Feeding America grows site traffic 2.5 times using Twitter

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Domestic hunger charity Feeding America looked to share their web site content on Twitter to maximize traffic back to their website. The charity targeted audiences who often tweeted about topics very similar to Feeding America's site content. As a result of tweeting about trending topics recommended by the BrightEdge, and tweeting about specific topics (including specific web pages) in those tweets, Feeding America yielded 2.5 times the average traffic levels per post relative to similar Tweets made in the same period.

New Idea

@FeedingAmerica is the nation’s leading domestic hunger relief charity, reaching more than 37 million low-income individuals facing hunger each year. The charity uses Twitter to drive awareness about hunger alleviation efforts, share key news and statistics, highlight celebrity partnerships, and call citizens to action.

The team had an idea. Could they optimize the way they shared their web site content on Twitter? The goal was to maximize traffic back to Feeding America from links embedded in Tweets, targeting audiences who often tweeted about topics very similar to Feeding America's site content.

Using BrightEdge, Feeding America combined information on Tweet volumes for specific topics with search engine rankings and web analytics data to determine what to tweet.
Implementation

Working with Performics and using BrightEdge technology, Feeding America combined information on Tweet volumes for specific topics with search engine rankings and web analytics data to determine what to tweet about. They then matched that with their most relevant web site content to reference in outbound tweets. By consistently tweeting about trending topics where they already had great content, Feeding America achieved great results.

Impact

As a result of tweeting about trending topics recommended by the BrightEdge, and tweeting about specific topics (including specific web pages) in those tweets, Feeding America yielded 2.5X the average traffic levels per post relative to similar Tweets made in the same period.

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