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SAS Institute Financials 2011
SAS Institute has release it’s financials for 2011 at http://www.sas.com/news/preleases/2011financials.html,
Revenue surged across all solution and industry categories. Software to detect fraud saw a triple-digit jump. Revenue from on-demand solutions grew almost 50 percent. Growth from analytics and information management solutions were double digit, as were gains from customer intelligence, retail, risk and supply chain solutions
AJAY- and as a private company it is quite nice that they are willing to share so much information every year.
The graphics are nice ( and the colors much better than in 2010) , but pie-charts- seriously dude there is no way to compare how much SAS revenue is shifting across geographies or even across industries. So my two cents is – lose the pie charts, and stick to line graphs please for the share of revenue by country /industry.
In 2011, SAS grew staff 9.2 percent and reinvested 24 percent of revenue into research and development
AJAY- So that means 654 million dollars spent in Research and Development. I wonder if SAS has considered investing in much smaller startups (than it’s traditional strategy of doing all research in-house and completely acquiring a smaller company)
Even a small investment of say 5-10 million USD in open source , or even Phd level research projects could greatly increase the ROI on that.
That means
Analyzing a private company’s financials are much more fun than a public company, and I remember the words of my finance professor ( “dig , dig”) to compare 2011 results with 2010 results.
http://www.sas.com/news/preleases/2010financials.html
The percentage invested in R and D is exactly the same (24%) and the percentages of revenue earned from each geography is exactly the same . So even though revenue growth increased from 5.2 % to 9% in 2011, both the geographic spread of revenues and share R&D costs remained EXACTLY the same.
The Americas accounted for 46 percent of total revenue; Europe, Middle East and Africa (EMEA) 42 percent; and Asia Pacific 12 percent.
Overall, I think SAS remains a 35% market share (despite all that noise from IBM, SAS clones, open source) because they are good at providing solutions customized for industries (instead of just software products), the market for analytics is not saturated (it seems to be growing faster than 12% or is it) , and its ability to attract and retain the best analytical talent (which in a non -American tradition for a software company means no stock options, job security, and great benefits- SAS remains almost Japanese in HR practices).
In 2010, SAS grew staff by 2.4 percent, in 2011 SAS grew staff by 9 percent.
But I liked the directional statement made here-and I think that design interfaces, algorithmic and computational efficiencies should increase analytical time, time to think on business and reduce data management time further!
“What would you do with the extra time if your code ran in two minutes instead of five hours?” Goodnight challenged.
Interview Jaime Fitzgerald President Fitzgerald Analytics
Here is an interview with noted analytics expert Jaime Fitzgerald, of Fitzgerald Analytics.
Ajay-Describe your career journey from being a Harvard economist to being a text analytics thought leader.
Jaime- I was attracted to economics because of the logic, the structured and systematic approach to understanding the world and to solving problems. In retrospect, this is the same passion for logic in problem solving that drives my business today.
About 15 years ago, I began working in consulting and initially took a traditional career path. I worked for well-known strategy consulting firms including First Manhattan Consulting Group, Novantas LLC, Braun Consulting, and for the former Japan-focused division of Deloitte Consulting, which had spun off as an independent entity. I was the only person in their New York City office for whom Japanese was not the first language.
While I enjoyed traditional consulting, I was especially passionate about the role of data, analytics, and process improvement. In traditional strategy consulting, these are important factors, but I had a vision for a “next generation” approach to strategy consulting that would be more transparent, more robust, and more focused on the role that information, analysis, and process plays in improving business results. I often explain that while my firm is “not your father’s consulting model,” we have incorporated key best practices from traditional consulting, and combined them with an approach that is more data-centric, technology-centric, and process-centric.
At the most fundamental level, I was compelled to found Fitzgerald Analytics more than six years ago by my passion for the role information plays in improving results, and ultimately improving lives. In my vision, data is an asset waiting to be transformed into results, including profit as well as other results that matter deeply to people. For example,one of the most fulfilling aspects of our work at Fitzgerald Analytics is our support of non-profits and social entrepreneurs, who we help increase their scale and their success in achieving their goals.
Ajay- How would you describe analytics as a career option to future students. What do you think are the most essential qualities an analytics career requires.
Jaime- My belief is that analytics will be a major driver of job-growth and career growth for decades. We are just beginning to unlock the full potential of analytics, and already the demand for analytic talent far exceeds the supply.
To succeed in analytics, the most important quality is logic. Many people believe that math or statistical skills are the most important quality, but in my experience, the most essential trait is what I call “ThoughtStyle” — critical thinking, logic, an ability to break down a problem into components, into sub-parts.
Ajay -What are your favorite techniques and methodologies in text analytics. How do you see social media and Big Data analytics as components of text analytics
Jaime-We do a lot of work for our clients measuring Customer Experience, by which I mean the experience customers have when interacting with our clients. For example, we helped a major brokerage firm to measure 12 key “Moments that Matter,” including the operational aspects of customer service, customer satisfaction and sentiment, and ultimately customer behavior. Clients care about this a lot, because customer experience drives customer loyalty, which in turn drives customer behavior, customer loyalty, and customer profitability.
