SAS Analytics Day Age of Analytics: Competing in the 21 st Century Dr. Radhika Kulkarni Vice President, Advanced Analytics R&D SAS Institute April 22, 2011
Outline Key challenges in today s marketplace What is Business Analytics? Responding to the market trends Educating the next generation
Harnessing Value from the Data Data Deluge Data-Driven Insights
Global Challenges Proactive Decision Making using Advanced Analytics What happened? Where exactly is the problem? What if these trends continue? How many, how often, where? What s the best that can happen? What will happen next? What actions are needed? Why is this happening?
Common Factors in Analytical Problems Large data volumes needing Flexible models Powerful algorithms Effective visualization techniques Easy deployment to enable wider access to the power of analytics
Addressing the Challenges: Let s take a look behind the scenes at SAS R&D
SAS Research & Development 2500+ software developers in Cary, Pune, Beijing 200+ Ph.D. specialists in statistics, data mining, optimization, applied math, numerical analysis, Software tools used by statisticians, researchers, data miners Analytical components for business solutions SAS Campus, Cary, North Carolina
Decision Making: Tools Needed Data Access Query and reporting Interactive exploration and visualization Estimation Forecasting Data mining Predictive modeling Optimization Deployment tools 8
Analytical Products SAS/STAT 1972 SAS/ETS 1980 SAS/OR 1982 SAS/IML 1986 SAS/QC 1986 JMP 1989. SAS Enterprise Miner 1998 SAS Forecast Server 2005 SAS Text Miner 2005.. 9
The Whole is Greater than the Sum of its Parts Each technology works well on its own, but combining them all is the real opportunity Forecasting Optimization Service Intelligence Statistics Data Mining Quality Improvement Forecasting Statistical Modeling Service Parts Optimization Optimization Quality Improvement Data Management Data Management
Design of the SAS System -- Today 11
Business Analytics What is it? The use of statistical analysis, data mining, forecasting, and optimization to make critical business decisions based on customer and operational data.
Trends: Information Explosion
Trends: Distributed Computing Gridded environments and multicore processors are increasingly cost effective Performance is gained by breaking work into tasks that can be done in parallel by nodes or processes
Markdown Optimization in Retail Department stores and fashion retailers clear apparel inventory by reducing prices What prices should we set through the end of the season to meet inventory goals and maximize revenue? Typical Retail Merchandise Hierarchy: Distributed data Department Large volumes of data Algorithmic complexity Class Subclass Style 15
Business Problem Determine a price schedule in order to liquidate inventory profitably by a certain date for a variety of business reasons Drop prices increase demand (hopefully) When should I give a discount? How much? What happens to my margin?
Why an Analytical Solution? Huge number of decisions 50,000 products/store x 2,000 stores = 100,000,000 decisions! Large volume of weekly data for two-year moving window Number of units sold, price at point of sale, reference price Starting and ending inventory in each store Promotions, TV ads, circulars, Building blocks Statistical model for demand Price optimization
Distributed computing Where else will we see need? Risk, medical applications, social network applications, text search, retail, financial applications,. What are the challenges with distributed computing? Data storage Different algorithms Preference for solution techniques that exploit data structures
18 of 31 Marketing Optimization - Problem Description Customers Cost=$3.00 Expected Return=$5.50 Cost=$2.25 Expected Return=$4.90 Cost=$1.00 Expected Return=$3.90 Visa Classic / Direct Marketing Visa Classic / Call Center Visa Classic / Branch Visa Gold / Direct Marketing Visa Gold / Call Center Visa Gold / Branch Visa Value / Direct Marketing Visa Value / Call Center Visa Value / Branch Products x Channels Common problem in multiple applications
Cross-Sell / Upsell opportunity Integrate optimization into campaign management Business Issue Grow market share Increase revenue per acquired customer Retain and upgrade existing customers Results/Benefits Reduction in campaign costs Better response rates Increased Campaign ROI 3 month implementation ROI in months
Call Center Offer Optimization Expanding the reach of analytical solutions Provider of Digital TV, broadband Business Issue Competitive market Use every contact point to cross-sell / upsell Solution Deliver analytical results to call center employees Results/Benefits Increased customer loyalty and retention Increased revenue Democratization of Analytics?
Operationalizing Analytics Scalability Similar business problem in database analytics Business Issue Analyze massive amounts of data to accurately predict which customers would be most likely to respond to targeted marketing offers. 250M transactions / week Solution SAS Enterprise Miner to build appropriate loyalty programs Results/Benefits Deliver the right offer to the right customer, right at the point of sale. Customer responses as high as 25%. Reduced processing time from a month to a few days
Key Takeaways from the Last 3 Examples Gain value by being part of an integrated solution Optimization parameters obtained as scores from a data mining model Results of optimization feed back into a campaign management system Expand reach by operationalizing the application Present offers at the right time via the call center Customized offers presented at the checkout counter Deliver the right offer to the right customer, right at the point of sale. Exploit in-database computations to minimize data movement (for the input scores to the optimization model)
Trends: Operationalizing analytics Where will we see need? Call center offers Real-time offers in retail Credit card fraud prevention Instrument solutions to self-monitor What are the challenges? Ability to embed algorithms in a business process Delivery mechanism for Software as a Service Automation of a workflow with ability to run instantaneous analytical decision support analytical engines
Trends: Integrating Modeling & Visualization -- Social Network Analysis Applications Fraud detection Terrorist network detection Targeted marketing Software needs Visualization Grid computing Algorithms
Trends: Use of Unstructured Data Unstructured data Structured data 25% 5% 70% Semistructured data
Uses for Text Analytics Text Analytics has numerous applications Retail Identify the most profitable customers and the underlying reasons for their loyalty. Finance Retention of current customer base using call center transcriptions or transcribed audio. Insurance Identify fraudulent claims. Track competitive intelligence. Brand management Manufacturing Reduce time to detect root cause of product issues. Identify trends in market segments. Government Early warning of terrorist behavior Healthcare fraud Corruption Life Sciences Identify adverse events. Recommend appropriate research materials.
What Does All of This Mean for Us?
Data Are Driving the Demand for Analytical Experts Critical business problems are increasingly characterized by large volumes of data and the need for rapid analysis. Organizations are competing on analytics, and they value data and analytics as strategic assets. There is a global demand for analytical experts who can Formulate and solve problems using a broad ensemble of methods Collaborate with interdisciplinary teams on analytical software solutions Davenport and Harris
Educate the next generation Need to get it right Professional programs should be (real-world) business-focused Embrace all areas of analytics Data integration, forecasting, predictive, optimization and simulation. Equip students to formulate and solve problems with large and diverse data sets Train more students in cross-discipline skills and computational techniques including knowledge of machine architecture and how to exploit it Partner with business and industry Internships Funding for new programs and research, including applied work
Collaborations Universities Graduate internship Summer fellowship Student sponsorship Consulting engagements Master s in Analytics program at NCSU Professional societies Participation in Meetings / Workshops / Symposia Board and committee membership Customers Development partnerships Consulting engagements 31
University Teaching Teaching Materials Joint Certificate Programs OnDemand Software Curriculum Consulting On-site workshops Assistance with new programs Guest presentations
SAS OnDemand for Academics OnDemand for Academics Globally Available No cost access Broadband speed required 3 pieces of software Enterprise Guide Enterprise Miner Forecast Server Servers in US, Germany Singapore & Australia 2011
For more details contact : Dr. Radhika Kulkarni Radhika.Kulkarni@sas.com