IT and Telecom

Telecom companies are thriving to be innovative and maximize their revenue by implementing right tools at the right place to harness massive volume, type, format, and velocity of data in order to leverage on actionable insights from that data. Telecommunication companies are managing terabytes of data stored in silos and scattered across the organization. Thus, for faster and easier processing of useful data, telcos are searching for a driven data advanced analytics solution to achieve accurate real-time insights through data mining and predictive analytics. The IT and telecommunications sector has witnessed rapid growth in the past decade, and it is undergoing various transformations. With its large size, the industry exhibits complex contracts, interdependencies of services, disparate spends, and minimal resources and time.

The major advantages of predictive analytics to a telecom company is a rise in sales, improved risk management, a decrease in the operational cost, and analysis of customer behavior. The latest predictions from CFCA 2015 Industry Survey reveals that the telecom operators globally incur an average loss of 13% or USD 294 billion due to the numerous uncollected revenue and frauds prevailing in this industry. This predictive analytics tools can help in identifying the potential threats that are prevailing in the industry to avoid losses.

Additionally, predictive analytics is being utilized across all the telecommunication enterprise to smoothen the supply chain, to understand targets and craft marketing campaign accordingly. For instance, network optimization has become an essential part of the telecommunications industry. Predictive analytics is facilitating the user a better customer experience. Optimizing a cellular network involves making numerous decisions, which are supported by predictive analytics based on analysis of historical data making the business stronger, prone to lower risk, and to achieve better outcomes.

COMPETITIVE LEADERSHIP MAPPING TERMINOLOGY

The vendors of predictive analytics software in IT and Telecom are placed into 4 categories based on their performance in each criterion: “visionary leaders,” “innovators,” “dynamic differentiators,” and “emerging companies.” The top 25 vendors evaluated in the data quality tools market include Agilone, Alteryx, Inc, Angoss Software Corporation, Dataiku, Domino Data Lab, Exago, Inc., Fair Isaac Corporation (Fico), Good Data, Greenwave Systems, Inc, IBM Corporation, Information Builders, Inc., Knime Ag, Kognitio, Microsoft Corporation, Microstrategy Incorporated, NTT Data Corporation, Oracle Corporation, Qliktech, Inc., Rapidminer, Inc, SAP SE, SAS Institute Inc, Sisense, Tableau Software Inc, Tibco Software and Teradata Corporation

USE CASES of Predictive Analytics in IT and Telecom

  • Customer Relationship Management: Predictive analytics would help companies to monitor and analyze actions, behavior and habits of existing and potential customers such as their demands, usage pattern, etc. and analyze these vital insights for effective customer relationship management.
  • Customer Churn Rate Optimization: Churn rate usually happens when customers stop using company’s product and services, preferably known as customer attrition. Predictive analytics tools help company to study factors such as customer usage, understanding their sentiments, and conduct behavioral analysis to design predictive model for customer retention.
  • Fraud Analytics: Predictive analytics tools enables organization to analyze complex datasets and events to discover the situations and triggers that can cause fraudulent activity related to payments, identity, and other misconducts.
  • Marketing & Sales Analytics: Predictive analytics is used in marketing to identity and analyze the potential customers as to what products and services could be suitable as per their buying patterns for cross selling.
  • Revenue Assurance: Predictive analytics helps the company to conduct a real-time revenue monitoring to predict and address the revenue loss associated with customer attrition, frauds or any kind of misconduct, to safeguard revenue across all channels.
  • Quality Assurance: Predictive analytics is used to validate business requirements and predict the issue impacting the quality of services, by optimizing testing to critical functions for improved business performance.
  • Price Optimization: Predictive analytics is used to analyze the pricing management model by taking into account all the variables impacting the pricing to calculate profit margin.

Case Studies of Predictive Analytics Software in IT and Telecom

IBM Corporation

Case Study: IBM helped eircom to improve customer churn rate

IBM helped Ireland-based telecommunications service provider “eircom” to improve customer experience using predictive analytics to reduce customer churn rate by automating churn prediction alerts. eircom implemented IBM SPSS analytics solution to gain actionable insights about customer’s journey to predict the churn outcome.

Business Outcome:

  • Reduction of data processing time by 75% through automation
  • Reduction in customer churn rate by 6%

 

Lavastorm Analytics

Case Study: Lavastorm Analytics helped Mobistar to combat frauds

Lavastorm Analytics helped Mobistar, one of the major mobile operators in Belgium, to implement Lavastrom’s Analytics-driven fraud solution, to prioritize and investigate multiple type of frauds related to payments, identity, dealer and communication frauds.

