CHICAGO, IL– – Mattersight Corporation today announced the signing of a new customer for its Predictive Customer Satisfaction Analytics. The customer is one of the top 5 Property & Casualty Insurance …
View full post on predictive analytics – Yahoo! News Search Results
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Adobe Systems Incorporated (Nasdaq:ADBE) today announced that Adobe® SearchCenter+, a comprehensive search marketing management system within the Adobe Online Marketing Suite, has added keyword performance predictive analytics through a partnering agreement with OptiMine Software, Inc. This agreement will enable search marketers to achieve significant increases in paid search performance (i.e. higher return on ad spend, increased leads, higher conversion rates, increased revenue, greater profit per keyword, etc.). Tests done with search marketers using OptiMine’s keyword modeling have shown improved performance of 25% or more.
The partnership with OptiMine provides keyword-level statistical modeling, which analyzes the unique characteristics of individual keywords. This approach delivers a higher return over typical cluster-based modeling, where bids are defined in groups of similar keywords.
“Digital marketers are examining their paid search spend in the context of many other digital channels, like social, display and email,” said John Mellor, vice president, business development, Omniture Business Unit, Adobe. “Having the ability to predict the return on paid search becomes critically important when deciding on marketing mix and budget allocation. SearchCenter+ helps our customers spend their paid search budget on keywords that will impact the metrics most important to their business.”
The addition of OptiMine predictive keyword analytics to SearchCenter+ is particularly powerful for Adobe customers because SearchCenter+ captures the actual performance of each keyword in every one of the customer’s paid search campaigns. The OptiMine algorithms will now leverage SearchCenter+ keyword performance data, giving these algorithms a highly valuable data source to more accurately predict keyword performance and automatically generate optimal bids accordingly. Adobe customers can begin adding this new feature to their SearchCenter+ contracts immediately.
“We are delighted to partner with Adobe SearchCenter+ and believe the relationship validates the significant customer returns generated through our unique ability to automatically model every keyword,” said Jim Moar, chief executive officer, OptiMine. “The integration of our automated bid optimization coupled with SearchCenter+’s robust data will enable Adobe’s Online Marketing Suite customers to be even more successful.”
About the Adobe Online Marketing Suite
The Adobe Online Marketing Suite, powered by Omniture®, offers an integrated and open platform for online business optimization, a strategy for using customer insight to drive innovation throughout the business and enhance marketing efficiency. The Suite consists of integrated applications to collect and unleash the power of customer insight to optimize customer acquisition, conversion and retention efforts as well as the creation and distribution of content. For example, using the Suite, marketers can identify the most effective marketing strategies and ad placements as well as create relevant, personalized and consistent customer experiences across digital marketing channels, such as onsite, display, e-mail, social, video and mobile. The Suite enables marketers to make quick adjustments, automate certain customer interactions and better maximize marketing ROI, which, ultimately, can positively impact the bottom line.
About Adobe Systems Incorporated
Adobe is changing the world through digital experiences. For more information, visit www.adobe.com.
© 2011 Adobe Systems Incorporated. All rights reserved. Adobe, the Adobe logo, and Omniture [Click and enter any additional Adobe trademarked products named in the release, in order of appearance] are either registered trademarks or trademarks of Adobe Systems Incorporated in the United States and/or other countries. All other trademarks are the property of their respective owners.
Press/ Analyst Contacts
David Olsen
Adobe Systems Incorporated
385-221-1600
davolsen@adobe.com
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WICHE, the Western Interstate Commission for Higher Education, announced today that WCET, the WICHE Cooperative for Educational Technologies, has been awarded a $1,000,000 grant from the Bill & Melinda Gates Foundation to fund an initiative to unify student data from six U.S. institutions and demonstrate the effective use of predictive analytic methods for improving student outcomes. The goal is to identify variables that influence student retention and progression, and guide decision-making that improves postsecondary student completion in the U.S.
This grant for the Predictive Analytics Reporting (PAR) Framework will aggregate data representing more than 400,000 student records from across six WCET member institutions: American Public University System, Colorado Community College System, Rio Salado College, University of Hawaii System, University of Illinois Springfield, and the University of Phoenix. Each participating institution has been exploring or implementing descriptive, inferential or predictive analytics projects on their own student data; the PAR Framework expands on this work through exploration of patterns that can be derived when the six institutional datasets are considered as a single, unified sample.
