From Surf Wiki (app.surf) — the open knowledge base
Attitudinal analytics
Marketing technology application
Marketing technology application
Attitudinal analytics is a marketing technology application that involves the integration of online surveys that capture visitor intent and critical demographic attributes with the tracking of explicit behavior through click stream monitoring on websites. This quantitative user experience collects data from thousands of user sessions rather than hundreds. This data is typically compared against key performance indicators for performance, customer satisfaction and overall customer experience success or failure. Reports of finding and recommendations are used to improve a website or customer experience program that is heavily dependent upon the use of an online campaigns driving traffic to a website or collection of sites. Offline measurement can also be incorporated to extend the customer experience understanding to illuminate what elements of the online experience impacted offline behavior such as purchasing in a store or visiting a branch office.
Several vendors provide attitudinal analytics solutions as stand-alone offerings. Leading web analytics players are also providing partner integration frameworks to better integrate silos of intent, attitudinal and behavioral data.
References
References
- (2007-05-29). "Actionable Web Analytics: Using Data to Make Smart Business Decisions". Sybex.
This article was imported from Wikipedia and is available under the Creative Commons Attribution-ShareAlike 4.0 License. Content has been adapted to SurfDoc format. Original contributors can be found on the article history page.
Ask Mako anything about Attitudinal analytics — get instant answers, deeper analysis, and related topics.
Research with MakoFree with your Surf account
Create a free account to save articles, ask Mako questions, and organize your research.
Sign up freeThis content may have been generated or modified by AI. CloudSurf Software LLC is not responsible for the accuracy, completeness, or reliability of AI-generated content. Always verify important information from primary sources.
Report