Business Outcome:

  • Accuracy rate for fraud threat detection increased by more than 60%
  • Reduction in fraud detection time from 24 hours to nearly 5 minutes

 

Versium Analytics

Case Study: Versium helped major software company to extend digital campaign

Versium Analytics helped major software company in extending the reach for their digital campaign and generate more leads cost effectively. Versium helped them in mapping consumer attributes of business decision makers using their LifeData warehouse and proprietary matching technology.

Business Outcome:

  • 320% increase in online campaign reach
  • The number of emails and phone numbers used for matching its customer has increased by 80%
  • Increased audience reach with reducing 75% of cost per sales on approved leads

 

Qlik Technologies

Case Study: Qlik helped Qualcomm in streamlining reporting and operation processes

Qlik enabled Qualcomm to optimize and streamline reporting and operation dashboards by deploying QlikView across their 15 business units, for real-time process analysis and improvement of workforce throughput time.

Business Outcome:

  • Saving staff time for reporting process up to 20 hours per month via automation
  • Efficiency of Qualcomm’s ASIC System Test group increased for chip testing lifecycle, saving a day’s time each month

 

TIBCO

Case Study: TIBCO assisted inQuba for enhanced customer experience

TIBCO helped inQuba, a customer experience software provider, in integrating their client’s data with their hosted services for embedded business intelligence and building data visualization reports, to drive improved performance.

Business Outcome:

  • Improved customer experience orchestration
  • Efficient embedded BI and reporting

 

Vizualytics

Case Study: Vizualytics helped Mahindra Comviva for telecom revenue assurance

Vizualytics implemented SMS Hub reporting for Mahindra Comviva, a value-added services provider for mobile operators, with the use of custom visualizations tool, for automating SMS delivery based on route cost, which would help source mobile operators to take low-cost route for message delivery for high profitability and revenue assurance.

Business Outcome:

  • Improved processing for several terabytes of messages logs within 2-3 hours, to boost profitability
  • Maximizing SMS delivery route profitability by identifying low-cost route

 

RapidMiner

Case Study: RapidMiner helped Mobilkom Austria to optimize customer support

RapidMiner used their Data Science platform to help Mobilkom Austria to analyze the incoming customer requests and automatically categorize them into different categories and forward them to concerned support analysts, using text mining technology.

Business Outcome:

  • The customer request categorization time reduced by 70%, for improved ROI
  • Minimized the error rate by 5%, for more than 50 different customer request categories

 

11Ants Analytics

Case Study: 11Ants Analytics helped 2degrees for customer churn rate optimization

2degrees, a mobile telecommunications company in New Zealand implemented 11Ants Analytics solution to identify customers who are at risk of churning, by monitoring various attributes such as time spent on network, customer usage activities and their behavior.

Business Outcome:

  • Boost in customer churn rate identification process by 1275%

 

SAS

Case Study: SAS helped Orange Business Services to improve customer relationship management

Orange Business Services implemented SAS analytics solution to build an improved and effective customer relationship management (CRM) strategy, via tracking sales and marketing campaign effectiveness.

Business Outcome:

  • 30% boost in productivity
  • Improvement in multi-channel sales and marketing efforts

 

SAP SE

Case Study: SAP helped Swisscom AG for next-generation data warehouse management

Swisscom AG implemented SAP BW/4HANA solution for faster reporting and interoperability for their new data warehouse landscape with more than 15,000 users, with self-service analytics capabilities.

Business Outcome:

  • Report execution time improved by a factor of 100.
  • Boost in IT productivity

Dataiku

Case Study: Dataiku helped LINK Mobility Group for revenue optimization

LINK Mobility implemented Dataiku’s data science platform to support their revenue monitoring services with capabilities such as self-documentation, technical and business collaboration, and improved customer dashboards.

Business Outcome:

  • 2X times increase in revenue generation
  • Improved collaboration between various processes

 

Hortonworks

Case Study: Hortonworks helped O2 for managing financial reporting and compliance

O2, a brand of Telefónica UK Limited implemented Hortonworks Data Platform (HDP) and Hortonworks Data Flow (HDF), for analytics of various business datasets within Q2 warehouses, to manage and mitigate financial and security risks, to meet International Financial Reporting Standards (IFRS).