The principal investigator is Phil Ice, Ed.D., American Public University System (APUS). The project will incorporate many of the best practices and award-winning predictive analytical methods for which Dr. Ice has been recognized by the Sloan Consortium and for which project partner Dr. Vernon Smith, Rio Salado College, has won the WCET Outstanding Work (WOW) award. WCET has appointed Beth Davis, WCET, to serve as the project director.
“We all have a vested interest in improving student outcomes in the United States. WCET is thrilled that the Bill & Melinda Gates Foundation has given us the opportunity to convene this exemplary team of member institutions,” says Dr. Ellen Wagner, Executive Director, WCET. “Our ability to bring public and proprietary institutions together to cooperate on solving big education problems like the ones that the PAR research will address is one of the things that make WCET unique. We are proud to support the leadership efforts of our members, and are looking forward to uncovering patterns that inform decision-making for improving student retention, progression and completion.”
About WCET
The WICHE Cooperative for Educational Technologies (WCET) is a cooperative, membership-driven, non-profit provider of solutions and services that accelerate the adoption of effective practices and policies, advancing excellence in technology-enhanced teaching and learning in higher education. More information about WCET’s institutional membership resources, services and common interest groups can be found on WCET’s website, wcet.wiche.edu.
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Intuition still reigns over fact-based decision making when it comes to customer engagement
When asked to evaluate how well they are using analytics, such as applying data, statistical and quantitative analysis, descriptive and predictive models to drive decisions and enhance customer experience, most organizations’ perceptions are very different from reality, according to new research from Accenture (NYSE: ACN).
Accenture’s Customer Analytics Survey of 800 directors and senior managers at blue chip organizations in Brazil, China, Germany, Italy, Japan, Spain, United Kingdom & Ireland and the US and Canada showed that more than half (55 percent) of respondents felt their methods for segmenting customers and providing relevant experiences are either “ideal” or “very good.” However, related consumer research tells a different story. Accenture’s Global Consumer Survey[i] found that only 21 percent of consumers believe the companies they do business with are good at providing a tailored, relevant experience. Further, it showed that two out of three consumers changed service providers in the past year. With an average cross-industry churn (or defection) rate of 64 percent, organizations that hope to grow must provide more relevant and satisfactory customer experiences.
Most Organizations Don’t Know What They Don’t Know
The Customer Analytics research revealed that more than half of the organizations surveyed do not take advantage of analytics to help them target, service or interact with customers. This suggests that many organizations may be unaware of what is really important to customers and lack the capability to measure their performance in engaging with them.
When making decisions about what customers want, many organizations are just as likely to rely on personal experience (which 23 percent of respondents described as “very important”) as analysis of data and facts (which 22 percent called “very important.”)
“Ten years ago, organizations could get away with relying on intuition to make decisions about how to engage with customers but today’s world is wired and interconnected in such a way that customer expectations and influence are greater than ever before,” said Julio Hernandez, global lead for customer analytics at Accenture. “Descriptive and predictive analytics enable organizations to draw fact based conclusions about what customers are actually doing and what they are most likely to do and need. Whether in product features, delivery or price, organizations are leaving money on the table by not applying the power of analytics to these decisions,” added Hernandez.
Even Analytically-Minded Organizations Are Missing the Big Opportunities
Even amongst organizations actively using analytics in marketing, sales and service, most are not applying it broadly across the full spectrum of marketing and customer activities. This includes critical areas such as pricing (86 percent of respondents not using), product/service delivery (77 percent not using) and product development (59 percent not using). Given the impact that new product development and delivery have on an organization’s future growth potential, applying analytics to make better informed decisions in these areas is an opportunity for companies to gain a significant competitive advantage and maximize their market potential.
What is Holding Organizations Back?
The study revealed that less analytically mature firms are much more likely to perceive their data sources as accurate and reliable than organizations with more developed analytic capabilities. This may be because larger firms are conducting more regular and complex data manipulation and so are more aware of the shortcomings of their data and analytical capabilities.
