Oops! The input is malformed! Data Mining & Predictive Analytics Articles - Eric King - BeyeNETWORK
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Articles

Displaying 1–15 (of 22)

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Title Popularity * Date
Analytics for Competitive Advantage Summary
Tony Rathburn delves into the benefits and challenges of analytics projects in distributed environments. He explains how these projects provide incremental enhancement to organizational decision-making processes with little risk and high return on investment.

Topics: Analytics

by Thomas Rathburn | 0 comments
12-18-2012
Five Things Leaders and Practitioners Are Not Doing in Predictive Analytics Summary
When projects fail to deliver the expected results, the culprit is often inadequate preparation. Eric King explains that for successful predictive analytics, program managers and data analysts need to first understand what it is they are getting into. (This article was included in a recent issue of our BI Trends + Strategies e-zine.)

Topics: Analytics, Predictive Analytics

by Eric King | 0 comments
10-15-2012
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

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