GigaOM posted an article claiming that we are in the midst of a data mining renaissance. Sounds good to me.
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.
The New York Times has picked up on the increasing popularity of the open source analytics and data mining software, R.
According to a recent report from the National Research Council, data mining is not a viable method of automatically identifying terrorist suspects.
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.
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.
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
by Alex Woodie
Business intelligence software developer SPSS recently unveiled a new version of Clementine, the data mining software that’s often deployed alongside CRM systems to help detect customer patterns, like fraud. Version 11 includes better data cleansing and transformation capabilities, many new calculations, and improved output.
Clementine is one of several predictive analytics products from SPSS that help companies improve their visibility into their customers’ buying trends and habits. In a nutshell, the software accomplishes this little feat of magic by analyzing information on past circumstances along with present events, and projecting their future actions, such as whether they might stop being customers, or try to rip somebody off.
With version 11, SPSS is aiming to make predicting the future even easier. The product includes new algorithms for credit scoring, complex pricing models, CRM and response modeling, forecasting, and rule-based models that incorporate users’ business knowledge, the company says.
Future predictions will arrive earlier than before, thanks to better tooling with Clementine, according to SPSS. The new release includes more robust transformation capabilities, more automated data cleansing, and the use of “optimal binning” to enable more predictive power, the company says. What’s more, the new Binary Classifier feature makes it easier to build multiple models simultaneously, so that the user can pick the best one.
The future is also clearer with Clementine 11.0 thanks to a new graphics engine that makes it easier to generate and edit images, and closer integration with SPSS statistical products.
Unfortunately for iSeries shops, the OS/400 version of Clementine is still several months away from general availability, according to SPSS officials; the Windows version is available now. Stay tuned for more coverage of Clementine for iSeries in an upcoming issue of this newsletter.
CARY, N.C. — SAS, the leader in business intelligence was voted Best Data Mining Toolset vendor for the third consecutive year. Value, reliability and broad applicability are the hallmarks of the Intelligent Enterprise Readers’ Choice Awards, the annual pick of preferred vendors. SAS also received 87 percent of votes in the customer satisfaction category for Web/Clickstream Analytics, tying for the top spot.
“SAS has a successful 30-year track record developing proven analytics capabilities,” said Mary Crissey, Analytics Product Marketing Manager at SAS. “Our investment in breath of analytics and accuracy leads the industry. Intelligent Enterprise readers who are SAS customers enjoy the solid analytical foundation that is needed to gain the competitive edge.”
“Each year, subscribers select their preferred vendors, and SAS was a clear leader,” said Doug Henschen, Editor of Intelligent Enterprise.com. “Hundreds of readers voiced their confidence in technology excellence through their ballots.”
DataFlux, a SAS company, also received the Readers’ Choice Award for Best Data Quality and Profiling software.
SAS Analytics Software is Unmatched in the Industry
SAS offers an integrated suite of analytics software that allows customers to formulate and evolve their analysis to obtain the best results and discover insights hidden in new data more quickly and easily than before using SAS.
SAS Enterprise Miner streamlines the entire data mining process from data access to model deployment by supporting all necessary tasks within a single, integrated solution, all while providing the flexibility for efficient workgroup collaborations. Delivered as a distributed client/server system, it is especially well-suited for data mining in large organizations. SAS Enterprise Miner is designed for data miners, marketing analysts, database marketers, risk analysts, fraud investigators, engineers and scientists who face difficult challenges in solving critical business or research issues.
The SAS Web Analytics solution delivers accurate, up-to-date information on an organization’s entire Web presence. The solution turns high volumes of Web data into key metrics specific to the business, enabling decision makers to determine the success of online operations and to proactively refine business strategies as needed.
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