Text analytics plays a key role in these projects because much of our data on customer sentiment comes via unstructured text data. For example, we have access to call center transcripts and notes, to survey responses, and to social media comments.
We use a variety of methods, some of which I’m not in a position to describe in great detail. But at a high level, I would say that our favorite text analytics methodologies are “hybrid solutions” which use a two-step process to answer key questions for clients:
Step 1: convert unstructured data into key categorical variables (for example, using contextual analysis to flag users who are critical vs. neutral vs. advocates)
Step 2: linking sentiment categories to customer behavior and profitability (for example, linking customer advocacy and loyalty with customer profits as well as referral volume, to define the ROI that clients accrue for customer satisfaction improvements)
Ajay- Describe your consulting company- Fitzgerald Analytics and some of the work that you have been engaged in.
Jaime- Our mission is to “illuminate reality” using data and to convert Data to Dollars for our clients. We have a track record of doing this well, with concrete and measurable results in the millions of dollars. As a result, 100% of our clients have engaged us for more than one project: a 100% client loyalty rate.
Our specialties–and most frequent projects–include customer profitability management projects, customer segmentation, customer experience management, balanced scorecards, and predictive analytics. We are often engaged to address high-stakes analytic questions, including issues that help to set long-term strategy. In other cases, clients hire us to help them build their internal capabilities. We have helped build several brand new analytic teams for clients, which continue to generate millions of dollars of profits with their fact-based recommendations.
Our methodology is based on Steven Covey’s principle: “begin with the end in mind,” the concept of starting with the client’s goal and working backwards from there. I often explain that our methods are what you would have gotten if Steven Covey had been a data analyst…we are applying his principles to the world of data analytics.
Ajay- Analytics requires more and more data while privacy requires the least possible data. What do you think are the guidelines that need to be built in sharing internet browsing and user activity data and do we need regulations just like we do for sharing financial data.
Jaime- Great question. This is an essential challenge of the big data era. My perspective is that firms who depend on user data for their analysis need to take responsibility for protecting privacy by using data management best practices. Best practices to adequately “mask” or remove private data exist…the problem is that these best practices are often not applied. For example, Facebook’s practice of sharing unique user IDs with third-party application companies has generated a lot of criticism, and could have been avoided by applying data management best practices which are well known among the data management community.
If I were able to influence public policy, my recommendation would be to adopt a core set of simple but powerful data management standards that would protect consumers from perhaps 95% of the privacy risks they face today. The number one standard would be to prohibit sharing of static, personally identifiable user IDs between companies in a manner that creates “privacy risk.” Companies can track unique customers without using a static ID…they need to step up and do that.
Ajay- What are your favorite text analytics software that you like to work with.
Jaime- Because much of our work in deeply embedded into client operations and systems, we often use the software our clients already prefer. We avoid recommending specific vendors unless our client requests it. In tandem with our clients and alliance partners, we have particular respect for Autonomy, Open Text, Clarabridge, and Attensity.
Biography-
http://www.fitzgerald-analytics.com/jaime_fitzgerald.html
The Founder and President of Fitzgerald Analytics, Jaime has developed a distinctively quantitative, fact-based, and transparent approach to solving high stakes problems and improving results. His approach enables translation of Data to Dollars™ using methodologies clients can repeat again and again. He is equally passionate about the “human side of the equation,” and is known for his ability to link the human and the quantitative, both of which are needed to achieve optimal results.
Experience: During more than 15 years serving clients as a management strategy consultant, Jaime has focused on customer experience and loyalty, customer profitability, technology strategy, information management, and business process improvement. Jaime has advised market-leading banks, retailers, manufacturers, media companies, and non-profit organizations in the United States, Canada, and Singapore, combining strategic analysis with hands-on implementation of technology and operations enhancements.
Career History: Jaime began his career at First Manhattan Consulting Group, specialists in financial services, and was later a Co-Founder at Novantas, the strategy consultancy based in New York City. Jaime was also a Manager for Braun Consulting, now part of Fair Isaac Corporation, and for Japan-based Abeam Consulting, now part of NEC.
Background: Jaime is a graduate of Harvard University with a B.A. in Economics. He is passionate and supportive of innovative non-profit organizations, their effectiveness, and the benefits they bring to our society.
Upcoming Speaking Engagements: Jaime is a frequent speaker on analytics, information management strategy, and data-driven profit improvement. He recently gave keynote presentations on Analytics in Financial Services for The Data Warehousing Institute, the New York Technology Council, and the Oracle Financial Services Industry User Group. A list of Jaime’s most interesting presentations on analyticscan be found here.
He will be presenting a client case study this fall at Text Analytics World re: “New Insights from ‘Big Legacy Data’: The Role of Text Analytics”
Connecting with Jaime: Jaime can be found at 
Linkedin, and 
Twitter. He edits the Fitzgerald Analytics Blog.
Carole-Ann’s 2011 Predictions for Decision Management
For Ajay Ohri on DecisionStats.com
What were the top 5 events in 2010 in your field?
