Data Lab, Teradata Analytics, Sisense, Microsoft Azure Machine Learning, and Good Data are recognized as dynamic differentiators in the predictive analytics software market. They are established vendors with very strong business strategies. However, they offer less products in the market. They focus on a specific type of technology related to the product.
Innovators in the MicroQuadrant are the vendors who have demonstrated substantial product innovations as compared to their competitors. They have very focused product portfolios. However, they do not have very strong growth strategies for their overall businesses. Information Builders WebFocus, Knime AG, Microstrategy, NTT Analytics Solution, Alteryx Predictive Analytics, Dataiku, and TIBCO Spotfire.
AgileOne Cortex, Kognito, Exago, and Qlik View are recognized as the emerging players in the predictive analytics software market. The emerging players specialize in offering highly niche solutions and services. They do not have strong business strategies as compared to the established vendors.
Predictive Analytics Software - Market Overview
The global predictive analytics software market is expected to grow from USD 4.57 billion in 2018 to reach USD 12.41 billion by 2022 at a CAGR of 22.1% during the forecast period. Major factors expected to drive the market include the data generated across various end-use industries, focus on competitive intelligence, and the use of analytics to determine future outcomes and customer requirements.
Most analytic platforms use data that is static or stored to analyze patterns that can affect business situations. Predictive analytics can, however, use current as well as historical data sets to extract meaningful information such as patterns in data, future outcomes and trends, anomalies, and changes in customer behavior. Predictive analytics software allows businesses to combine historical data with customer insights to predict future events. When combined with AI and ML, predictive analytics software can provide many competitive business advantages.
The predictive analytics software market has been segmented into solution and service; solutions include risk analytics, financial analytics, marketing analytics, sales analytics, customer analytics, web and social media analytics, supply chain analytics, network analytics, and others (HR analytics and legal analytics), while services include managed services and professional services. Professional services are further categorized into consulting and support & maintenance. The risk analytics solutions segment is estimated to have the largest market size in the predictive analytics solutions market. The Asia Pacific predictive analytics software market is expected to see the highest growth during the forecast period. Improvements in technology due to the increase in technology investments and the growing retail and manufacturing sector are some of the major factors driving the growth of the market in the region. The BFSI, manufacturing, and telecommunications and IT industries are some of the largest in the APAC region. Global competition has necessitated higher productivity at lower costs, which manufacturers need to address to stay competitive in the market. Companies in Asia Pacific are striving to improve customer service to drive competitive differentiation and revenue growth, resulting in companies exploring hosted and cloud alternatives for premises-based systems. China, India, Singapore, Malaysia, and Australia are some of the countries favoring cloud adoption.
What are the types of Predictive Analytics Software?
Financial Analytics
Due to the intense competition in the market, accurate financial statements and reports obtained from financial analytics are not sufficient; companies need predictive insights to shape impactful business strategy and improve decision-making in real time. Financial analytics, when used in conjunction with predictive analytics, can help companies combine internal financial information and operational data with external information to address critical business questions quickly.
Risk Analytics
Risk analysis in an organization is mainly used to fight any risk exposure to the organization. Risk exposure can be either financial, operational, or a risk associated with the organization’s network efficiency. The use of advanced analytical frameworks helps organizations avoid, address, or recover from risk exposure quickly.
Marketing Analytics
Marketing analytics measures, manages, and analyzes marketing performance to optimize the return on investment by improving marketing campaigns. Marketing analytics consolidates data from all marketing channels into a common marketing view enabling insights into customer preferences and trends. This common view also helps companies extract results that can help improve the efficiency of marketing efforts.
Sales Analytics
Sales analytics helps build cross-selling and up-selling opportunities to existing clients along with analyzing pipeline opportunities, generating new business, analyzing customer spending trends, and maximizing value from CRM applications. When combined with predictive analytics, sales analytics can leverage insights from customer behavior to determine actionable targets. Sales analytics can also help identify, comprehend, model, track, and augment the sales performance of an organization with the help of predictive models. Sales analytics can also be used to track customer performance at every stage and assist in deal closures.