Corporate culture also poses challenges. While almost 70 percent of respondents said their senior management was totally or highly committed to analytics and fact based decision making, corporate culture still presents a major barrier. Less mature firms are more likely to cite overall corporate culture as a major barrier (42 percent of respondents versus 33 percent of more analytically mature firms). The more analytically mature firms cite budget limitations (47 percent versus 29 percent), departmental culture (40 percent versus 29 percent of less analytically mature firms) and senior management support (45 percent versus 31 percent) as obstacles to better segmentation.
Failure to Recognize the Customer’s Perspective
The findings suggest that most organizations analyze and segment customers according to their value to the organization rather than according to their customers’ unique needs and preferences. For example, across organizations at each end of the analytics maturity spectrum, the most commonly used metrics to segment customers tend to be company focused, such as: profit per customer (cited by 41 per cent), lifetime value (27 per cent) and share of wallet (how much of a customer’s total spending occurs with the company) (24 per cent). Whereas indicators of customer requirements such as customer needs and behaviours, service levels received and psychographics are often used least.
“Regardless of how analytically mature they are, most firms largely focus on using analytics to better understand the customer’s value to the organization and much less on the organization’s value to the customer,” said Hernandez. “This leads to a customer experience that is over-indexed on meeting the needs of the organization versus the customer. Organizations will benefit from a more balanced approach to using analytics, one that takes into account what’s sustainable for the business as well as what’s relevant to the customer.”
About the Research
This research is based on 800 telephone interviews with customer facing directors or senior managers in major blue chip organizations with responsibility for, or knowledge of, the use and application of customer analytics in the company. The research was conducted in March 2011 across eight countries (Brazil, China, Germany, Italy, Japan, Spain, United Kingdom & Ireland and the US & Canada) with 100 interviews in each country.
For a copy of the findings please click here.
About Accenture
Accenture is a global management consulting, technology services and outsourcing company, with more than 215,000 people serving clients in more than 120 countries. Combining unparalleled experience, comprehensive capabilities across all industries and business functions, and extensive research on the world’s most successful companies, Accenture collaborates with clients to help them become high-performance businesses and governments. The company generated net revenues of US$21.6 billion for the fiscal year ended Aug. 31, 2010. Its home page is www.accenture.com.
[i] Accenture 2010 Global Consumer Survey , published February 8, 2011
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Accenture is to create 100 new IDA Ireland-supported jobs including Predictive Analytics jobs at a new centre in Dublin over the coming four years
Accenture already employs 1,300 people in Dublin in a range of areas including management consultancy, technology services and outsourcing according to press reports
The new jobs will be based at a new research and innovation centre and its role will involve advanced statistical modelling for companies and governments.
The jobs will be aimed at graduates to PhD level candidates who have analytical and statistics experience. Predictive analytics applies advanced statistical modelling techniques to internal and external data sources to generate deeper insights that help businesses and governments drive better outcomes.
“Today’s announcement marks another milestone in the ongoing development of Accenture in Ireland,” said Mark Ryan, country managing director of Accenture in Ireland according to the news report.
First of its kind
“The centre is the first Accenture innovation centre of its kind to be located in Ireland and is designed to help accelerate the development, delivery and commercialisation of innovative predictive analytics solutions employing a mixture of technology experts and management scientists.
“Many of the 100 new roles created will be focused specifically on research and development and we are currently recruiting both graduates and experienced hires with analytical and statistics experience ranging from PhD level to graduate level.”
The Dublin centre will focus on the innovative development of sophisticated techniques in compliance-related analytics to address many of the key business challenges facing Accenture’s clients today.
Over time, the centre will expand its capabilities to demonstrate, develop and deliver wider cross-functional and cross-industry predictive analytics services.
The centre will also be a global showcase for Accenture analytical capabilities to clients who will visit from around the world. The technologies developed locally will be marketed globally to Accenture clients.
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Navigating Predictive Analytics - Interview with Nick Lim, Sonamine Play Now | Play in Popup | DownloadNavigating Predictive Analytics: Benefits and Challenges of Using Graph Theoretic Methods Leveraging Contextual Network Information for Better Accuracy
Nick Lim is the CEO and Founder of Sonamine, LLC. Over the past 15 years, Nick has worked in analytic environments with large amounts of data. Leveraging his training at Harvard, he has led product and strategy teams at MicroStrategy, Enpocket and Nokia to build systems that predicted ad clicks and consumer behavior using terabytes of information.