- Maturity: the Decision Management space was made up of technology vendors, big and small, that typically focused on one or two aspects of this discipline. Over the past few years, we have seen a lot of consolidation in the industry – first with Business Intelligence (BI) then Business Process Management (BPM) and lately in Business Rules Management (BRM) and Advanced Analytics. As a result the giant Platform vendors have helped create visibility for this discipline. Lots of tiny clues finally bubbled up in 2010 to attest of the increasing activity around Decision Management. For example, more products than ever were named Decision Manager; companies advertised for Decision Managers as a job title in their job section; most people understand what I do when I am introduced in a social setting!
- Boredom: unfortunately, as the industry matures, inevitably innovation slows down… At the main BRMS shows we heard here and there complaints that the technology was stalling. We heard it from vendors like Red Hat (Drools) and we heard it from bored end-users hoping for some excitement at Business Rules Forum’s vendor panel. They sadly did not get it
- Scrum: I am not thinking about the methodology there! If you have ever seen a rugby game, you can probably understand why this is the term that comes to mind when I look at the messy & confusing technology landscape. Feet blindly try to kick the ball out while superhuman forces are moving randomly the whole pack – or so it felt when I played! Business Users in search of Business Solutions are facing more and more technology choices that feel like comparing apples to oranges. There is value in all of them and each one addresses a specific aspect of Decision Management but I regret that the industry did not simplify the picture in 2010. On the contrary! Many buzzwords were created or at least made popular last year, creating even more confusion on a muddy field. A few examples: Social CRM, Collaborative Decision Making, Adaptive Case Management, etc. Don’t take me wrong, I *do* like the technologies. I sympathize with the decision maker that is trying to pick the right solution though.
- Information: Analytics have been used for years of course but the volume of data surrounding us has been growing to unparalleled levels. We can blame or thank (depending on our perspective) Social Media for that. Sites like Facebook and LinkedIn have made it possible and easy to publish relevant (as well as fluffy) information in real-time. As we all started to get the hang of it and potentially over-publish, technology evolved to enable the storage, correlation and analysis of humongous volumes of data that we could not dream of before. 25 billion tweets were posted in 2010. Every month, over 30 billion pieces of data are shared on Facebook alone. This is not just about vanity and marketing though. This data can be leveraged for the greater good. Carlos pointed to some fascinating facts about catastrophic event response team getting organized thanks to crowd-sourced information. We are also seeing, in the Decision management world, more and more applicability for those very technology that have been developed for the needs of Big Data – I’ll name for example Hadoop that Carlos (yet again) discussed in his talks at Rules Fest end of 2009 and 2010.
- Self-Organization: it may be a side effect of the Social Media movement but I must admit that I was impressed by the success of self-organizing initiatives. Granted, this last trend has nothing to do with Decision Management per se but I think it is a great evolution worth noting. Let me point to a couple of examples. I usually attend traditional conferences and tradeshows in which the content can be good but is sometimes terrible. I was pleasantly surprised by the professionalism and attendance at *un-conferences* such as P-Camp (P stands for Product – an event for Product Managers). When you think about it, it is already difficult to get a show together when people are dedicated to the tasks. How crazy is it to have volunteers set one up with no budget and no agenda? Well, people simply show up to do their part and everyone has fun voting on-site for what seems the most appealing content at the time. Crowdsourcing applied to shows: it works! Similar experience with meetups or tweetups. I also enjoyed attending some impromptu Twitter jam sessions on a given topic. Social Media is certainly helping people reach out and get together in person or virtually and that is wonderful!
Image via Wikipedia
What are the top three trends you see in 2011?
- Performance: I might be cheating here. I was very bullish about predicting much progress for 2010 in the area of Performance Management in your Decision Management initiatives. I believe that progress was made but Carlos did not give me full credit for the right prediction… Okay, I am a little optimistic on timeline… I admit it… If it did not fully happen in 2010, can I predict it again in 2011? I think that companies want to better track their business performance in order to correct the trajectory of course but also to improve their projections. I see that it is turning into reality already here and there. I expect it to become a trend in 2011!
- Insight: Big Data being available all around us with new technologies and algorithms will continue to propagate in 2011 leading to more widely spread Analytics capabilities. The buzz at Analytics shows on Social Network Analysis (SNA) is a sign that there is interest in those kinds of things. There is tremendous information that can be leveraged for smart decision-making. I think there will be more of that in 2011 as initiatives launches in 2010 will mature into material results.
Image by Intersection Consulting via Flickr
- Collaboration: Social Media for the Enterprise is a discipline in the making. Social Media was initially seen for the most part as a Marketing channel. Over the years, companies have started experimenting with external communities and ideation capabilities with moderate success. The few strategic initiatives started in 2010 by “old fashion” companies seem to be an indication that we are past the early adopters. This discipline may very well materialize in 2011 as a core capability, well, or at least a new trend. I believe that capabilities such Chatter, offered by Salesforce, will transform (slowly) how people interact in the workplace and leverage the volumes of social data captured in LinkedIn and other Social Media sites. Collaboration is of course a topic of interest for me personally. I even signed up for Kare Anderson’s collaboration collaboration site – yes, twice the word “collaboration”: it is really about collaborating on collaboration techniques. Even though collaboration does not require Social Media, this medium offers perspectives not available until now.
