I was wondering why the planet spends so much money in the $150-billion business process outsourcing industry, especially in voice calls to call centers.
If your iPhone Siri phone can be configured to answer any query, Why can’t it be configured to be a virtual assistant, customer support, marketing outbound or even a super charged call center interactive voice response .
Can we do and run some tests on this?
Continuing the DecisionStats series on trends for 2012, Timo Elliott , Technology Evangelist at SAP Business Objects, looks at the predictions he made in the beginning of 2011 and follows up with the things that surprised him in 2011, and what he foresees in 2012.
You can read last year’s predictions by Mr Elliott at http://www.decisionstats.com/brief-interview-timo-elliott/
Timo- Here are my comments on the “top three analytics trends” predictions I made last year:
(1) Analytics, reinvented. New DW techniques make it possible to do sub-second, interactive analytics directly against row-level operational data. Now BI processes and interfaces need to be rethought and redesigned to make best use of this — notably by blurring the distinctions between the “design” and “consumption” phases of BI.
I spent most of 2011 talking about this theme at various conferences: how existing BI technology israpidly becoming obsolete and how the changes are akin to the move from film to digital photography. Technology that has been around for many years (in-memory, column stores, datawarehouse appliances, etc.) came together to create exciting new opportunities and even generally-skeptical industry analysts put out press releases such as “Gartner Says Data Warehousing Reaching Its Most Significant Inflection Point Since Its Inception.” Some of the smaller BI vendors had been pushing in-memory analytics for years, but the general market started paying more attention when megavendors like SAP started painting a long-term vision of in-memory becoming a core platform for applications, not just analytics. Database leader Oracle was forced to upgrade their in-memory messaging from “It’s a complete fantasy” to “we have that too”.
(2) Corporate and personal BI come together. The ability to mix corporate and personal data for quick, pragmatic analysis is a common business need. The typical solution to the problem — extracting and combining the data into a local data store (either Excel or a departmental data mart) — pleases users, but introduces duplication and extra costs and makes a mockery of information governance. 2011 will see the rise of systems that let individuals and departments load their data into personal spaces in the corporate environment, allowing pragmatic analytic flexibility without compromising security and governance.
The number of departmental “data discovery” initiatives continued to rise through 2011, but new tools do make it easier for business people to upload and manipulate their own information while using the corporate standards. 2012 will see more development of “enterprise data discovery” interfaces for casual users.
(3) The next generation of business applications. Where are the business applications designed to support what people really do all day, such as implementing this year’s strategy, launching new products, or acquiring another company? 2011 will see the first prototypes of people-focused, flexible, information-centric, and collaborative applications, bringing together the best of business intelligence, “enterprise 2.0”, and existing operational applications.
2011 saw the rise of sophisticated, user-centric mobile applications that combine data from corporate systems with GPS mapping and the ability to “take action”, such as mobile medical analytics for doctors or mobile beauty advisor applications, and collaborative BI started becoming a standard part of enterprise platforms.
And one that should happen, but probably won’t: (4) Intelligence = Information + PEOPLE. Successful analytics isn’t about technology — it’s about people, process, and culture. The biggest trend in 2011 should be organizations spending the majority of their efforts on user adoption rather than technical implementation.
Unsurprisingly, there was still high demand for presentations on why BI projects fail and how to implement BI competency centers. The new architectures probably resulted in even more emphasis on technology than ever, while business peoples’ expectations skyrocketed, fueled by advances in the consumer world. The result was probably even more dissatisfaction in the past, but the benefits of the new architectures should start becoming clearer during 2012.
What surprised me the most:
The rapid rise of Hadoop / NoSQL. The potentials of the technology have always been impressive, but I was surprised just how quickly these technology has been used to address real-life business problems (beyond the “big web” vendors where it originated), and how quickly it is becoming part of mainstream enterprise analytic architectures (e.g. Sybase IQ 15.4 includes native MapReduce APIs, Hadoop integration and federation, etc.)
Prediction for 2012:
As I sat down to gather my thoughts about BI in 2012, I quickly came up with the same long laundry list of BI topics as everybody else: in-memory, mobile, predictive, social, collaborative decision-making, data discovery, real-time, etc. etc. All of these things are clearly important, and where going to continue to see great improvements this year. But I think that the real “next big thing” in BI is what I’m seeing when I talk to customers: they’re using these new opportunities not only to “improve analytics” but also fundamentally rethink some of their key business processes.
Instead of analytics being something that is used to monitor and eventually improve a business process, analytics is becoming a more fundamental part of the business process itself. One example is a large telco company that has transformed the way they attract customers. Instead of laboriously creating a range of rate plans, promoting them, and analyzing the results, they now use analytics to automatically create hundreds of more complex, personalized rate plans. They then throw them out into the market, monitor in real time, and quickly cull any that aren’t successful. It’s a way of doing business that would have been inconceivable in the past, and a lot more common in the future.
Timo Elliott is a 20-year veteran of SAP BusinessObjects, and has spent the last quarter-century working with customers around the world on information strategy.
He works closely with SAP research and innovation centers around the world to evangelize new technology prototypes.
His popular Business Analytics blog tracks innovation in analytics and social media, including topics such as augmented corporate reality, collaborative decision-making, and social network analysis.
His PowerPoint Twitter Tools lets presenters see and react to tweets in real time, embedded directly within their slides.
A popular and engaging speaker, Elliott presents regularly to IT and business audiences at international conferences, on subjects such as why BI projects fail and what to do about it, and the intersection of BI and enterprise 2.0.