Predictive Analytics: Benefits and Challenges of Using Graph Theoretic Methods Leveraging Contextual Network Information for Better Accuracy
The practice of predictive analytics has come a long way since the advent of operations research. Current predictive models routinely use more than 100 variables, most of them are characteristics of the object being analyzed, including age and gender for customer analysis, or color and text for advertisements. In this article, we will explore how graph methods can be used in predictive analytics.
Graph methods refer to techniques that analyze a network of objects, rather than pie charts and trend lines. Networks are made up of nodes and connections among the nodes. Other synonyms include vertices, edges, and arcs. Graph methods analyze the link structure, rate of diffusion and the clustering of nodes in a network. Many concepts in the popular press such as Google’s page rank, social network analysis, influencer marketing and driving directions all utilize graph theoretic methods.
Real World Use Cases
Why are these graph methods useful for predictive analytics? One reason is that additional contextual predictors can be derived using graph methods. Let’s start with a well known example: Google uses page rank as one of many predictors for the relevance of a web page. The link structure in the world-wide-web network provides valuable contextual information about which pages are deemed most relevant by the web page creators—this contextual link structure is then used to predict relevance for a user’s query.
Another example: If you are trying to predict the revenue contribution of each employee, in addition to the educational level, job responsibility and experiences, you might consider how well the employee relates to the customers or other managers. The contextual information added here is not something inherent about the employee. IBM conducted such a study with 2600 of their consultants, and created a model with revenue as the outcome target variable and three sets of predictor variables.1 In addition to the standard employee and job-related characteristics, two sets of contextual variables were added. One was related to the structure of the web of relationships, and the other was related to how each employee was related to managers and customers. It turned out that graph-derived contextual predictors were highly correlated with revenue.
Enriching Context Where It Seemingly Did Not Exist
One common question raised is how much of the contextual information is already captured in the demographic data. After all, there is a common understanding that birds of the same feather flock together. To tackle this question, a group of researchers at MIT studied how the Yahoo instant messenger network affected the adoption of the Yahoo Go product.2 They discovered that only 50% of the contextual network effect could be explained by the common underlying demographic variables.
This finding has big implications for predictive analytics in data poor environments. A data poor environment would benefit greatly from using contextual predictors that you already have. For example, if you are trying to predict churn in a prepaid mobile customer base where there is little to no demographic information, using the social network contextual information can greatly improve the prediction accuracy and lower the false positive rate.
Another type of contextual network information that can be included into predictive analytics is the underlying form of the social community that one belongs to. In the study with Yahoo data above that was trying to predict product adoption, they found that the chances of you adopting a product rises if more people in your social community have already adopted the product.
Besides social contextual network data that can be added to predictive functions, you can also deduce contextual information. For example, in trying to predict fraud from a set of credit applications, we can construct a network of information attributes. Using the basic idea of that people with bad credit tended to relate to other similar people, some researchers generated the graph-based hub and authority variables from such a network and added them to a support vector machine prediction function.3 The graph-derived context variables improved the fraud prediction by approximately 20%.
Clearly, the benefits of adding graph-based predictors are better lifts and lower rates of false positives. The improvements range from 20% to 100% depending on the richness of data available today. These benefits translate directly into bottom line results to the extent that your company is already putting predictive analytics into daily operations. So what are the practical aspects of putting this into operation?
How Do You Get Started?
Let’s start with the data: Where do you obtain the data to construct such contextual networks? The IBM study looked at the anonymized email traffic, address books and buddy lists. If you were a retailer like Amazon, you could use wish lists, product gift transactions and share-the-love data to construct the social context for predictions. Financial services companies can use the trading data to construct the contextual network among the traders or credit application data to construct an information network. Telecommunications companies can leverage the call detail records. In fact, the explosion of data collection means that most organizations already have this contextual network information just waiting to be accessed and utilized.