Brief Bio-
Carole-Ann is a renowned guru in the Decision Management space. She created the vision for Decision Management that is widely adopted now in the industry. Her claim to fame is the strategy and direction of Blaze Advisor, the then-leading BRMS product, while she also managed all the Decision Management tools at FICO (business rules, predictive analytics and optimization). She has a vision for Decision Management both as a technology and a discipline that can revolutionize the way corporations do business, and will never get tired of painting that vision for her audience. She speaks often at Industry conferences and has conducted university classes in France and Washington DC.
Leveraging her Masters degree in Applied Mathematics / Computer Science from a “Grande Ecole” in France, she started her career building advanced systems using all kinds of technologies — expert systems, rules, optimization, dashboarding and cubes, web search, and beta version of database replication – as well as conducting strategic consulting gigs around change management.
She now tweets as @CMatignon, blogs at blog.sparklinglogic.com and interacts at community.sparklinglogic.com.
She started her career building advanced systems using all kinds of technologies — expert systems, rules, optimization, dashboarding and cubes, web search, and beta version of database replication. At Cleversys (acquired by Kurt Salmon & Associates), she also conducted strategic consulting gigs mostly around change management.
While playing with advanced software components, she found a passion for technology and joined ILOG (acquired by IBM). She developed a growing interest in Optimization as well as Business Rules. At ILOG, she coined the term BRMS while brainstorming with her Sales counterpart. She led the Presales organization for Telecom in the Americas up until 2000 when she joined Blaze Software (acquired by Brokat Technologies, HNC Software and finally FICO).
Her 360-degree experience allowed her to gain appreciation for all aspects of a software company, giving her a unique perspective on the business. Her technical background kept her very much in touch with technology as she advanced.
She also became addicted to Twitter in the process. She is active on all kinds of social media, always looking for new digital experience!
Outside of work, Carole-Ann loves spending time with her two boys. They grow fruits in their Northern California home and cook all together in the French tradition.
Follow me on Twitter
Image by Intersection Consulting via Flickr
Image by fredcavazza via Flickr
Related Articles
- Business Analytics Predictions from Gartner and Forrester (readwriteweb.com)
- 5 Big Themes in BI for 2011 (informationweek.com)
- Unfinished Business: Questions Social Media Must Answer In 2011 – Liza With A “Z” (lizasperling.com)
- Gartner Predicts Top 10 Technologies for 2011 (marketingtechblog.com)
- How Will Technology Disrupt the Enterprise in 2011? (readwriteweb.com)
- Social ecosystems tracked by Xeesm social graphs (wendysoucie.com)
- Social media can enhance the buying decision (customerthink.com)
- Top Ten Predictions for 2011 (enterpriseirregulars.com)
- Open Data: Why the Crowd Can Be Your Best Analytics Tool (mashable.com)
- Closing the Loop: NCDM, the Future of Customer Interactions and Maturing Social Marketing Practices (customerthink.com)
- Your 2011 Social Media Quick Start! (socialmediadudes.com)
- A Sure Sign News Teases Don’t Work in Social Media (journalistics.com)
- Social Media Strategists Look Hard At ROI This Year (webguild.org)
PAW Videos
A message from Predictive Analytics World on newly available videos. It has many free videos as well so you can check them out.
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Access PAW DC Session Videos Now Predictive Analytics World is pleased to announce on-demand access to the videos of PAW Washington DC, October 2010, including over 30 sessions and keynotes that you may view at your convenience. Access this leading predictive analytics content online now: View the PAW DC session videos online Register by January 18th and receive $150 off the full 2-day conference program videos (enter code PAW150 at checkout) Trial videos – view the following for no charge:
Select individual conference sessions, or recognize savings by registering for access to one or two full days of sessions. These on-demand videos deliver PAW DC right to your desk, covering hot topics and advanced methods such as:
PAW DC videos feature over 25 speakers with case studies from leading enterprises such as: CIBC, CEB, Forrester, Macy’s, MetLife, Microsoft, Miles Kimball, Monster.com, Oracle, Paychex, SunTrust, Target, UPMC, Xerox, Yahoo!, YMCA, and more. How video access works:
Sign up by January 18 for immediate video access and $150 discount
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Increasing views to Youtube Videos
Image via Wikipedia
The Youtube Promoted Videos (basically a video form of Adsense) can really help companies like Oracle, SAP, IBM, Netezza, SAS Insititute, AsterData, Rapid Miner, Pentaho, JasperSoft, Teradata, Revolution who create
either corporate videos/training videos or upload their seminar, webinar,conference videos to Youtube.
Making a video is hard work in itself- doing an A/ B test with Youtube Promoted videos might just get a better ROI for your video marketing budget and IMHO embeddable videos from Youtube are much better and easier to share than Videos that can be seen only after registration on a company web site. You want to get the word out for your software, or you want to get website views?