Business Outcome:

  • Dataset analytics reduced to days from weeks, with almost 20 million records processed daily
  • 9% daily accuracy was achieved to meet IFRS 15’s compliance

 

Qlik Technologies

Case Study: Qlik helped Qualcomm in streamlining reporting and operation processes

Qlik enabled Qualcomm to optimize and streamline reporting and operation dashboards by deploying QlikView across their 15 business units, for real-time process analysis and improvement of workforce throughput time.

Business Outcome:

  • Saving staff time for reporting process up to 20 hours per month via automation
  • Efficiency of Qualcomm’s ASIC System Test group increased for chip testing lifecycle, saving a day’s time each month

 

Vizualytics

Case Study: Vizualytics helped Mahindra Comviva for telecom revenue assurance

Vizualytics implemented SMS Hub reporting for Mahindra Comviva, a value-added services provider for mobile operators, with the use of custom visualizations tool, for automating SMS delivery based on route cost, which would help source mobile operators to take low-cost route for message delivery for high profitability and revenue assurance.

Business Outcome:

  • Improved processing for several terabytes of messages logs within 2-3 hours, to boost profitability
  • Maximizing SMS delivery route profitability by identifying low-cost route

 

Dataiku

Case Study: Dataiku helped LINK Mobility Group for revenue optimization

LINK Mobility implemented Dataiku’s data science platform to support their revenue monitoring services with capabilities such as self-documentation, technical and business collaboration, and improved customer dashboards.

Business Outcome:

  • 2X times increase in revenue generation
  • Improved collaboration between various processes

Frequently Asked Questions

  • How will the Predictive Analytics Market perform in near future?
    The predictive analytics market size is expected to grow from USD 4.6 billion in 2017 to USD 12.4 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 22.17% during the forecast period. Proliferation of internet and the availability of various means for accessing the internet have led to a massive increase in the data volumes being generated. This will help in the advancement and expansion of high-speed internet services.
  • What are the opportunities in the predictive analytics market?
    With the rise in touchpoint and the need for collecting data to understand consumer behavior, every touch by a consumer has become an important data point that can be processed to reveal user behavior. With the exponential rise in individual and organizational data, businesses are now deploying teams of data scientists and analysts to process the collected data. Another factor accelerating adoption is the revenue generating potential of predictive analytics. This is compelling firms to invest in predictive analytics.
  • What is the competitive landscape in the market?
    The predictive analytics ecosystem comprises vendors, such as Alteryx, Inc. (US), AgilOne (US), Angoss Software Corporation (Canada), Domino Data Lab (US), Dataiku (US), Exago, Inc. (US), Fair Isaac Corporation (FICO) (US), GoodData Corporation (US), International Business Machines (IBM) Corporation (US), Information Builders (US), Kognitio Ltd. (UK), KNIME.com AG (Switzerland), MicroStrategy, Inc. (US), Microsoft Corporation (US), NTT DATA Corporation (Japan), Oracle Corporation (US), Predixion Software (US), RapidMiner (US), QlikTech International (US), Sisense, Inc. (US), SAP SE (Germany), SAS Institute, Inc. (US), Tableau Software, Inc. (US), TIBCO Software, Inc. (US), and Teradata Corporation (US). The exponential growth in data volume is due to the expansion of businesses worldwide, which is driving the rise in data volumes and sources. The accumulation of big data in a single location has rapidly developed the evaluation capabilities of data science experts in every organization. Additionally, companies prefer to provide stand-alone solutions rather than combined solutions. This is eventually resulting in a rise in the number of big data analytics startups, which are driving noteworthy innovations.
  • What are the regulations that will impact the market?
    Predictive analytics leads to ad hoc analysis, which assists companies to have all workable solutions for their business specific questions and forecast past, present, and possible future predictive scenarios. In the current competitive business scenarios, companies need more than accurate predictive statements and reports from its predictive analytics. Companies now need more forward-looking, predictive insights that can help them shape impactful business strategy and improve the day-to-day decision-making in real time.
  • How are mergers and acquisitions evolving the market?
    July 2017, SAP collaborated with energy and services company Centrica to help their customers in managing assets and energy consumption on insights available through the IoT. February 2017, Oracle announced the expansion of its IoT portfolio with the introduction of 4 new cloud solutions to assist businesses to fully utilize the advantages of the digital supply chain. By applying advanced predictive analytics to devise signals, IoT applications can help in automating business processes and operations across the supply chain to enhance customer experience. March 2016, the company has extended their strategic partnership to offer combined capabilities of cloud analytics and big data to their users. This will help users to automate and simplify the decisions while attaining greater business insights for smarter business decisions.
  • What are the dynamics of the market?
    Proliferation of internet and the availability of various means for accessing the internet have led to a massive increase in the data volumes being generated. This will help in the advancement and expansion of high-speed internet services. Globalization and economic growth are also playing major roles in driving greater data generation worldwide. Also, the rise in connected and integrated technologies has provided a platform to predictive analytics software vendors for leveraging this development and the unprecedented growth of the internet. Additionally, the eCommerce sector has modified the traditional shopping behavior of customers. Dedicated email campaigns, online/social media advertising, and cognitive analyzing of customers are the key enablers driving sales and increasing customers’ loyalty. With connected devices coming to the forefront, retailers are focusing on real-time analysis of customers’ shopping behavior and market basket analysis for analyzing consumers’ perception, which can be used for building tailor-made offers to increase customer retention. Similarly, with the rise in the global IoT analytics demand in the retail sector, the market is expected to have unprecedented growth opportunities for predictive analytics.

