Web Mining for Clementine greatly simplifies the preparation of Web data for predictive analysis using Web analytics technology proven over more than a decade—expertise unmatched by any vendor. Data miners easily process Web data using the Web Mining for Clementine source node integrated within the Clementine visual workflow.
Time-tested Web data processing rules drive the Web mining node as it automatically transforms floods of Web data into focused online business events. The Web mining node efficiently scales to enterprise Web analysis demands by processing only the data needed to create an accurate view of online behavior. Accurate user identification, business event mapping, sessionizing of visits, and noise data filtering help ensure that decision makers draw reliable conclusions about current conditions and future events.
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(1) Raw Web data is technically focused and difficult to interpret
(2) Prepare Web data by transforming it into business events
(3) Deliver actionable predictive insight such as "97% of searches result in a terminated visit"
The Web Mining for Clementine Application Template combines the power and flexibility of the Clementine workbench with predictive Web analysis applications designed to quickly deliver online insight. Since business event data is automatically prepared, Web analysis applications are easily executable without previous Web mining experience—eliminating the need for specialist support.
Web Mining for Clementine brings all the benefits of the award-winning Clementine workbench to predictive Web analysis—including easy data preparation, scalability, and interactive visualization. Clementine’s visualization techniques accelerate time-to-insight with the ability to derive new attributes and metrics. Unlike traditional Web analytics—which is limited to predefined, generic data—Web Mining for Clementine provides unrivaled flexibility to perform the additional data preparation required for actionable insight. Business event data can even be combined with data from other channels—such as transactional data and textual data from call center notes—for multi-channel analysis.
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