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[Case Study] $60k Profit on Taboola Running Casino App Installs


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Today we are going to share with you a recent profitable campaign we ran on Taboola including all it’s details.

We often hear people say that these days it’s really hard finding a profitable campaign, it won’t last, natives are hard etc etc. As usual, most of these people never took the effort to at least test a few offers before throwing the towel and love to find excuses. So, to prove them wrong, we decided to run a campaign on native using an offer and traffic source available to everyone.

The idea is to find an offer available on affiliate networks using AdPlexity and trying to make it profitable. So, without losing further time, let’s get started!

First, let’s fire up AdPlexity and see what’s been hot lately. We put a filter showing ads in the last 60 days that have been showing up for at least 14 days in a row. Here’s how it looks like:


There are lots of results so need to filter them and combine it with info we got from our affiliate managers.
After spending some time reviewing the offers we decided to go with a Casino App install offer found on MundoMedia.

The reasoning on picking that offer was :

  • It has a relatively small payout, between $5-$10 so it will take less budget to test it out.
  • We saw it popping up quite often.
  • Our affiliate managers confirmed that it’s an offer performing well for other people too.
To get started quickly, we decided to get a few creatives and landers from AdPlexity to test the waters.

The creatives picked were:


and the two landers below on rotation:

We downloaded the landers directly from AdPlexity native and were ready to use after removing some tracking javascript that could affect our traffic.

Another tool we used in this case study of course was TheOptimizer Native. Which made our life easier from campaign uploading, to cutting loses with automatic rules and having accurate spend / revenue on a granular level.

So, we fired it up and uploaded the campaign on Taboola ( where it was seen the most running and has quality traffic from our experience).It creating combinations of our headline with the eight images picked. TheOptimizer also automatically created two variations automatically, one for iOS and one for Android, saving us time.

Before starting traffic, we put a set of rules on TheOptimizer. These are used to minimize losses from bad publishers & bots, increase budgets automatically on good performance and pause campaign if things go really bad.

So, the rules used are the three below:

- Stop campaign if tracker NET < -$30

- Block publisher if tracker NET < -$15

- Block low CTR sites

- Increase daily budget to $200 if ROI > -30%

- Block super high LP CTR publishers and add to bot list ( so we can re-use on future campaigns and not spend money again)

After these were in place we hit the Start button and clicks started coming in. Here’s how the campaign looked the first couple days till three weeks after:

Day 1 – Ad Spent: $99.31 | Revenue: $80 | ROI: -19.44%
Day 2 – Ad Spent: $198.61 | Revenue: $130 | ROI: -34.55%

3 weeks – Ad Spent: $8,634.61 | Revenue: $11620 | ROI: 34.57%

As you can see, the first day was pretty good for a new campaign, with just about -20% ROI. After this, the 4th rule kicked in and doubled the budget the second day, from $100 to $200 which caused a new influx of publishers coming in and lowered the overall ROI to -35%.

At the end of the second day, the campaign was automatically paused by rule no.1. However we enabled it back manually since most of the bad publishers were already stopped anyway and a few profitable widgets started popping up.

After letting it run for 3 weeks, the campaign was consistently profitable every day, doing around $3k in profit.

Apart from having a few stops for quality checks from the advertiser, the campaign ran for quite a few months, generating around $60k in profit overall. Here are a few more screenshots of the publishers automatically blocked by TheOptimizer, campaign performance on the Taboola dashboard and some more stats.




Key lessons learned from this case study:

  • Always use competitive intelligence tools like AdPlexity to get started. It’s easier to see what other are doing first instead of going in blind.
  • Pick low payout offers if you don’t have big budgets – Native can get expensive really fast.
  • Use automatic rules to stop publishers or bots bleeding money.
  • Collect these publishers into lists to re-use in the future so you won’t spend money on the same bots again. We have huge lists built through the years.