Related Articles
- How to Use YouTube’s Analytics for B2B Marketing (hubspot.com)
- YouTube Prepares to Offer Skippable Ads (marketingpilgrim.com)
- Will Hulu-Like Video Ads Work on YouTube? (nytimes.com)
- Nearing Profitability, YouTube Hits 500 Million Promoted Video Views (techcrunch.com)
Quantifying Analytics ROI
Image via Wikipedia
I had a brief twitter exchange with Jim Davis, Chief Marketing Officer, SAS Institute on Return of Investment on Business Analytics Projects for customers. I have interviewed Jim Davis before last year http://decisionstats.com/2009/06/05/interview-jim-davis-sas-institute/
Now Jim Davis is a big guy, and he is rushing from the launch of SAS Institute’s Social Media Analytics in Japan- to some arguably difficult flying conditions in time to be home in America for Thanksgiving. That and and I have not been much of a good Blog Boy recently, more swayed by love of open source, than love of software per se. I love equally, given I am bad at both equally.
Anyways, Jim’s contention ( http://twitter.com/Davis_Jim ) was customers should go in business analytics only if there is Positive Return on Investment. I am quoting him here-
What is important is that there be a positive ROI on each and every BA project. Otherwise don’t do it.
That’s not the marketing I was taught in my business school- basically it was sell, sell, sell.
However I see most BI sales vendors also go through -let me meet my sales quota for this quarter- and quantifying customer ROI is simple maths than predictive analytics but there seems to be some information assymetry in it.
Here is a paper from North Western University on ROI in IT projects-.
but overall it would be in the interest of customers and Business Analytics Vendors to publish aggregated ROI.
The opponents to this transparency in ROI would be market leaders in market share, who have trapped their customers by high migration costs (due to complexity) or contractually.
A recent study listed Oracle having a large percentage of unhappy customers who would still renew!, SAP had problems when it raised prices for licensing arbitrarily (that CEO is now CEO of HP and dodging legal notices from Oracle).
Indeed Jim Davis’s famous unsettling call for focusing on Business Analytics,as Business Intelligence is dead- that call has been implemented more aggressively by IBM in analytical acquisitions than even SAS itself which has been conservative about inorganic growth. Quantifying ROI, should theoretically aid open source software the most (since they are cheapest in up front licensing) or newer technologies like MapReduce /Hadoop (since they are quite so fast)- but I think that market has a way of factoring in these things- and customers are not as foolish neither as unaware of costs versus benefits of migration.
The contrary to this is Business Analytics and Business Intelligence are imperfect markets with duo-poly or big players thriving in absence of customer regulation.
You get more protection as a customer of $20 bag of potato chips, than as a customer of a $200,000 software. Regulators are wary to step in to ensure ROI fairness (since most bright techies are qither working for private sector, have their own startup or invested in startups)- who in Govt understands Analytics and Intelligence strong enough to ensure vendor lock-ins are not done, and market flexibility is done. It is also a lower choice for embattled regulators to ensure ROI on enterprise software unlike the aggressiveness they have showed in retail or online software.
Who will Analyze the Analysts and who can quantify the value of quants (or penalize them for shoddy quantitative analytics)- is an interesting phenomenon we expect to see more of.
Related Articles
- SAS Turns out in Force for Internet Summit 2010 (eon.businesswire.com)
- From Web Analytics & SEM To Business Intelligence (searchengineland.com)
- Twitter Rolls Out Analytics Tool (hubspot.com)
- Social Media ROI: Customer Engagement, Brand Interactivity, And Revenue (lockergnome.com)
- Drowning in data? Start with a clear strategy to effectively measure your marketing. (kilgannonsays.wordpress.com)
- Twitter Testing Analytics Tool (informationweek.com)
PAWCON -This week in London
Watch out for the twitter hash news on PAWCON and the exciting agenda lined up. If your in the City- you may want to just drop in
http://www.predictiveanalyticsworld.com/london/2010/agenda.php#day1-7
Disclaimer- PAWCON has been a blog partner with Decisionstats (since the first PAWCON ). It is vendor neutral and features open source as well proprietary software, as well case studies from academia and Industry for a balanced view.
Little birdie told me some exciting product enhancements may be in the works including a not yet announced R plugin 
and the latest SAS product using embedded analytics and Dr Elder’s full day data mining workshop.
Citation-
http://www.predictiveanalyticsworld.com/london/2010/agenda.php#day1-7
Monday November 15, 2010
All conference sessions take place in Edward 5-7
Registration, Coffee and Danish
Room: Albert Suites
9:00am-9:50am
Keynote
Five Ways Predictive Analytics Cuts Enterprise Risk
All business is an exercise in risk management. All organizations would benefit from measuring, tracking and computing risk as a core process, much like insurance companies do.
Predictive analytics does the trick, one customer at a time. This technology is a data-driven means to compute the risk each customer will defect, not respond to an expensive mailer, consume a retention discount even if she were not going to leave in the first place, not be targeted for a telephone solicitation that would have landed a sale, commit fraud, or become a “loss customer” such as a bad debtor or an insurance policy-holder with high claims.
