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Case Study: $XXX Profit with 1 Lander and 1 Offer on Auto-pilot!

There have been a lot of rumors lately saying that Pop/Redirect traffic is dying out, it’s not worth loosing time and money on it unless you cloak etc etc etc.

If you are using that as an excuse, sorry to burst your bubble, this is ABSOLUTELY NOT TRUE and here I’ll show you the PROOF.

Pop/Redirect traffic is still a very solid traffic source that can generate high $x,xxx a day profit if done correctly. Obviously, that requires intensive work, accurate data analysis and a sound optimization process.

To better show you what I mean, let’s get into a real-world scenario where every new and experienced affiliate/media buye is familiar with.

The following campaign has been running on auto-pilot using TheOptimizer Mobile while conducting internal platform tests where the main focus has been on the platform’s performance itself.

To better test the stability of our platform we decided to run a real-world test in a high-volume geo using one of our supported traffic sources.

How did we find the offer?

This was a 15 minutes job! Went on Adplexity, checked the results of all Pop/Redirect campaigns in UK that was running for at least 5 days (date range last 2 weeks).

After picking one of the most promising campaigns from the search results, we “borrowed” and cleaned one lander and got access to the offer on the affiliate network detected in Adplexity.

This was just to test TheOptimizer. I would never suggest starting with one lander and offer only. As you may have heard a million times already, split-test, split-test and then split-test some more.

Funnel Setup

Just 1 Lander and 1 Offer, no split-test at all.

Auto-Optimization Rules

Once we created our campaign in Voluum and ZeroPark the only missing elements of our test before sending traffic to our campaign were the auto-optimization rules.
Here we go:

Rule 1 Block Bot Traffic 1 ( 0% LP CTR )

Condition: Block all targets with more than 100 impressions on the traffic source and 0 Clicks on the Landing Page.
Action: Alert & Execute
Rule Level: On Publishers
Rotation: Every 10mins
Stats Interval: Last 7 Days

Rule 2 Block Bot Traffic 2 ( Unrealistically high LP CTR )

Condition: Block all targets with more than 20 Impressions on the traffic source and LP CTR More than 80% with 0 Revenue.
Action: Alert & Execute
Rule Level: On Publishers
Rotation: Every 10mins
Stats Interval: Last 7 Days

Rule 3 Block Low CTR Targets

Condition: Block all targets with more than 70 Impressions on the traffic source and Less than 1% LP CTR on the Landing Page with 0 Revenue.
Action: Alert & Execute
Rule Level: On Publishers
Rotation: Every 10mins
Stats Interval: Last 7 Days

Rule 4 Stop Loss (Avoid losing more than $100)

Condition: Pause Campaign if Net Profit is less than -$100. This was to make sure that we were not going to waste more than $100 in this test.
Action: Alert & Execute
Rule Level: On Campaign
Rotation: Every 1 hour
Stats Interval: Last 30 Days

With everything in place, all we had to do was to start the campaign on ZeroPark and monitor our rule’s execution performance.

During the first week this campaign was bouncing from -70% to -85% ROI without showing anything promising at all. But, this was an expected result since we were bidding extremely low in order to block publishers progressively as we raised the bid.

Even though this campaign was in loss, we were extremely satisfied with the results. Auto-optimization rules managed to block approximately 150+ bad publishers per day, until there was no more traffic available on that specific bid.

And this is where this test became more interesting…

On week two after rising the bid by 60%, we started receiving more and more traffic which was a typical mix of good a bad publishers, complex enough to manually optimize it.

On day one of week two we logged around 200+ blocked publishers within the first 6 hours by Rule 1. Furthermore, during this week, the auto-optimization rules continued to block unwanted publishers resulting in higher traffic quality and ROI.

Considering our conservative bidding strategy that intentionally throttled the traffic flow, here are the results of the following weeks in numbers.

Week Spent Revenue Profit ROI
1 $63.18 $9.00 $(54.18) -86%
2 $117.90 $160.55 $42.65 36%
3 $150.89 $229.50 $78.61 52%
4 $199.84 $400.50 $200.66 100%
5 $152.36 $226.50 $74.14 49%
6 $136.54 $270.00 $131.46 95%
7 $197.72 $228.00 $90.28 46%
8 $52.66 $57.66 $5.00 9%
9 $55.77 $40.28 $(15.49) -28%
10 $64.40 $53.92 $(10.48) -16%
11 $182.64 $139.23 $(43.41) -24%
12 $17.41 $14.04 $(3.37) -19%

As you may notice there is a fluctuating ROI trend that changes with bid and optimization performance.

Once reached a bid of $7.5 CPM with an overall spent of $1,366.07 and convinced of the rule’s performance, we decided to pause this test campaign since keeping it profitable was not our goal.

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