eCAFtech’s New Audience Measurement Solution on the market2 min read
eCAFtech provides marketing intelligence solutions for digital out-of-home displays, that enable businesses to more effectively communicate with on-site customers by using targeted and dynamic messages in real time. At the start of 2011, announces that it has just updated its web site, which now includes 3 new solutions:
- Media Player Framework DS Viewer Counter Solution;
- Wireless Transmission Framework DS Viewer Counter Solution;
- Embedded Framework DS Viewer Counter Solution.
The above 3 solutions aim to empower digital signage operators’ business by providing business intelligence that allows you to understand the following audience characteristics for your displays:
- Actual Impressions – The number of people who look at your displays
- Length of Impressions – How long people look for
- Potential Audience Size – The number of people who walk by
- Dwell Time – How long people stay near your displays
- Anonymous Demographics – Demographics of your audience
- Providing proof of performance metrics for your displays
- Understanding audience engagement levels
- Optimizing advertising based on accurate audience measurement data
As an emerging technology, digital signage has much to offer businesses. It’s flexible, targeted and affordable. Just as important, methods of measuring ROI for digital signage has become more creative and effective. eCAFtech’ s new audience measurement solution DSVC-200 can analysis the above 1 to 8 characteristics data accurately, in case to measure how your popular your Ad are, it will be industry’s complete to allow advertisers to measure effectiveness of their digital-out-of-home ad campaigns.
For more information, please visit our website.
Ecaf Technology, Inc. has devoted to research of biometric identification technology since 2005 and eCAFtech integrate unique face recognition algorithms into the development of application products. In 2010, eCAFtech has developed a powerful Face U host that utilizes the combination of two main recognition algorithms-Principal Component Analysis with eigenface and Linear Discriminate Analysis-to achieve at fast and accurate recognition of each individual face.