Customer Analytics
Customer analytics uses customer segmentation and predictive analytics to understand customer behavior and help in strategic decision-making. Customer analytics can help organizations identify customers for targeted marketing campaigns, helping them not only retain existing customers but also maximize customer lifecycle and improve customer loyalty.
Web and Social Media Analytics
Web and social media analytics is mainly used to analyze web and social media data to understand and optimize a customer’s web usage. Web and social media analytics can help in understanding the challenges and controversies resulting from marketing strategies. Digital marketers, advertisers, and publishers need to separate premium customers from regular customers, track & monitor website traffic, manage marketing & advertising campaigns, and improve the overall web and social media experience for all customers, which can be achieved through the insights provided by web and social media analysis.
Supply Chain Analytics
Supply chain analytics enables data-driven decisions at operational, strategic, and tactical levels, leading to higher operational efficiency and effectiveness. It helps build revenue growth, improve profit margins, and boost control points across the entire supply chain. Currently, an organization's supply chain generates petabytes of data, right from the procurement of raw materials to the distribution and logistics of refined goods. Supply chain analytics can extract meaningful insights from this data to help businesses improve efficiency and make strategic decisions.
Network Analytics
Network analytics helps analyze network data to identify IT issues before they impact the performance and efficiency of an organization. The increase in the adoption of IoT and the consequent increase in connected devices around the world are putting a strain on network infrastructure. Network analytics can monitor network data to preempt issues and thus optimize network performance. Thus, the adoption of network analytics is anticipated to rise in the near future.
What are the Steps Involved in the Predictive Analytics Software Process?
Predictive analytics software helps organizations by predicting the outcomes and behavior of data collected, making them more proactive. The process of analyzing this data includes the following steps.Problem Identification: The process begins with the definition of the scope and identification of data sets that need to be used.
- Data Preparation: The next step is the preparation of data sets for data mining. This enables a holistic view of customer interactions.
- Data Exploration: This step focuses on the inspection and sanitizing of the data that has been collected.
- Transformation and Selection: In this step, the data that has been sorted is transformed; it is then selected and processed for further analysis.
- Model Building: The data that is refined is collected and used to create data models that enable the discovery of useful information.
- Model Validation: On the completion of model building, its validation is carried out, based on business rules.
- Model Deployment: This is the final step in the process. The model is deployed to enable daily decision-making and obtain the required outcome.
- Result Monitoring: The deployed data models are monitored to evaluate their performance and ensure delivery of the expected outcomes.
Use Cases of Predictive Analytics Software
Presented below are case studies from some of best predictive analytics software and service offerings. These include scenarios where predictive analytics software and services (with their underlying benefits) were deployed to obtain comprehensive solutions.
USE CASE: Identify Suspicious Claim Cases
Project Objective: To help the company minimize losses caused by fraud
Description: Infinity Property & Casualty Corporation of Birmingham, Alabama, a national provider of car insurance, was witnessing revenue loss as a result of insurance fraud. This was causing it a loss of both, monetary value as well as reputation.
IBM Corporation’s Solution: To tackle the instances of losses incurred by insurance fraud, the company opted for the IBM SPSS predictive analytics solution. This solution is capable of scrutinizing claim histories to identify and flag suspicious claims that can be investigated. It also helps fast-track legitimate claims. The use of this solution resulted in the company gaining a 400% ROI in 6 months. It also led to the addition of USD 1 million to its bottom line and reduced the time taken to refer a suspicious claim for further investigation by 95%.
Benefits Achieved:
- 400% increase in ROI
- Identification of suspicious claims for further investigation
USE CASE: Understand Buying Patterns
Project Objective: To observe the buying patterns of consumers to target promotions and increase salesDescription: Large retailers in India are investing in various methods to analyse the intent of customers, offer immediate responses to consumer expectations, predict future behavior, and enhance the shopping experience (both digital and physical). They are focusing on customer intelligence and predictive analytics, which are digital transformation tools that provide a personalized experience as well as meet in-store expectations of customers.
