Segmentation and Shakespeare

Interesting news release out of Stratford England—The Royal Shakespeare Company has developed a successful partnership with an American analytics firm to successfully segment their database to identify and engage different ticketing behavior.

DonorCast has been moving into the ticketing side of predicting modeling and this technique looks promising given adequate data (isn’t that always the case though…)

The Two-Step Cluster feature in SPSS is very powerful—our practice has only just touched the surface of application possibilities. This technique can be used as a finishing “sorting” of records, or can do a sort based on key variables pre-modeling (it can handle both categorical and continuous variables).

I will find some more relevant articles to share about clustering and segmentation techniques in the next edition. In the meantime, play around with this SPSS feature and consider how it might be applied in your work.

Advanced Analytics Move Centre Stage at the Royal Shakespeare Company

SAN FRANCISCO & LONDON--(BUSINESS WIRE)--Analytics software from KXEN is helping boost audiences at Royal Shakespeare Company (RSC) productions in a pioneering arts marketing move. The initiative, an Accenture-led program to segment audiences, has seen a 50% rise in ticket buyers at RSC's Stratford-upon-Avon theatre, a more than 70% increase in regular attendees and significantly earlier sell-outs for London bookings.

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Netflix Contest has Produced Prizes for the Analytics Community

In June 2007, we posted about the "Netflix Prize" - a contest promoted by analytics savvy movie-rental-house Netflix.

The goal: improve the accuracy of the existing Cinewatch movie recommendation system.

The prize: $1 million

Fifteen months along, and no model has come forward meeting the victory threshold of 10% improvement on matching accuracy. Fortunately, for everyone that doesn't work at Netflix, this contest has produced something of value.

The discussions and attempts conceived from this contest have provided those interested in analytics new perspectives and questions to ponder as we seek to analytically quantify and predict preference and behavior.

This article discusses some of the most interesting insights thus far:

"Open Questions" (text mining) has emerged as a theme to "fine-tune" the specificity of predictive models. Allowing individuals an opportunity to express, instead of forcing them to conform entirely to a pre-defined format, is really emerging as a more nuanced and "high-touch" approach. As I have posted previously, there is software emerging that is making great strides towards allowing text mining to be a pragmatic tool. Discriminate choice models of "ultimate" giving destination preference (athletics, fine arts, brick and mortar) for example, could be greatly enhanced by appropriately applied text mining.

Another model suggested that information about tastes as related genre, language, actors, directors etc, was surprisingly powerless compared to the star ranking of the movie itself. Perhaps this suggests that second tier "affiliation" data (I love Tom Hanks, or in the fundraising field, I was a Sociology major) may be more ambiguous than standard industry assumptions. At minimum, this revelation suggests that more consideration should be given to the importance of the top preference metric (for movies its a star rating, for fundraising, it is giving to the institution).

The $1,000,000 Netflix Prize competition has produced interesting results, even if no winner, 15 months in. Some of those results are a bit surprising; others we should have expected but didn't anticipate. So while participants haven't yet bettered the accuracy of Netflix's Cinematch recommendation algorithm by 10%, the threshold to win the $1 million prize, we can still take away lessons about predictive-analytics fundamentals.

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