Last weekend saw the final phase of my case study using POF’s conversion tracking to help turn a campaign profitable. Unfortunately, Phase 1 provided some skewed results because I tried to split $500 in testing between men and women and I only was able to spend $250 testing each gender. Simply put, that wasn’t enough and I realized it after analyzing the results. But, I took what data I had and went with it. Since I learned that $250 wasn’t enough testing, I decided to test the results of only the male gender because they provided the most statistically significant results. The campaigns didn’t lose as much money as the first round and none of the campaigns were profitable, but I’ll explain why it was my fault and what you can try to turn a similar campaign profitable.

Targeting Criteria: Body Type – Average

Analysis: On my previous case study an average body type showed a conversion rate of 5.56% which was higher than average for the entire campaign in the testing phase. On this final phase these numbers held true and the conversion ratio even increased a bit. This campaign had the second most amount of traffic. The CTR was the highest of any of the other campaigns. If you were going to start a campaign, I would definitely include this targeting criteria in your campaign.

Targeting Criteria: Drinking Habits – Socially

Analysis: When I did my original test campaign, this criteria converted right at the same percentage as the overall campaign.  In this second go round, it didn’t fare so well and performed well under what I expected to.  This target criteria by far had the most amount of traffic available.

Targeting Criteria: Education Level – Bachelors Degree

Analysis: This target criteria performed the best in the initial testing stage converting at 7.61%, but it only had 7 conversions.  I had high hopes for this campaign and a little bit of worry because I wasn’t sure if the conversion ratio was a fluke.  And as you can the conversion ratio didn’t perform as well as I had hoped.  After the testing, it converted just under the average ratio for the campaign.  I’m also pretty sure the target demographic for this criteria is pretty small, hence the CTR dying out on all my images really quickly and most images not even being clicked on after a few thousand impressions.

Targeting Criteria: Income – $35,001 – $50,000

Analysis: This is another target criteria that was pretty risky.  In my initial tests it only had 7 conversions, but it converted at 5.22%, well above the campaign average.  After sending a fair amount of traffic to it, it converted even better than in my initial tests.  The only thing holding this campaign back from being profitable was the CTR.  I struggled with CTR in this campaign for a reason unknown to me.  I used what I thought were some of my best images at this campaign without any luck.  So if you know you have some images with really good CTR, this is something you might want to build into your next campaign.

Targeting Criteria: Search Type – Long-term

Analysis: This criteria converted right at the campaign average and I wanted a fifth criteria to test so I threw it in there.  Unfortunately, the campaign didn’t convert nearly as well as it did in my initial tests, which could be contributed to it only having 6 conversions.  These users seemed to be pretty click happy as it had the second best CTR of all the campaigns, they just didn’t convert nearly well enough.

Final Thoughts

The day after the campaign I received an email from Convert2Media that informed me the payout for Singlesnet 25+ was raised to 4.50. So you can take all the revenue figures here and increase them by 12.5%. It still doesn’t make any campaign profitable, but it definitely helps out quite a bit.

It would really help if Plenty of Fish could include the estimated number of people in our target demographics to give us an idea of how long we can expect our images to last before the users succumb to banner blindness.

One of my biggest problems with these final campaigns was choosing the correct images.  When I was running these campaigns I had numerous other campaigns I was working on so it was tough to keep an eye on these campaigns, even while using Mr. Green‘s POF Tool to upload new images throughout the campaign’s livelihood.  If you can keep your CTR up on your campaigns you will be way closer to profitability than I was.  I also have to fault myself for not split testing images.  I rarely ever do this and is one thing I need to severely address.  I’ve done this a few other times and I do know that images to play a role in the conversion rates.

There are a number of things you can take away from this case study.  By looking at the numbers above you can clearly see two targeting criteria that convert better than others, so try building those into your campaigns.  Maybe you can even combine the criteria and see how they perform together, but that will limit the amount of traffic you can get it.  Or you can do it the other way and exclude poor performing criteria from your campaigns.

If you use any of the tips I mentioned here and don’t mind sharing in the comments below how it turned out for you, please do!

Related posts:

1. Case Study: Using POF Conversion Tracking to make a Profitable Campaign – Phase 2
2. Case Study: Using POF Conversion Tracking to make a Profitable Campaign – Phase 1
3. Case Study: Using POF Conversion Tracking to weed out non-converters – Results
4. Case Study: Using POF Conversion Tracking to weed out non-converters – Phase 2
5. Case Study: Using POF Conversion Tracking to weed out non-converters.