In this keynote session, Dr. Eric Siegel will reveal:
- Five ways predictive analytics evolves your enterprise to reduce risk
- Hidden sources of risk across operational functions
- What every business should learn from insurance companies
- How advancements have reversed the very meaning of fraud
- Why “man + machine” teams are greater than the sum of their parts for
enterprise decision support
Speaker: Eric Siegel, Ph.D., Program Chair, Predictive Analytics World
[ Top of this page ] [ Agenda overview ]
9:50am-10:10am
Platinum Sponsor Presentation
The Analytical Revolution
The algorithms at the heart of predictive analytics have been around for years – in some cases for decades. But now, as we see predictive analytics move to the mainstream and become a competitive necessity for organisations in all industries, the most crucial challenges are to ensure that results can be delivered to where they can make a direct impact on outcomes and business performance, and that the application of analytics can be scaled to the most demanding enterprise requirements.
This session will look at the obstacles to successfully applying analysis at the enterprise level, and how today’s approaches and technologies can enable the true “industrialisation” of predictive analytics.
Speaker: Colin Shearer, WW Industry Solutions Leader, IBM UK Ltd
[ Top of this page ] [ Agenda overview ]
10:10am-10:20am
Gold Sponsor Presentation
How Predictive Analytics is Driving Business Value
Organisations are increasingly relying on analytics to make key business decisions. Today, technology advances and the increasing need to realise competitive advantage in the market place are driving predictive analytics from the domain of marketers and tactical one-off exercises to the point where analytics are being embedded within core business processes.
During this session, Richard will share some of the focus areas where Deloitte is driving business transformation through predictive analytics, including Workforce, Brand Equity and Reputational Risk, Customer Insight and Network Analytics.
Speaker: Richard Fayers, Senior Manager, Deloitte Analytical Insight
[ Top of this page ] [ Agenda overview ]
10:20am-10:45am
Break / Exhibits
Room: Albert Suites
10:45am-11:35am
Healthcare
Case Study: Life Line Screening
Taking CRM Global Through Predictive Analytics
While Life Line is successfully executing a US CRM roadmap, they are also beginning this same evolution abroad. They are beginning in the UK where Merkle procured data and built a response model that is pulling responses over 30% higher than competitors. This presentation will give an overview of the US CRM roadmap, and then focus on the beginning of their strategy abroad, focusing on the data procurement they could not get anywhere else but through Merkle and the successful modeling and analytics for the UK.
Speaker: Ozgur Dogan, VP, Quantitative Solutions Group, Merkle Inc.
Speaker: Trish Mathe, Life Line Screening
[ Top of this page ] [ Agenda overview ]
11:35am-12:25pm
Open Source Analytics; Healthcare
Case Study: A large health care organization
The Rise of Open Source Analytics: Lowering Costs While Improving Patient Care
Rapidminer and R were the number 1 and 2 in this years annual KDNuggets data mining tool usage poll, followed by Knime on place 4 and Weka on place 6. So what’s going on here? Are these open source tools really that good or is their popularity strongly correlated with lower acquisition costs alone? This session answers these questions based on a real world case for a large health care organization and explains the risks & benefits of using open source technology. The final part of the session explains how these tools stack up against their traditional, proprietary counterparts.
Speaker: Jos van Dongen, Associate & Principal, DeltIQ Group
[ Top of this page ] [ Agenda overview ]
12:25pm-1:25pm
Lunch / Exhibits
Room: Albert Suites
1:25pm-2:15pm
Keynote
Thought Leader:
Case Study: Yahoo! and other large on-line e-businesses
Search Marketing and Predictive Analytics: SEM, SEO and On-line Marketing Case Studies
Search Engine Marketing is a $15B industry in the U.S. growing to double that number over the next 3 years. Worldwide the SEM market was over $50B in 2010. Not only is this a fast growing area of marketing, but it is one that has significant implications for brand and direct marketing and is undergoing rapid change with emerging channels such as mobile and social. What is unique about this area of marketing is a singularly heavy dependence on analytics:
- Large numbers of variables and options
- Real-time auctions/bids and a need to adjust strategies in real-time
- Difficult optimization problems on allocating spend across a huge number of keywords
- Fast-changing competitive terrain and heavy competition on the obvious channels
- Complicated interactions between various channels and a large choice of search keyword expansion possibilities
- Profitability and ROI analysis that are complex and often challenging
The size of the industry, its growing importance in marketing, its upcoming role in Mobile Advertising, and its uniquely heavy reliance on analytics makes it particularly interesting as an area for predictive analytics applications. In this session, not only will hear about some of the latest strategies and techniques to optimize search, you will hear case studies that illustrate the important role of analytics from industry practitioners.
Speaker: Usama Fayyad, , Ph.D., CEO, Open Insights
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2:15pm-2:35pm
Platinum Sponsor Presentation
Creating a Model Factory Using in-Database Analytics
With the ever-increasing number of analytical models required to make fact-based decisions, as well as increasing audit compliance regulations, it is more important than ever that these models can be created, monitored, retuned and deployed as quickly and automatically as possible. This paper, using a case study from a major financial organisation, will show how organisations can build a model factory efficiently using the latest SAS technology that utilizes the power of in-database processing.