One unique aspect of contextual network predictors is that they are usually specific to the target outcome you want to predict. This is the definition of contextual after all. So if you want to predict churn, then you need to understand how the contextual network predisposes a subscriber to churn. Such contextual churn insight will certainly be different if you were trying to predict employee revenue or product adoption.
Since the contextual environment is changing, the window that an enterprise has to leverage the additional predictive power of contextual networks is also dynamic. Most customers will switch vendors very soon after they learn about a bad experience from a friend, so vendors must respond in a timely manner. Practically, this means that the calculation of graph-based predictors must occur more frequently.
Some contextual network information is relatively stable over time. For example, if someone has a preferred takeout restaurant, she will likely continue to call that restaurant even after she gets a new phone number. These “creatures of habits” can be more easily identified, say in the case of fraud or terrorism prevention.
Challenges
One potential impediment to leveraging contextual network information involves the privacy and data security issues. Since contextual networks tend to involve personal transactions, companies would like to ensure the highest security to prevent breaches. Various regulatory bodies at different state, country or global levels might have conflicting guidelines on what information is considered private and cannot be used for data analytics.
Another obstacle today is the scalability challenge. Often these contextual networks are many times the size of the current data used for analysis. Contextual networks rely on relationship information, so sampling runs the risk of losing valuable information. James Kobelius of Forrester postulates that the advent of social network analysis will catalyze the need for petabyte-sized data warehouses.4
Another obstacle is the lack of software tools that will generate these graph-based predictors. Types of specialized graph based calculations include community detection, diffusion simulation, vertex similarity, topology and centrality analysis. Open source tools such as Jung and Pajek work well for R&D, while proven commercial tools such as Sonamine are geared toward large scale and production graph mining.
A last obstacle is education. Contextual network predictors are unlike other types of data being captured and analyzed. Enterprises must first capture this new data, and that will require some mind-set shift. Setting up an infrastructure to model the contextual network will require new tools and approaches. Finally, using these contextual networks in predictive analytics that improve the bottom line will require the cooperation of multiple disciplines—analytics, marketing and operations.
Despite obstacles, the time to investigate and leverage these graph-based methods is now. IDAnalytics, a company that provides identity theft protection, has been issued a patent for a system and method for identity-based fraud detection through graph anomaly detection. How will you start adding graph-derived predictors into your predictive analytics?
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Is Predictive Analytics on the (Health IT) Horizon? – Health Data Management Reporting
Health Data Management is reporting that BI reports generated within health care usually are retrospective looks at cumulative data. But what if BI could be used to actually predict patient outcomes and risks, and steer clinicians to steps to provide even better care?
That’s the question that Novant Health, a nine-hospital delivery system based in Winston-Salem, N.C., hopes to answer. For the last decade, Novant has been using data mining and analysis tools from MEDai. The health system has compiled benchmark data for 50 distinct disease states, says Jim Lederer, M.D., vice president of clinical improvement. “Our data is great, and helps us identify opportunities to improve care,” he says. “But a better step would be predicting patient experience before it happened, rather than evaluating it after it happened.”
Toward that goal, Novant is an alpha site development partner with MEDai on a BI platform called “PinPoint Review.” Using the tool, clinicians at Novant will be provided a list of patients ranked on a scale of 1 to 10 for their risk of certain outcomes, including hospital readmission and the development of pressure ulcers during a current stay.
The amount of data needed to create such predictive scorecards is huge, Lederer says. “From tens to hundreds of thousands of HL7 messages every hour,” he estimates. The data, which might include lab results, vital signs, and X-ray reports, goes to MEDai in a real-time data feed, run through an artificial intelligence processor, and then returns to Novant in the form of a predictive scorecard.
The system, Lederer says, would not replace clinician judgment, nor would it necessarily be foolproof. “A prediction is nothing more than a prediction,” he advises. “I have to validate it by looking at the patient. If I have a risk of 2 out of 10 for mortality, it doesn’t mean I won’t die. But if I have someone with a high risk of transfer to the ICU, [I can ask] what can I do to prevent it.”
By the end of the year, Lederer expects that the predictor for patient readmission will be complete. The effort will also include predictors for mortality and ICU transfer risk for patients admitted to the general floor, and LOS and mortality risks for all patients in the ICU, he adds. The reports would be delivered real-time thru a portal. “We can parse out the population down to patients who need more attention,” he says.
