Wednesday, 12 March 2014

Predictive Analytics with IBM SPSS: Basic Q&As


What is Predictive Analytics?

Predictive Analytics is the transformational technology that enables more proactive decision making, driving new forms of competitive advantage by analyzing patterns found in historical and current transaction data as well as attitudinal survey data to predict potential future outcomes. This helps organizations to become more proactive in cutting cost, reducing risk and increasing profitability, optimizing their business and driving new forms of competitive advantage. Below figure shows how decision making is changed over the period.


“Predictive analytics helps connect data to effective action by drawing reliable conclusions about current conditions and future events. It enables organizations to make predictions and then proactively act upon that insight to drive better business outcomes and achieve measurable competitive advantage.” - Gareth Herschel, Research Director, Gartner Group

New approaches are being employed in order to take advantage of predictive analytics capabilities. Business leaders know that to meet their goals for profitability, revenue, cost reduction, and risk management, especially in the current economy; they cannot continue to operate the way they have in the past. Today’s marketplace involves an exponential increase in the number and source of customer interactions; it is now a high-volume, multi-channel game.

Through better management and use of information, business leaders can remove the blind spots that hinder informed decisions, and also achieve the next generation of efficiencies by providing precise, contextual analytics and insight at the point where these items can make a direct impact on business (point of impact). Doing so can enable micro-optimization, improving insight into patterns of customers, processes, and businesses, and deliver better real-time decisions and actions in every area of the organization.

This micro-optimization is made possible by establishing well-constructed processes and empowering individuals throughout the organization with pervasive, predictive real-time analytics. This approach can help shift from a sense-and-respond focus to a forward-looking predict-and-act focus. This approach also moves analysis from a back-office activity limited to a handful of experts to an approach that can empower everyone in the organization at the point of impact and in the context of the current situation. The result is rapid, informed, and confident decisions and actions throughout the organization, based on consistent and trusted information.


Why predictive analytics?

Eric Segel identified it very beautifully in “Seven Reasons You Need Predictive Analytics today”. Just summarizing his 7 reasons below —
1. Compete – Secure the Most Powerful and Unique Competitive Stronghold
2. Grow – Increase Sales and Retain Customers Competitively
3. Enforce – Maintain Business Integrity by Managing Fraud
4. Improve – Advance Your Core Business Capacity Competitively
5. Satisfy – Meet Today's Escalating Consumer Expectations
6. Learn – Employ Today's Most Advanced Analytics
7. Act – Render Business Intelligence and Analytics Truly Actionable

The power of predictive analytics in driving optimal outcomes and profitable revenue growth is clearly demonstrated by organizations that deploy predictive solutions. An independent financial impact study by IDC found that the median return on investment (ROI) for the projects that incorporated predictive technologies was 145%, compared with a median ROI of 89% for those projects that did not. Source: IDC Report: Predictive Analytics Yield Significant ROI - SPSS Inc. available at http://www.spss.hu/home_page/idcreport.htm

An independent assessment of SPSS customers found that 94% achieved a positive ROI with an average payback period of 10.7 months. Returns were achieved through reduced costs, increased productivity, increased employee and customer satisfaction, and greater visibility. Flexibility, performance, and price were all key factors in purchase decisions. Source: Nucleus Research Report: The Real ROI of SPSS - SPSS Inc., available at http://www.spss.com/home_page/NucleusResearch.htm

IBM offers strong capabilities in information management, reporting and analysis, and with the addition of SPSS, now can offer users predictive power that leverages both structured and unstructured data. This provides IBM SPSS users a distinct advantage as advanced analytics becomes a mainstream table stake in today’s hyper-competitive marketplace. Source: Nucleus Research Report: IBM and SPSS: Analytics for Everyone, available at http://www.spss.com/media/collateral/programs/IBM-and-SPSS-Nucleus.pdf

Where predictive analytics can help in private and public sectors?

Most commercial organizations share similar goals in private sector -
Typical application areas are as follows:
  • Attracting the best, most profitable customers through well-targeted campaigns.
  • Increasing revenues through cross- and up-sell to new and existing customers.
  • Reducing defection of high-quality customers and, conversely, identifying those who are costly and should be allowed to go through attrition.
  • Minimizing the effect of fraudulent activity by focusing the work of investigators appropriately.
  • Increasing customer satisfaction through faster response and processing of legitimate claims.
  • Building customer loyalty through effective and reliable inventory management.
  • Reducing operating costs by predicting maintenance needs proactively.

Typical public sector application areas are as follows:

  • Government agencies manage functions as diverse as tax audit selections, military force recruitment, and proactive policing and public safety.
  • Healthcare organizations seek to proactively manage their resources and fine-tune their practices to provide better patient care.
  • Colleges and universities manage the entire student life cycle more efficiently, recruiting the right mix of students, offering students a selection of programs and assistance to keep them enrolled, and managing alumni development programs with greater success.


