We love working with brands pushing the boundaries and promoting positive change. iPlaySafe is an app that is breaking the stigma surrounding sexual health. Over the past 3 months, Edge has been tracking iPlaySafe’s creator campaign with the goal of creating awareness of the iPlaySafe app. Edge automatically tracked: over 70 posts across 11 different creators.
The results speak for themselves:
+2K post engagements
10K+ post reach
A whopping 20K+ story reach
iPlaySafe is a startup launching a product solely using Influencer Marketing. Traditionally they would use an expensive agency to deliver the campaign but with Edge, they are able to run their monthly campaigns without an agency or internal team. Edge does all the heavy lifting so their Marketing Manager doesn’t have to.
How do iPlaySafe agree a set of deliverables with 10 influencers every month, validate dozens of pieces of content are posted (at any time over a month) and measure the performance of each piece of content across multiple social platforms in order to optimise their campaigns and increase return of marketing spend. iPlaySafe’s Influencer Marketing Manager spends, on average, 5 days per month on agreeing briefs, tracking deliverables, measuring content performance, collating data and reporting on a campaign this size at a cost of £7,119 per year calculated at an average market rate salary.
Edge Agreements and Analytics. iPlaySafe connected with all the influencers they were working with through Edge and agreed the deliverables in Edge agreements. Once a piece of content went live Edge began tracking that content, showing it being delivered in the agreements workflow and measuring its performance. Automatically. This would normally be done manually by a team of human robots. Using Edge Analytics iPlaySafe are able to easily see the best performing influencers and content, they then optimise every month, changing their mix of influencers and content depending on performance, this would not be possible using an agency which retrospectively reports using inaccurate scraped data, or an inhouse team collating data points manually.