BRIDGEi2i Analytics’ Solution: The predictive analytics solutions of BRIDGEi2i’s assisted these retailers to obtain actionable insights on customer behavior and to devise approaches that are customer-centric in order to maximize retention. ExTrack, its proprietary platform was offered to track issues related to customer experience effectively. These were correlated to enhance business outcomes.
Benefits Achieved:
- Improved customer experience
- 360° customer view
- Identification of issues that require instant attention
- Customer loyalty and personalized schemes
USE CASE: Increase Revenue and Decrease Business Inefficiencies
Project Objective: To enhance operational inefficiency, improve business processes, increase revenue, and reduce business inefficiencies
Description: FTI Consulting, Inc. required quick, easy-to-use, and actionable data analytics. The company needed operational improvements to eradicate inefficient processes.
TIBCO Software’s Solution: TIBCO Spotfire, its API library, and predictive modeling engine were used by FTI Consulting Health Solutions. The use of Spotfire resulted in an improvement in productivity for both, clients as well as consultants.Being easy-to-use for its service line experts, the time required for the operational improvements that were recommended was reduced. It also led to the company being able to accommodate more clients without increasing the number of consultants.
Benefits achieved:
- Increased productivity for clients and consultants
- Improved and accelerated business operations
- Increased efficiency
USE CASE: Market Basket Analysis
Project Objective: To obtain insights into market baskets across products, categories, and stores
Description: Grupo Merza, which offers food & beverage distribution, transportation, and logistics services, in the retail as well as wholesale formats required augmentation of its analytical insights to enhance the efficiency of its inventory management, transportation, delivery, and crediting & invoicing functions.
SAP SE’s Solution: SAP HANA platform, SAP Lumira software, SAP Sales Insights for Retail analytics application, and SAP Predictive Analysis software were used to understand customer needs, increase sales, and enhance customer engagement. The SAP Lumira software was installed within 4 weeks, without the involvement of a consulting service.Benefits achieved:
- Improved transactional data and reporting delivery
- Quicker decisions with self-service data visualization
- Insights into the contribution of product assortment and promotion to market baskets
- Identification of defaulters on debts
- Creation of scorecards to predict lender behavior
What do experts have to say about Predictive Analytics?
“Retailers are some of the early adopters of analytics and are now embracing the AI wave to improve customer experience and journey. Omnichannel touchpoint integration and automation will be one of the key focus for retailers; AI, machine learning, deep learning, and IoT analytics will enable that and transform retailers’ business in a data-driven way.”- C level executive Leading Predictive Analytics Software Provider
“Predictive analytics solutions are gaining traction due to the advent of dynamic technologies, as it has increased the pressure on organizations to sustain in the competitive environment. - Analyst Relations Leading Predictive Analytics Software Provider
“In a dynamic business environment, the growth of on-demand analytics is expected to increase substantially.” -Analyst Relations Leading Predictive Analytics Software Provider
Best Predictive Analytics Software
The company focuses on the development of an AI-driven Big Data Analytics Engine and its eco-system that any App Developer could easily adapt for different applications, even without any prior machine-learning knowledge but yet able to achieve what only a team of experienced senior data scientists could deliver. The engine uses machine learning and semantic analysis to automatically tag and characterizes stories, and then link together related material.
An excellent one-stop platform for online shopping, Gshopper helps customers meet all their shopping needs. It uses methods like AI product recommendations and predictive analysis to suggest customers and help them discover high-quality global products that are popular and engaging. Gshopper uses big data and predictive analytics, along with artificial intelligence-based products, to discover new trends in the market and meet the ever-growing customer demands. You can experience secure cross-border shopping and also discover the best global high-quality products.