Predictive analytics helps your organization predict with confidence what will happen next so that you can make smarter decisions and improve business outcomes. IBM offers easy-to-use predictive analytics products and solutions that meet the specific needs of different users and skill levels from beginners to experienced analysts.

With IBM SPSS predictive analytics software, you can:
  • Transform data into predictive insights to guide front-line decisions and interactions.
  • Predict what customers want and will do next to increase profitability and retention.
  • Maximize the productivity of your people, processes and assets.
  • Detect and prevent threats and fraud before they affect your organization.
  • Measure the social media impact of your products, services and marketing campaigns.
  • Perform statistical analysis including regression analysis, cluster analysis and correlation analysis.

The SPSS portfolio is designed to serve the three main phases of the analytical process, capture, predict, and act.

         Capture information
        Ability to capture attributes, interactions, behaviors, and attitudes for customers, employees or constituents
        Data collection capabilities for market  research and feedback management

         Predict behavior and preferences
        Top down statistical analysis, useful for all  data types and frequently used for survey  data, delivers deeper insight
        Data Mining enables predictive modeling
        Text Analytics extracts and categorizes concepts from unstructured text, making qualitative data more quantifiable and delivering new insights

         Act on results
        Unique technology and methodology streamlines deployment of analytical results throughout the enterprise to enable better decision making
        Provides reliable automation of analytical processes for better orchestration & discipline
        Enables collaboration to deliver more effective analytical results



Can you please explain SPSS product portfolio in detail?


Here we’ll discuss the full suite of IBM SPSS Predictive Analytics software:



  • IBM SPSS Data Collection for capturing attitudes, preferences, and feedback [Capture]
  • IBM SPSS Statistics Suite for research and analysis [Predict]
  • IBM SPSS Modeler for predicting future behavior [Predict]
  • IBM SPSS Decision Management for optimizing operational decisions [Act]
  • IBM SPSS Collaboration and Deployment Services for enterprise-wide management of analytical assets and results [Act]

IBM SPSS Data Collection is a complete suite of products for survey, market, or business researchers. It enables you to quickly and efficiently acquire clean data from the widest range of sources by using an expansive array of methods, and actively bring data about people’s attitudes and preferences into your analytical decision-making. It is the best way to capture a complete perspective about your important constituents, making research efforts more accurate and more efficient.

This is increasingly important as business today demands faster, more representative and more cost-effective surveys for deeper insight into thoughts and opinions of customers. Both commercial organizations and market research firms rely on data collection’s advanced technologies.



IBM SPSS Modeler is a predictive analytics platform that is designed to bring predictive intelligence to decisions made by individuals, groups, systems and the enterprise. It provides a range of advanced algorithms and techniques, including text analytics, entity analytics, decision management and optimization, to help you select the actions that result in better outcomes. Available in several editions, SPSS Modeler can scale from desktop deployments to integration within operational systems. Key benefits of using IBM SPSS Modeler are as follows:
  • Access, prepare, and integrate structured data and text, and web and survey data.
  • Support the entire data mining process with a broad set of tools that are based on Cross Industry Standard Process for Data Mining (CRISP-DM) methodology.
  • Identify and extract sentiments from text in more than 30 languages and use this insight to build more accurate predictive models.
  • Deploy textual insights so your entire organization benefits from a comprehensive, 360-degree view of the people you serve.


IBM SPSS Modeler help analysts build accurate predictive models quickly and intuitively, without programming. Modeling, also known as data mining, helps organizations take seemingly unrelated data and find hidden relationships in data. Using these models, an organization can look into the future and understand what will happen in any current or future case based on what has happened before. From predicting which offer will have the most impact, to understanding and preventing churn, the modeling family helps people consistently make decisions, maximizing the results. This process repeatability makes modeling a powerful tool for embedding best practices inside the systems and processes of a business. In addition to predicting outcomes, models can explain what factors influence them so users can take advantage of opportunities and mitigate risks.  Please visit http://www.ibm.com/developerworks/industry/library/ba-predictive-analytics2 for more details.

IBM SPSS Statistics uses sophisticated mathematics to help researchers validate assumptions and test hypotheses. From testing opinions about the latest product feature ideas or the viability of a political candidate, to the efficacy of a new drug treatment or prospective supply-chain allocation, statistics enables an organization to look at the beliefs of an organization and validate whether those views are based in fact. Statistics can give you confidence in the results and final outcomes of decisions you make.

SPSS Statistics provides essential statistical analysis tools for every step of the analytical process.  It is used by commercial, government, and academic organizations to solve business and research problems. IBM SPSS Statistics is one of the most accessible statistics tool in the market, enabling organizations to apply mathematical discipline to their decision-making.