Predictive Analytics Software in IT and Telecom

Comparing 168 vendors in Predictive Analytics Software across 193 criteria.
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SPSS Modeler reduces the complexities involved in the transformation of data by providing easy-to-use models. SPSS Modeler is extensively used across various languages to analyze data from multiple databases. It majorly helps in analyzing data to predict customer churn rates and data sets. The application can be used across various industry verticles.
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Rapid Miner Studio enables users to create complex predictive models by using a drag and drop visual interface. It has a library of 1500+ machine learning algorithms and functions that can be used to build models specific to any situation. It also offers templates for common cases such as prediction of customer churn, fraud detection, predictive maintenance, etc. It provides proactive recommendations at each step for guidance. Rapidminer helps increase productivity across teams.
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SAP Predictive Analytics software enables users to create, deploy and maintain various predictive models. These on-premise tools can help users anticipate future behavior and outcomes and better guide the decision-making ability to help grow the business. SAP Predictive Analytics Cloud works alongside the BI and planning tools to visualize, plan and predict context. The tool uses in-memory technology and machine learning to uncover relevant predictive insights in real-time.
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ORACLE Analytics Cloud’s Database Platform allows the use of seamless predictive analytics software within the platform, giving it an edge over other vendors. ORACLE Analytics Cloud helps mine various data types, eradicate movement of data, and deliver actionable insights. Application developers deploy this analytics model along with SQL and R functions. ORACLE Analytics Cloud helps predict the behavior of customers, the gap between the demand and supply, and make better marketing strategies.
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Angoss uses data and predictive modeling to present insights that help users make better decisions faster. It uses advanced statistical algorithms for the prediction of outcomes. These outcomes are generated across all stages of model cycles. It helps improving predictive analytics for organizations looking to monetize their data.
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SAS Advanced Analytics provides users with better response time and faster insights provided by its in-memory analytics. SAS Advanced Predictive Analytics software helps organize data in a structured manner, making it easy to understand and present. It enables the user to analyze past, present, and future models using quality-tested algorithms. Automation of large-scale forecasts is also possible without the need for high levels of technical knowledge.
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Use the full potential of information to unleash the capability of the human resources of an organization.
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Information Builders WebFocus RStat is a cost-effective, robust, intuitive, and accurate predictive analytics software. WebFocus can help organizations by extracting meaningful insights from data of any kind. It creates interactive dashboards to consolidate information which increases the chances of actionable insights to be used in the everyday conduct of data-driven businesses.
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FICO Decision Management Suite is an integrated environment for development that is compatible with web as well as mobile applications. It is a platform that handles real-time streaming of data including its visualization, indexing, search, and pre-processing, based on rules that are defined in advance. The company's USP is the ability to provide models based on precise customer requirement to reduce time and cost.
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Alteryx provides information science that can viably and productively tap into a code-free and code-accommodating easy-to-use application. The predictive analytics software requires no coding, however, it is coding friendly for those interested. It has a fantastic interface without code for both analytics modelling and advanced modelling with code. It enables easy deployment and management of analytic models, flexibility, agility, and high speed. It supports visualization tools and all data sources. Alteryx helps find, manage, and understand all sort of analytic information of an organization at a high speed, thereby making better decisions and increasing productivity.
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MicroStrategy’s features and algorithms provide enterprises with advanced predictive analytics capabilities. MicroStrategy is useful for deploying models with governed data. It integrates seamlessly with R and can be connected to any source with the use of APIs. Some of its important features include: Scalable integration with R, incorporation of statistics, ARIMA, etc. and minimal IT support required
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Rapid assembling of predictive data that changes crude information into a business affecting service. This product has advantages for all types of users: analytics leaders, data scientists, IT professionals, and business analysts. It helps analytics leaders in terms of managing productivity, collaboration, coordination, and measuring team growth. Data scientists benefit in terms of automation, modelling, flexibility, and reproducibility. IT professionals gain advantages pertaining to scalability, code & integration, operationalization, and data governance; while business analysts obtain data access, preparation, exploration, and automated ML benefits.
