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Selling a Data Mining Project to Management Summary
 To help you build the business case for a new data mining project, Casey Klimasauskas offers a framework for quantifying the project's risks and benefits, and building broad-based support for the project at the same time.

Topics: Analytics, Data Mining and Statistical Analysis
by Casey Klimasauskas | 0 comments		 12-20-2011
E-CRM Analytics: Leveraging Data Integration for Prospective Customer Insight and Breakthrough ROI: Part 2 Summary
 In the second part of a two-part series, the authors detail their research methodology and discuss the results of a survey they conducted to better understand the role of data integration in achieving the goals of electronic customer relationship management.

Topics: Analytics, Business Intelligence, CRM, Data Integration
by Christopher Barko, Ashfaaq Moosa, Hamid Nemati | 0 comments		 02-21-2011
E-CRM Analytics: Leveraging Data Integration for Prospective Customer Insight and Breakthrough ROI—Part 1 Summary
 In the first part of a two-part series, the authors provide the foundation of their research and propose their hypotheses for a survey they conducted to better understand the role of data integration in achieving the goals of electronic customer relationship management (e-CRM).

Topics: Analytics, CRM
by Christopher Barko, Ashfaaq Moosa, Hamid Nemati | 0 comments		 01-26-2011
Predictive Analytics: Benefits and Challenges of Using Graph Theoretic Methods Summary
 Graph theoretic methods are already used successfully in Google’s page rank, social network analysis, influencer marketing and driving directions. Including contextual network information into predictive analytics can improve accuracy.

Topics: Analytics, Predictive Analytics
by Nick Lim | 2 comments		 09-02-2010
101 Ways to Sabotage Your Predictive Analytics Project Summary
 It’s not the paralysis of overestimating the tactical implementation, but rather the underestimating of the strategic approach that kills most data mining implementations before they begin. This article reveals the barriers standing between your organization and the effective insights that data mining and predictive analytics can provide.

Topics: Business Intelligence, Data Mining and Statistical Analysis, Predictive Analytics, Analytics
by Eric King, Thomas Rathburn | 0 comments		 05-17-2010
Predictive Modeling at the Transaction Level Summary
 Terry Hipolito describes the implementation of a simple policy component to efficiently apply predictive and risk models into the work flow.

Topics: Data Warehousing, Data Modeling and Design, Predictive Analytics
by Terry Hipolito, Ph.D. | 0 comments		 11-03-2009
Gambling versus Probability: Predictive Analytics Requires Advanced Skills Summary
 Data mining and customer relationship management endeavors, says Tony Rathburn, are dynamic games that continue over time and must constantly evolve to maintain value in helping us evaluate the complex realities in which we operate.

Topics: Analytics, CRM, Data Mining and Statistical Analysis, Data Modeling and Design
by Thomas Rathburn | 1 comment		 09-22-2009
How to Prepare for Data Mining Summary
 Ben Hitt and Eric King state that data mining projects do not fail because of poor or inaccurate predictive models. They explain that despite the availability of highly effective tools for data mining, many data mining projects fail because of a lack of competent assessment, environmental preparation and resulting strategy.

Topics: Analytics, Data Mining and Statistical Analysis, Data Modeling and Design, Predictive Analytics
by Ben Hitt, Ph.D., Eric King | 0 comments		 07-23-2009
Evaluating the Impact of Promotions without Randomly Assigned Control Groups Summary
 Patrick Rooney describes a method to evaluate the impact of promotions without randomly assigning customers to control groups. He also discusses the limitations of the approach and comparisons with other methods, ending with future directions for research.

Topics: Analytics, Sales & Marketing Analytics
by Patrick Rooney, Ph.D. | 0 comments		 06-11-2009
RFM: A Precursor to Data Mining Summary
 Jim Stafford explains that RFM, business intelligence and data mining represent a common progression away from mass marketing as marketing efforts become more analytically based and targeted.

Topics: Analytics, Data Mining and Statistical Analysis, Data Modeling and Design, Sales & Marketing Analytics
by Jim Stafford | 0 comments		 05-14-2009
Adding the Environmental Context to Customer Analytics Summary
 The industry examples Kenneth Levin provides in this article illustrate the importance of putting customer-level analysis in the context of the broader environment.

Topics: Analytics
by Kenneth Levin | 0 comments		 04-07-2009
Data Cleansing Summary
 The hard reality is that always problematic and often costly data anomalies do exist. Will Dwinnell explains why it is helpful to have a tool to automatically ferret out a substantial fraction of those anomalies.

Topics: Analytics
by Will Dwinnell | 0 comments		 02-17-2009
Predictive Analytics Delivers Value Across Business Applications Summary
 This article summarizes the wide range of business applications of predictive analytics, each of which predicts a different type of customer behavior in order to automate operational decisions. A named case study is linked for each of eight pervasive commercial applications of predictive analytics.

Topics: Analytics, Predictive Analytics
by Eric Siegel, Ph.D. | 1 comment		 01-06-2009
Web Mining: Creating, Enhancing, Mining and Acting on Web Data Summary
 Customer interactions reveal important trends and patterns that can help a company design a website that effectively communicates and markets its products and services.

Topics: Analytics, Predictive Analytics
by Jesus Mena | 0 comments		 12-18-2008
Healthful Applications of Predictive Modeling Summary
 With annual fraud-related losses estimated at $90 to $180 billion annually, the healthcare sector requires finite models that detect fraud. An intelligent combination of various technologies can be an efficient method for identifying fraud.

Topics: Analytics, Data Modeling and Design, Predictive Analytics
by Alex Filimon | 0 comments		 10-30-2008

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