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Predictive Analytics is playing a key role in helping conserve wildlife in northern Kenya according to press reports by IBM
Marwell Wildlife is conducting a survey on Grevy’s zebra where local nomadic herdsmen are interviewed about their attitudes towards the zebras, what they think the key threats facing them are and where they think they are within this large area. The herdsmen have very good knowledge about wildlife and interviewing them is a very efficient means of collecting information over this vast and inaccessible area.
Video Interview
Marwell Wildlife selected IBM predictive analytics software to help identify patterns and analyse data which will help inform decisions on conservation measures for Grevy’s zebra.
By improving Marwell’s insight into herders’ attitudes through the surveys, and combining this with information from aerial surveys, camera traps and radio collars on the zebras, Marwell now has a more detailed understanding of the issues surrounding the zebra and are therefore able to understand the main threats facing the species, which allows limited conservation resources to be focused towards these areas.
For example, one of the main reasons for hunting is to produce traditional medicines from the zebra’s body fat. If herding communities were able to access modern medicines then the need for hunting would be much reduced.
“The IBM predictive analytics software is critical in analysing the information we collect from the field. The data from the surveys is vast and complex and requires powerful software to analyse it. The software is ideal for identifying trends and patterns from this data,” said Dr. Guy Parker, Head of Biodiversity Management at Marwell Wildlife. “In the case of the recent interview survey, the software enabled us to determine peoples’ attitudes towards the Grevy’s zebra. Furthermore, we were able to determine what influence factors such as education level, age, location, and wildlife benefits had upon peoples’ attitudes. This is the kind of complex multi-variate analysis that the IBM predictive analytics software is designed to tackle.”
“The work at Marwell Wildlife takes the use of analytics to a whole new level,” said Colin Shearer, predictive analytics strategist at IBM. “It is great to see analytics play such a critical part towards the conservation of Wildlife.”
Dr. Guy Parker will explain the benefits of using predictive analytics to achieve sustainability of wildlife in his presentation ‘The Management of Information’, to take place on Wednesday September 15th from 14.45 – 15.30 during the START Summit. For more information, please visit: http://www.ibm.com/uk/start.
This analytics solution is powered by IBM SPSS predictive analytics software.
For more information about Marwell Wildlife please contact Helen Jeffreys, PR Officer, Marwell Wildlife helenj@marwell.org.uk.
Marwell and partners surveyed the far north of Kenya at the beginning of 2010. Because the area was so difficult to survey, Marwell decided to interview local people. Pastoral communities live across the whole of northern Kenya and move their livestock with the rains. They know a great deal about the landscape and its wildlife. Therefore, talking to them is the most efficient means of collecting information.
The far north of Kenya provides a link between northern Kenya and southern Ethiopia. This area is also vast and may contain large numbers of wildlife. In addition, there are currently only 2 conservation areas in the whole of the far north, so there is little protection for the wildlife there.
Marwell has been working with Kenyan conservation partners for 15 years to conserve the Grevy’s zebra. This has involved working with the Kenyan government to get the Grevy’s zebra protected by law, conducting surveys to work out where the Grevy’s zebra occur and in what numbers, and working with local communities in northern Kenya to protect the Grevy’s zebra and other wildlife.
Marwell Wildlife works with a number of conservation organisations including: Northern Rangelands Trust, Lewa Wildlife Conservancy, Grevy’s zebra Trust, Kenya Wildlife Service, Denver Zoo and St Louis Zoo, as well as a number of community conservancies across northern Kenya.
There are believed to be approximately 2,500 Grevy’s zebra left in the wild. However, there is some uncertainty about the numbers because they range over a vast area and are notoriously difficult to count.
While Grevy’s zebra used to live across the Horn of Africa, today they are only found in northern Kenya – their stronghold – and small areas of southern Ethiopia. The core of their range is in central northern Kenya where their numbers have stabilised and they appear to be recovering.