  • A comprehensive range of statistical procedures for conducting accurate analysis.
  • Built-in techniques to prepare data for analysis quickly and easily.
  • Sophisticated reporting functionality for highly effective chart creation.
  • Powerful visualization capabilities that clearly show the significance of your findings.
  • Support for all types of data including very large data sets.



The deployment family of SPSS includes the following products:
  • Decision Management
  • Collaboration and Deployment Services

The deployment family can help you maximize the impact of analytics in your operation by embedding the results of your analytic efforts in the hearts of enterprise systems. Deployment is about making analytics practical for the people who handle real-world challenges every day. From helping the call center agent by alerting them to the risk of a churning customer, to recommending corrective action for a failing student, the deployment family ensures that the processes of your organization operate at peak efficiency and that objectives are met.

SPSS Decision Management harnesses the power of a variety of technologies that IBM offers, such as data mining, business intelligence, rules, event processing, data management, and then blends them all together. It is a great mashup. No longer is the business solely reliant on back-office data analysts, data miners, or expert statisticians. It facilitates the ability to create web-based, business user applications that are designed for a specific business problem. These applications help business people participate in the use of predictive analytics to meet their challenges.

Decision Management is a highly effective method for optimizing and automating your business decisions. It also helps organizations make the best decisions in real time. Supported by predictive analytics, it offers organizations the ability to move beyond reactive decisions to anticipate which actions are most likely to create successful outcomes in the future. All this technology is wrapped in a graphical user interface (GUI), using language that is familiar and meaningful to the business user.



Decision Management features:
  • Predictive tools and mathematical techniques to optimize transactional decisions.
  • Combined and integrated predictive models, rules and decision logic to deliver recommended actions.
  • “What if…” simulations to accommodate changing conditions based on the volume, variety and velocity of incoming data.
  • A flexible and intuitive user interface to support the development and implementation of targeted configurations and content.
  • Seamless integration with IBM Business Analytics and other software solutions.
SPSS Collaboration and Deployment Services (CD&S) lets you manage analytical assets, automate processes and efficiently share results widely and securely. Because when the people developing and the people using analytics can collaborate, your analytic efficiency increases.
SPSS CD&S capabilities can be described under these 3 headings:
Collaboration refers to the ability to share and reuse analytical assets efficiently, and is the key to developing and implementing analytics across an enterprise.
  • Analysts place files in C&DS repository that are made available to other analysts or business users with appropriate permissions.
  • The repository offers a search facility to assist users in finding assets, and backup and restore mechanism to protect the business from losing these crucial assets.
  • Logging features provide the ability to track file and system modifications.
Automate so you can construct flexible analytical processes that can be can be deployed throughout your operations – ensuring consistent results.
  • C&DS brings greater consistency to results by giving analysts the power to construct flexible, repeatable analytical processes, these analytical processes can be operationalized.
  • C&DS enables management to efficiently govern the analytical environments in which automated processes take place.
  • Analytical processes can be defined and executed in job. A job is a container for a set of steps. Each step has parameters associated with it. Before you execute a step, you must embed it within a job. Individual files stored in the repository can be included in processing jobs as job steps. Job steps can be executed sequentially or conditionally. The execution results can be stored in the repository, or on a file system. More important, the jobs themselves can be triggered according to defined time-based or message-based schedules.
Deploy by embedding analytic results in front-line business processes while integrating with your existing infrastructure with standard programming tools and interfaces.
  • C&DS supports application server clustering to optimize the performance of application.
  • Single sign-on reduces the need to manually provide credentials. Moreover, the system can be configured to be compliant with Federal Information Processing Standard for encryption (AES algorithm).
  • The scoring service of C&DS allows analytical results from deployed models to be delivered in real time when interacting with a customer. An analytical model configured for scoring can combine data collected from a current customer interaction with historical data to produce a score.
  • The deployment facilities of C&DS are designed to easily integrate with your enterprise infrastructure and other SPSS products, and built with enterprise readiness in mind.


Reference:

1)     Redpaper - IBM SPSS predictive analytics: Optimizing decisions at the point of impact [www.redbooks.ibm.com/redpapers/pdfs/redp4710.pdf‎]
2)     Seven Reasons You Need Predictive Analytics Today [http://www.ibm.com/software/products/en/category/predictive-analytics]
3)     Predicting the future, Part 1: What is predictive analytics? [http://www.ibm.com/developerworks/industry/library/ba-predictive-analytics1]
4)     Predicting the future, Part 2: Predictive modeling techniques [http://www.ibm.com/developerworks/industry/library/ba-predictive-analytics2]
5)     Predicting the future, Part 3: Create a predictive solution [http://www.ibm.com/developerworks/industry/library/ba-predictive-analytics3]
6)     Predicting the future, Part 4: Put a predictive solution to work [http://www.ibm.com/developerworks/industry/library/ba-predictive-analytics4]

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