Image Tech Helps Refine Pinfluencer Analytics

by , Jan 29, 2013, 9:12 AM
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As image recognition gains momentum from consumers switching to a picture-based Web experience, Pinfluencer will show brands how to identify the images driving the most traffic from Pinterest and eventually the Web.

The marketing-and-analytics company officially launched a parse engine Tuesday. The image recognition technology analyzes raw data based on URL patterns and SEO tags to help marketers identify images and products that are generating engagement for their brand on Pinterest and driving traffic back to their company sites.

The technology supports Sephora, Etsy, Z Gallerie, Orbitz and others tracking hundreds of results from contests and sweepstakes on Pinterest, some of which averaged $0.64 in revenue per pin in December 2012.

"We found images might appear on multiple pages and needed a technology that reports much more accurate analytics," said Sharad Verma, Pinfluencer CEO. Verma called the technology "image duplicate detection," described as "a multilevel negative selection algorithm" that filters images, not duplicates.

Marketers want to identify the most visually stimulating images that will prompt consumers to share across the Web. Verma said Pinfluencer plans to expand its offering to collect visual data beyond Pinterest. It will monetize traffic to landing pages from social visits powered by pins, comments, recommendations and thumbs up on Facebook, Twitter, Google+ and newer ecommerce platforms and shopping apps, such as wanelo, gojie, fab, and thefancy.

Pinfluencer has powered more than 100 Pinterest contests and sweepstakes. Brands using the Pinfluencer Promotions platform saw their follower acquisition growth rate increase by 156%, while pins per day rose 125%. The company also supports integration with analytics providers Adobe, Coremetrics, and Google.

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