Pig is an open source platform for analyzing large data sets that works in conjunction with Hadoop clusters and Map-Reduce jobs. They recently announced their 0.20 release featuring a 5X performance gain over the previous version. Check out the details.
Mar 25
Rexer Analytics is just about to close their data mining survey and could use your input. The link from the Oracle data mining blog will give you the code you need to participate. After the survey, you can access previous versions of the compiled survey results.
Mar 16
Various groups within a company have adopted new technologies at different rates with Human Resources not know for leading the way. A Business Week article outlines how HR is now starting to leverage data mining to improve it’s
Analyzing employees’ noverbal communication to identify who are the ones good at sharing information and who are the ones that are on their own. Data mining can also be applied to help improve employee retention. By looking at the characteristics of employees who have quit, HR can then look for current employees with similar patterns.
The ultimate goal of all this data mining may be to eventually estimate the contribution that a new hire can make to the company.
Mar 14
The Data Mining Blog put together a great diagram showing how the tons of publicly available data feeds could possibly linked together for a new type of analysis. Also check out linkeddata.org.
Mar 05
Jan 07
The New York Times has picked up on the increasing popularity of the open source analytics and data mining software, R.
Nov 21
The NY Times highlights the problem that recommendation systems like Netflix sometimes have. For movies like Napoleon Dynamite, people either love them or hate them and it is very tricky to predict which it will be.
Oct 07
According to a recent report from the National Research Council, data mining is not a viable method of automatically identifying terrorist suspects.
Sep 17
DUBLIN, Ireland –(Business Wire)– Research and Markets (http://www.researchandmarkets.com/reports/c66692) has announced the addition of “The Spss Business Intelligence Radars (Vendor Focus)” to their offering.
This brief analyses the strengths and weaknesses of SPSSs Business Intelligence offering. Information Builders is rated according to its market impact (based on revenues), user sentiment (based on customer perceptions) and technology.
Scope of this title:
Technology: an assessment of SPSSs technology based on specific attributes and the availability of certain features. User sentiment: tracks end-users impression of SPSSs products based on a survey of over 700 Business Intelligence users. Market impact: measures SPSSs market impact based on its revenues and financial performance.
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Highlights of this title:
Many enterprises have invested heavily in enterprise applications that by now form an integral part of their core business processes. Subsequently enterprises are increasingly deploying Business Intelligence solutions in order to unlock the potential of the increasing volume of the mission-critical data captured within an enterprise. Since Business Intelligence could be of benefit to a wide range of organizations, the demand for these solutions will expand. Particularly as organizations are seeking to consolidate their existing BI capabilities further.
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Reasons to order your copy:
Gain detailed knowledge of SPSSs strengths with regards to technology, user sentiment and market impact. Business Intelligence vendors can benchmark their own performance against SPSS in various key criteria. Enterprise IT managers will gain valuable insight to improve their Business Intelligence purchasing decisions.
Topics Covered:
Our VIEW
CATALYST
SUMMARY
METHODOLOGY
MARKET DEVELOPMENTS
The BI market will continue to evolve and grow
ANALYSIS
SPSS: Business Intelligence Radars
Recommendation: Explore
APPENDIX
Definitions
Our Ratings
Extended methodology
Technology
User Sentiment
Market Impact
Further reading
Ask the analyst
For more information, visit http://www.researchandmarkets.com/reports/c66692.
Source: Datamonitor
Apr 17
Predictive analytics allow publishers to analyze and build a profile for each player—allowing for highly targeted marketing
by Jeff Kaplan
The video gaming industry has become a significant contributor to the U.S. GDP with software and hardware sales up 19 percent to $12.5 billion in 2006 according to market research firm NPD Group.
With forecasts for worldwide total game hardware and software sales anticipated to achieve $44 billion in sales by 2011 one can appreciate the real money being invested.
As any executive can attest with this type of growth comes increased expectations and demands. As a result, the intense competition between top publishers for each gaming dollar has skyrocketed, and with it so have the costs to produce and “one up” new titles from competitors.
The good news is there are ways for companies to increase revenue by leveraging an asset they already have invested in their data.
Veteran Marketers
The smart companies are recognizing the importance of recruiting veteran marketers who can implement a profitable CRM strategy, and turn the massive amounts of data being collected by online play into a goldmine that can significantly increase their revenue-per-gamer.
Most importantly and what gives them competitive advantage is that they understand the sheer size of data being collected is too much for even the best team of analysts to manually churn through, so they have turned to predictive analytics for help.
By leveraging predictive models publishers are able to analyze historical campaign performance and determine which two promotions are going to generate the highest response rate per gamer.
By using the combination of statistics and data mining, predictive analytics provides an automated way to process and make predictions from the mounds of data collected, which typically can include hundreds to thousands of attributes per gamer: session activity, purchases, downloads, titles played, number of friends, genre preferences, and the list of data points goes on.
Marketers now have the ability to make accurate predictions on future gamer behavior to more effectively target and increase sales by identifying which customer is most likely to purchase a new title, play a micro-game, respond to a local promotional event, remain active and loyal, and more.
Generic Content
As a member of several gaming communities I receive on average three to four pieces of email per month from each company. While a small minority of people may not mind this email bombardment, it has created a cry wolf syndrome where customers no longer read the emails because they are just blasting out generic content and promotions to the masses.
However, the companies that are gaining competitive advantage are the ones putting in place campaign policies to limit the number of emails sent per month. By leveraging predictive models they are able to analyze historical campaign performance and determine which two promotions are going to generate the highest response rate per gamer.
In addition to campaign optimization benefits, predictive analytics offers a unique opportunity to drive new revenue through behavioral ad targeting and social networking models. With predictive analytics you can analyze and build a profile for each gamer allowing for personalized in-game advertisement. Imagine how much more an advertiser would pay for the ability to sponsor the football scoreboard to a highly targeted audience.
For example, being able to show a Ford Mustang sponsored scoreboard to one group of gamers that the model has identified as fitting the target profile because they enjoy playing racing games, display behavior indicative of males between the ages of 25 to 35 and make frequent purchases.
In parallel you have other customers playing the same game; however, their scoreboard is sponsored by Toyota’s Sienna minivan because these users although enjoy sporting games, also are extremely active playing family and kids titles and appear to have several gamers in the house ranging in ages.
As you can imagine the customer intelligence and targeting opportunities predictive analytics can offer to drive ad revenue are tremendous.
As mentioned before social networking is another powerful way to foster brand loyalty and continue to drive customer lifetime value. With peer-to-peer influence at an all time high, another great opportunity is to model and identify high valued gamers that have a strong friend network. The predictive model can identify the correlations and strength of relationships among friends. This intelligence can be used to identify active gamers who have the ability to influence their large inactive group of friends.
Once the model has pinpointed these “ambassadors” it’s time for marketers to provide incentives, promote parties, and reward them for getting their inactive friends active again with online play.
Although we’ve only covered a handful of ways predictive analytics can help drive additional revenue and strengthen community one can see how there is a goldmine sitting in the data collected from online play. The companies that have embraced predictive analytics are gaining competitive advantage as they are better equipped to target and service their customers. In turn, they are successfully strengthening their brand loyalty and increasing lifetime value with their customer base.
Provided by Next Generation—Interactive Entertainment Today
http://www.businessweek.com/innovate/content/apr2007/id20070416_933325.htm