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GoodData is a cloud-based platform with high SLA availability and maintenance. It allows for easy incorporation of already existing data warehouses. It allows the platform to be integrated into web or mobile applications. It is one of the most dominant cloud data warehouse that meets most versatile analytics platform requirements.
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Extensive set of cloud services that enables associations to address business problems, construct, oversee, and convey applications on a massive, worldwide system utilizing various tools and frameworks. Microsoft Azure ML Studio can be used to prepare and manage the data they need for machine learning. It can improve productivity through its powerful capabilities that can integrate the current model cycle with that of the app lifecycle.
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Natural language search and AI-powered bits of knowledge discovery make creating bits of knowledge a characteristic, instinctive, and intuitive experience. Spotfire has strong built-in predictive analytical methods that are smart, yet easy to use. Its intelligent data wrangling helps you clean and modify data, and auto-records it so you can edit it later as well. It is flexible and can scale secured documents as well.
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AXON Predict permits OEMs and enterprises to catch, screen, and break down information. AXON Predict is a product suitable for OEMs as well as large enterprises since it collates, scrutinizes, and analyzes data across networks and provide valuable visual analytical insights to users. This gives users real-time analytics to work with in order to enhance innovation and reduce costs.
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NTT Data offers effective solutions that supplement the decision-making process in an organization. This is possible across multiple business platforms and across different development and deployment capabilities. With the help of a comprehensive analytics and business insight methodology, NTT Analytics Solutions can change a client organization into an information-driven pioneer.
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Teradata predictive analytics software provides “Flip the Switch” analytics which allows on-the-fly switching of best campaign users from reverse modeling to forward prediction. Teradata Analytics for Enterprise Applications eradicates the complexity of enterprise application integration, delivers real-time access to integrated data from ERP and other enterprise applications, as well as provides transparency and visibility into business and customer insights.
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KNIME is an open source software and it helps create data science applications and services. Being open, intuitive, and able to integrate new developments, this platform makes reusable components accessible to all the users and helps understand data science workflows. The software provides actual data analysis as well as a number of processes and has productivity funtions to help operations.
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Sisense makes the analytics process easy for users right from the preparation of data to the creation of insights. Sisense is an intelligence software known for its agility and easy implementation. It can be used by varied companies. This platform offers a range of business analytics features. It is designed to make complex data preparation and visualizations simple to make better business decisions and intelligent strategies.
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Domino delivers predictive models using ML and AI techniques capturing all the dependencies of experiments. It is perfect for models across cloud databases as well as distributed systems. Powering model-driven organizations to rapidly create and convey models that drive business impact.
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Cloud-based Predictive Intelligence is used to generate insights into the behavior of customers and provides recommendations based on these insights to enhance revenue generation. Delivers reliable and customized experiences over each interaction point through a flexible, adaptable, and versatile stage that addresses enterprise needs.
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Cutting edge projects delivered within deadlines. This software is majorly used by marketing and sales teams. It combines machine learning and artificial intelligence and empowering both, sellers and buyers. It is used to create and implement marketing campaigns and is compatible with most platforms. This software also helps users discover new sources of income.
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Harnessing advanced artificial intelligence/machine learning to deliver better experiences and enhance business execution at scale. Opera Solutions offers AI solutions is supported by a team of data scientists that are scalable, practical, and transformative. This solution serves various industries such as financial, hospitality, healthcare, travel, retail, and telecommunications. In addition, problems related with scaling Big Data analytics gets solved by the AI/ML platform provided by the company.
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Kognitio empowers data analysts and business clients to run a large number of complex questions simultaneously, providing a competitive edge to large-volume business data activities. Kognitio is an immensely flexible platform that has the strong feature of viewing images and executing complex manipulations on images using standard SQL queries. It can be deployed on both, an existing framework clusters or on a complete standalone one, on-premises or on cloud.
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