More information on IBM Analytics
Today, IBM is working with more than 250,000 clients worldwide on analytics projects, including 22 of the top 24 global commercial banks, 18 of the world’s top 22 telecommunication carriers and 11 of the top 12 U.S. specialty retailers
In just four years, IBM has invested more than $11 billion, dedicated 6,000 business consultants and opened seven Analytics Centers of Excellence around the world to help clients uncover hidden insights within their data.
To learn more about IBM business analytics please visit: www.ibm.com/gbs/bao.
More information on START
START is a national initiative by The Prince’s Charities to promote and celebrate sustainable living. It aims to demonstrate what a more energy efficient, cleaner and healthier future could look like. During 2010, START will grow into a vibrant and diverse programme, which will engage people right across the UK. The START founding partners include: IBM, Addison Lee, Asda, B&Q, BT Group plc, EDF Energy, M&S, Virgin Money, Waitrose and Your Water companies. For more information go to: www.startuk.org.
The IBM Summit is the business-to-business component of START and each day will engage 120 global leaders in business, the public sector and academia to discuss the varying economic, societal and environmental aspects of sustainability. Each of the Summit’s nine days has a unique theme including cities, energy, transportation, skills & people, youth, supply chain, finance and analytics. The final day of the event will combine the work of all days into a discussion on Smarter Business.
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How companies use real-time Predictive Analytics data to plan for the future.
Fortune magazine reports that in a tough global economy, sloppy decision making and “going with your gut” can get you punished–swiftly. That’s why leading companies are increasingly turning to a new management discipline called predictive analytics to compete and thrive. Rather than relying on intuition when pricing products, maintaining inventory or hiring talent, managers are using data, analysis and systematic reasoning to improve efficiency, reduce risk and increase profits.
In simple terms analytics means using quantitative methods to derive insights from data, and then drawing on those insights to shape business decisions and, ultimately, improve business performance. Thus predictive analytics is emerging as a game-changer. Instead of looking backward to analyze “what happened?” predictive analytics help executives answer “What’s next?” and “What should we do about it?”
Accenture research shows that high-performance businesses have a much more developed analytical orientation than other organizations. They are five times more likely than their low-performing competitors to view analytical capabilities as core to the business. Our research shows that there are big rewards for organizations that embrace analytics decision making.
Some of the most famous examples of analytics in action come from the world of professional sports, where “quants” increasingly make the decisions about what players are really worth. Consider these examples from the business world:
Best Buy was able to determine through analysis of member data that 7% of its customers were responsible for 43% of its sales. The company then segmented its customers into several archetypes and redesigned stores and the in-store experience to reflect the buying habits of particular customer groups.
Olive Garden uses data to forecast staffing needs and food preparation requirements down to individual menu items and ingredients. The restaurant chain has been able to manage its staff much more efficiently and has cut food waste significantly.
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Predictive Analytics Training - Inteview with John Elder, Elder Research Play Now | Play in Popup | DownloadElder Research will be presenting a 2-day course, “Tools for Discovering Patterns in Data,” on September 13-14 in Charlottesville, Virginia. Drawing on 20 years of experience, Dr. John Elder will explain the inner workings of techniques employed by experts to solve challenging problems.
Attendees will receive a copy of the new book, co-authored by the instructor, Handbook of Statistical Analysis and Data Mining Applications. This ~900-page hardback is designed to help practitioners achieve success with data mining and includes valuable fully-working 90-day versions of powerful data mining software from three leading vendors.
The course is practical, and experience-based, and demystifies data mining with clarity and humor. Learn to isolate the essential aspects of a problem and select and combine appropriate software tools to find useful patterns in noisy, incomplete data. Practical examples are drawn from successful consulting engagements in a wide range of fields — including investment modeling, fraud detection, biometrics, credit scoring, CRM, national security, drug efficacy, process optimization, and image recognition. The course contains the popular segment, “Top 10 Data Mining Mistakes, and How to Avoid Them,” vendor-neutral discussions of the strengths of leading software tools, and clear explanations of the essential technologies of text mining, resampling statistics, ensembles of models, and scientific visualization.
Space is limited, so reserve your place now! To register, call 434-973-7673 begin_of_the_skype_highlighting 434-973-7673 end_of_the_skype_highlighting
While attending the course, enjoy your stay in historic Charlottesville, Virginia.
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