Prior to Business Objects, Elliott was a computer consultant in Hong Kong and led analytics projects for Shell in New Zealand. He holds a first-class honors degree in Economics with Statistics from Bristol University, England
Timo can be contacted via Twitter at https://twitter.com/timoelliott
Part 1 of this series was from James Kobielus, Forrestor at http://www.decisionstats.com/jim-kobielus-on-2012/
Jim Kobielus revisits the predictions he made in 2011 (and a summary of 2010) , and makes some fresh ones for 2012. For technology watchers, this is an article by one of the gurus of enterprise software.
All of those trends predictions (at http://www.decisionstats.com/brief-interview-with-james-g-kobielus/ ) came true in 2011, and are in full force in 2012 as well.Here are my predictions for 2012, and the links to the 3 blogposts in which I made them last month:
The Year Ahead in Next Best Action? Here’s the Next Best Thing to a Crystal Ball!
The Year Ahead in Advanced Analytics? Advances on All Fronts!
The Year Ahead In Big Data? Big, Cool, New Stuff Looms Large!
And in an exclusive and generous favor for DecisionStats, Jim does some crystal gazing for the cloud computing field in 2012-
Cloud/SaaS EDWs will cross the enterprise-adoption inflection point. In 2012, cloud and software-as-a-service (SaaS) enterprise data warehouses (EDWs), offered on a public subscription basis, will gain greater enterprise adoption as a complement or outright replacement for appliance- and software-based EDWs. A growing number of established and startup EDW vendors will roll out cloud/SaaS “Big Data” offerings. Many of these will supplement and extend RDBMS and columnar technologies with Hadoop, key-value, graph, document, and other new database architectures.
About-
http://www.forrester.com/rb/analyst/james_kobielus
 |
James G. Kobielus Senior Analyst |
James serves Business Process & Application Development & Delivery Professionals. He is a leading expert on data warehousing, predictive analytics, data mining, and complex event processing. In addition to his core coverage areas, James contributes to Forrester’s research in business intelligence, data integration, data quality, and master data management.
James has a long history in IT research and consulting and has worked for both vendors and research firms. Most recently, he was at Current Analysis, an IT research firm, where he was a principal analyst covering topics ranging from data warehousing to data integration and the Semantic Web. Prior to that position, James was a senior technical systems analyst at Exostar (a hosted supply chain management and eBusiness hub for the aerospace and defense industry). In this capacity, James was responsible for identifying and specifying product/service requirements for federated identity, PKI, and other products. He also worked as an analyst for the Burton Group and was previously employed by LCC International, DynCorp, ADEENA, International Center for Information Technologies, and the North American Telecommunications Association. He is both well versed and experienced in product and market assessments. James is a widely published business/technology author and has spoken at many industry events.
Contact –
Twitter: http://twitter.com/jameskobielus
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.
What are the top three trends you see in 2011?
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.
Here is a brief one question interview with James Kobielus, Senior Analyst, Forrester.
Ajay-Describe the five most important events in Predictive Analytics you saw in 2010 and the top three trends in 2011 as per you.
Jim-
Five most important developments in 2010:
Three top trends I see in the coming year, above and beyond deepening and adoption of the above-bulleted developments:
About –http://www.forrester.com/rb/analyst/james_kobielus
James G. Kobielus
Senior Analyst, Forrester Research
James serves Business Process & Applications professionals. He is a leading expert on data warehousing, predictive analytics, data mining, and complex event processing. In addition to his core coverage areas, James contributes to Forrester’s research in business intelligence, data integration, data quality, and master data management.
James has a long history in IT research and consulting and has worked for both vendors and research firms. Most recently, he was at Current Analysis, an IT research firm, where he was a principal analyst covering topics ranging from data warehousing to data integration and the Semantic Web. Prior to that position, James was a senior technical systems analyst at Exostar (a hosted supply chain management and eBusiness hub for the aerospace and defense industry). In this capacity, James was responsible for identifying and specifying product/service requirements for federated identity, PKI, and other products. He also worked as an analyst for the Burton Group and was previously employed by LCC International, DynCorp, ADEENA, International Center for Information Technologies, and the North American Telecommunications Association. He is both well versed and experienced in product and market assessments. James is a widely published business/technology author and has spoken at many industry events
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
Registration, Coffee and Danish
Room: Albert Suites
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:
enterprise decision support
Speaker: Eric Siegel, Ph.D., Program Chair, Predictive Analytics World
[ Top of this page ] [ Agenda overview ]
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 ]
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 ]
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 ]
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:
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
[ Top of this page ] [ Agenda overview ]
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)
[ Top of this page ] [ Agenda overview ]
Session Break
Room: Albert Suites
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:
Speaker: Anne Stephens, SABMiller plc
Speaker: Titir Pal, AbsolutData
[ Top of this page ] [ Agenda overview ]
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
[ Top of this page ] [ Agenda overview ]
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
[ Top of this page ] [ Agenda overview ]
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
[ Top of this page ] [ Agenda overview ]
Reception / Exhibits
Room: Albert Suites
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:
Moderator: Eric Siegel, Program Chair, Predictive Analytics World
Speaker: John Elder, Ph.D., Elder Research, Inc.
Also see Dr. Elder’s full-day workshop
[ Top of this page ] [ Agenda overview ]
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
[ Top of this page ] [ Agenda overview ]
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.
[ Top of this page ] [ Agenda overview ]
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:
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
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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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
Speaker: John Elder, Ph.D., CEO and Founder, Elder Research, Inc.
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