Case Studies

There have been a number of new features, new targeting criteria and new banner placements introduced by the team at POF. Of course new features, and old features, always need to be tested to see if they could help turn your campaign from losing money to making money. I’ve split-tested bids, session depths (twice), login counts, browsers, mobile traffic and even used their tracking pixel to increase my revenues.

The targeting for every campaign, along with the ad copy I used for every campaign are included on the results pages. Most of the time I included my exact, or best creatives from that split test.

CPM Bid Effects

When I first started advertising on the POF platform I was interested to find out what kind of difference my bid made. I setup different campaigns with $0.15, $0.25, $0.35 and $0.45 bids. All campaign targeting, ad copy and creatives were identical and are included on the next page.

Session Depth

After seeing the results of my CPM Bid Effects case study, Ben informed me that higher bids would mean my ads were shown earlier to users and that my CTR and conversions would be higher. I wanted to verify his claim, so I decided to do a case study targeting different session depths. I split session depth into groups of 10, so I had identical campaigns that would target session depths of 1-10, 11-20, 21-30, 31-40, 41-50 and 51-60 separately.

POF Conversion Tracking for Profitability

POF had recently introduced their new tracking pixel. So I decided to use it to my advantage. I ran a broadly targeted dating campaign, analyzed the data and then created campaigns targeting only the highest converting criteria. In the end my goal was to create very highly targeted campaigns with really high conversion rates.

POF Conversion Tracking to Weed Out Non-Converters

This time I’m using POF’s tracking pixel to analyze the data on a very broadly targeted dating campaign to determine which specific criteria are not converting. I then created separate campaigns excluding those non-converting criteria to increase my overall conversion rate and ROI.

POF Ads Clicked – Who Clicks? Who Converts?

Again, POF introduced a new feature, a new targeting criteria called Ads Clicked. You could target users based on how many ads they’ve clicked. I created separate campaigns targeting the different Ads Clicked values, 0-4, 5-19 and 20+. I assumed that people who to clicked a lot were the same users who didn’t convert very well and I wanted to put my theory to the test.

WAP & IAB Traffic on POF

POF had recently allowed affiliates to start advertising their mobile traffic. I was talking to Ben at POF and he suggested that I try targeting IAB banners using mobile traffic. I decided to turn this into a case study. I posted this on my blog, but as an added bonus, you guys get the final campaigns that I ran that turned me a profit, including my 10 best creatives from every campaign. Some of these creatives had ctr’s higher than 1%. (no typo)

POF Login Counts

After previously doing a case study on session depth, I wanted to see how login count effected my impressions, ctr and conversion rate. I created separate, but identical campaigns targeting login counts of ≤ 50, 50-100, 100-150 and 150-200. Most people already target the lower login counts because of a general consensus that those users convert better. Do you wanna know if the consensus is correct?

POF Session Depth (Revisited)

For whatever reason I wanted to retest the session depth criteria. Again, I created separate campaigns targeting session depths 1-10, 11-20, 21-30, 31-40, 41-50 and 51-60. After seeing my results, I was further intrigued and decided to split up the best performing session depths even further. I created identical campaigns targeting session depths of 1-5, 6-10, 11-15, 16-20, 21-25 and 26-30. You’ll want to know the results!

Split Testing Browsers

I have a preconceived notion that certain users are smarter than other, certain users. Specifically, that Internet Explorer users tend to be dumber than Firefox, Chrome or Safari users. I wanted to see if my notion holds true and Internet Explorer users are more likely to convert than other users.

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