Will Scully-Power

3rd generation analytics in action
November 17, 2009, 3:00 am
Filed under: Datarati, Web Analytics | Tags: , ,

The following is from a whitepaper jointly published by SAS and Web Analytics Demystified.

A very good read which should be read by every member of the Datarati.

True digital analytical competitors have already cobbled together their own third‐generation solutions using a variety of existing technology. Various kinds of work being done today using the third‐generation approach include:


• The use of modeling capabilities to predict the traffic and revenue impact associated with changing an investment in branded and non‐branded search engine keywords in Pay‐Per‐Click marketing efforts, rather than making changes and observing the financial impact in real‐time.


• The use of statistical models to determine the level of confidence obtained during complex behavioral segmentation efforts, rather than assuming that every segmented population is statistically relevant and appropriate for analysis and decision making.


• The use of forecasting algorithms fed historical data from digital and nondigital channels to estimate sales based on changing customer behavior, rather than assuming some level of linear or cyclical growth that does not account for a changing environment.


• The mining of huge paid search marketing datasets that combine online and moffline data to mine for keyword buying opportunities in the “long‐tail” based on multivariate cluster analysis and predicted lifetime customer value, rather than spending time evaluating hundreds of thousands of keywords manually.


• The use of modeling capabilities and marketing mix analysis to understand the relationship between different marketing channels, both online and off, rather than assuming independence and potentially making costly mistakes in marketing resource allocation.


• The use of decision optimization to determine which products or content to present in combination or sequence online to maximize levels of visitor engagement and sales, rather than making assumptions or decisions based solely on inventory or management opinion.


• The use of statistical models to better understand the real impact of design changes, different pages and new technologies on the consumer experience online, rather than assuming that “new is better.”


• The use of decision optimization and data from online and offline channels to understand how explicit consumer behaviors online such as search and navigation can be leveraged to improve layout in catalogs and physical retail outlets, rather than ignoring the treasure‐trove of online data detailing evolving consumer interests.


Download full whitepaper: http://www.webanalyticsdemystified.com/sample/Web_Analytics_Demystified_SAS_Revolution.pdf

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