Speaker: John Spooner, Analytics Specialist, SAS (UK)
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2:35pm-2:45pm
Session Break
Room: Albert Suites
2:45pm-3:35pm
Retail
Case Study: SABMiller
Predictive Analytics & Global Marketing Strategy
Over the last few years SABMiller plc, the second largest brewing company in the world operating in 70 countries, has been systematically segmenting its markets in different countries globally in order optimize their portfolio strategy & align it to their long term country specific growth strategy. This presentation talks about the overall methodology followed and the challenges that had to be overcome both from a technical as well as from a change management stand point in order to successfully implement a standard analytics approach to diverse markets and diverse business positions in a highly global setting.
The session explains how country specific growth strategies were converted to objective variables and consumption occasion segments were created that differentiated the market effectively by their growth potential. In addition to this the presentation will also provide a discussion on issues like:
- The dilemmas of static vs. dynamic solutions and standardization vs. adaptable solutions
- Challenges in acceptability, local capability development, overcoming implementation inertia, cost effectiveness, etc
- The role that business partners at SAB and analytics service partners at AbsolutData together play in providing impactful and actionable solutions
Speaker: Anne Stephens, SABMiller plc
Speaker: Titir Pal, AbsolutData
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Retail
Case Study: Overtoom Belgium
Increasing Marketing Relevance Through Personalized Targeting
Since many years, Overtoom Belgium – a leading B2B retailer and division of the French Manutan group – focuses on an extensive use of CRM. In this presentation, we demonstrate how Overtoom has integrated Predictive Analytics to optimize customer relationships. In this process, they employ analytics to develop answers to the key question: “which product should we offer to which customer via which channel”. We show how Overtoom gained a 10% revenue increase by replacing the existing segmentation scheme with accurate predictive response models. Additionally, we illustrate how Overtoom succeeds to deliver more relevant communications by offering personalized promotional content to every single customer, and how these personalized offers positively impact Overtoom’s conversion rates.
Speaker: Dr. Geert Verstraeten, Python Predictions
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Break / Exhibits
Room: Albert Suites
4:50pm-5:40pm
Uplift Modelling:
Case Study: Lloyds TSB General Insurance & US Bank
Uplift Modelling: You Should Not Only Measure But Model Incremental Response
Most marketing analysts understand that measuring the impact of a marketing campaign requires a valid control group so that uplift (incremental response) can be reported. However, it is much less widely understood that the targeting models used almost everywhere do not attempt to optimize that incremental measure. That requires an uplift model.
This session will explain why a switch to uplift modelling is needed, illustrate what can and does go wrong when they are not used and the hugely positive impact they can have when used effectively. It will also discuss a range of approaches to building and assessing uplift models, from simple basic adjustments to existing modelling processes through to full-blown uplift modelling.
The talk will use Lloyds TSB General Insurance & US Bank as a case study and also illustrate real-world results from other companies and sectors.
Speaker: Nicholas Radcliffe, Founder and Director, Stochastic Solutions
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Consumer services
Case Study: Canadian Automobile Association and other B2C examples
The Diminishing Marginal Returns of Variable Creation in Predictive Analytics Solutions
Variable Creation is the key to success in any predictive analytics exercise. Many different approaches are adopted during this process, yet there are diminishing marginal returns as the number of variables increase. Our organization conducted a case study on four existing clients to explore this so-called diminishing impact of variable creation on predictive analytics solutions. Existing predictive analytics solutions were built using our traditional variable creation process. Yet, presuming that we could exponentially increase the number of variables, we wanted to determine if this added significant benefit to the existing solution.
Speaker: Richard Boire, BoireFillerGroup
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Reception / Exhibits
Room: Albert Suites
Tuesday November 16, 2010
All conference sessions take place in Edward 5-7
Registration, Coffee and Danish
Room: Albert Suites
9:00am-9:55am
Keynote
Multiple Case Studies: Anheuser-Busch, Disney, HP, HSBC, Pfizer, and others
The High ROI of Data Mining for Innovative Organizations
Data mining and advanced analytics can enhance your bottom line in three basic ways, by 1) streamlining a process, 2) eliminating the bad, or 3) highlighting the good. In rare situations, a fourth way – creating something new – is possible. But modern organizations are so effective at their core tasks that data mining usually results in an iterative, rather than transformative, improvement. Still, the impact can be dramatic.
Dr. Elder will share the story (problem, solution, and effect) of nine projects conducted over the last decade for some of America’s most innovative agencies and corporations:
- Streamline:
- Cross-selling for HSBC
- Image recognition for Anheuser-Busch
- Biometric identification for Lumidigm (for Disney)
- Optimal decisioning for Peregrine Systems (now part of Hewlett-Packard)
- Quick decisions for the Social Security Administration
- Eliminate Bad:
- Tax fraud detection for the IRS
- Warranty Fraud detection for Hewlett-Packard
- Highlight Good:
- Sector trading for WestWind Foundation
- Drug efficacy discovery for Pharmacia & UpJohn (now Pfizer)
Moderator: Eric Siegel, Program Chair, Predictive Analytics World
Speaker: John Elder, Ph.D., Elder Research, Inc.
Also see Dr. Elder’s full-day workshop
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9:55am-10:30am
Break / Exhibits
Room: Albert Suites
10:30am-11:20am
Telecommunications
Case Study: Leading Telecommunications Operator
Predictive Analytics and Efficient Fact-based Marketing
The presentation describes what are the major topics and issues when you introduce predictive analytics and how to build a Fact-Based marketing environment. The introduced tools and methodologies proved to be highly efficient in terms of improving the overall direct marketing activity and customer contact operations for the involved companies. Generally, the introduced approaches have great potential for organizations with large customer bases like Mobile Operators, Internet Giants, Media Companies, or Retail Chains.
Main Introduced Solutions:-Automated Serial Production of Predictive Models for Campaign Targeting-Automated Campaign Measurements and Tracking Solutions-Precise Product Added Value Evaluation.
Speaker: Tamer Keshi, Ph.D., Long-term contractor, T-Mobile
Speaker: Beata Kovacs, International Head of CRM Solutions, Deutsche Telekom
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11:20am-11:25am
Session Changeover
11:25am-12:15pm
Thought Leader
Nine Laws of Data Mining
Data mining is the predictive core of predictive analytics, a business process that finds useful patterns in data through the use of business knowledge. The industry standard CRISP-DM methodology describes the process, but does not explain why the process takes the form that it does. I present nine “laws of data mining”, useful maxims for data miners, with explanations that reveal the reasons behind the surface properties of the data mining process. The nine laws have implications for predictive analytics applications: how and why it works so well, which ambitions could succeed, and which must fail.
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12:15pm-1:30pm
Lunch / Exhibits
Room: Albert Suites
1:30pm-2:25pm
Expert Panel: Kaboom! Predictive Analytics Hits the Mainstream
Predictive analytics has taken off, across industry sectors and across applications in marketing, fraud detection, credit scoring and beyond. Where exactly are we in the process of crossing the chasm toward pervasive deployment, and how can we ensure progress keeps up the pace and stays on target?
This expert panel will address:
- How much of predictive analytics’ potential has been fully realized?
- Where are the outstanding opportunities with greatest potential?
- What are the greatest challenges faced by the industry in achieving wide scale adoption?
- How are these challenges best overcome?
Panelist: John Elder, Ph.D., Elder Research, Inc.
Panelist: Colin Shearer, WW Industry Solutions Leader, IBM UK Ltd
Panelist: Udo Sglavo, Global Analytic Solutions Manager, SAS
Panel moderator: Eric Siegel, Ph.D., Program Chair, Predictive Analytics World
2:25pm-2:30pm
Session Changeover
2:30pm-3:20pm
Crowdsourcing Data Mining
Case Study: University of Melbourne, Chessmetrics
Prediction Competitions: Far More Than Just a Bit of Fun
Data modelling competitions allow companies and researchers to post a problem and have it scrutinised by the world’s best data scientists. There are an infinite number of techniques that can be applied to any modelling task but it is impossible to know at the outset which will be most effective. By exposing the problem to a wide audience, competitions are a cost effective way to reach the frontier of what is possible from a given dataset. The power of competitions is neatly illustrated by the results of a recent bioinformatics competition hosted by Kaggle. It required participants to pick markers in HIV’s genetic sequence that coincide with changes in the severity of infection. Within a week and a half, the best entry had already outdone the best methods in the scientific literature. This presentation will cover how competitions typically work, some case studies and the types of business modelling challenges that the Kaggle platform can address.
Speaker: Anthony Goldbloom, Kaggle Pty Ltd
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Breaks /Exhibits
Room: Albert Suites
3:50pm-4:40pm
Human Resources; e-Commerce
Case Study: Naukri.com, Jeevansathi.com
Increasing Marketing ROI and Efficiency of Candidate-Search with Predictive Analytics
InfoEdge, India’s largest and most profitable online firm with a bouquet of internet properties has been Google’s biggest customer in India. Our team used predictive modeling to double our profits across multiple fronts. For Naukri.com, India’s number 1 job portal, predictive models target jobseekers most relevant to the recruiter. Analytical insights provided a deeper understanding of recruiter behaviour and informed a redesign of this product’s recruiter search functionality. This session will describe how we did it, and also reveal how Jeevansathi.com, India’s 2nd-largest matrimony portal, targets the acquisition of consumers in the market for marriage.
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Speaker: Eric Siegel, Ph.D., Program Chair, Predictive Analytics World
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Wednesday November 17, 2010
Full-day Workshop
The Best and the Worst of Predictive Analytics:
Predictive Modeling Methods and Common Data Mining Mistakes
Click here for the detailed workshop description
- Workshop starts at 9:00am
- First AM Break from 10:00 – 10:15
- Second AM Break from 11:15 – 11:30
- Lunch from 12:30 – 1:15pm
- First PM Break: 2:00 – 2:15
- Second PM Break: 3:15 – 3:30
- Workshop ends at 4:30pm
Speaker: John Elder, Ph.D., CEO and Founder, Elder Research, Inc.
