At MGID, we added a rule-based optimization feature to help advertisers manage native campaigns. Our solution will act at anytime the specified condition is met and either block the corresponding traffic source or adjust the CPC coefficient.

Native campaign optimization requires monitoring statistics on a regular basis, analyzing the actual data and comparing it with the goals, then turning off non-performing sources and adjusting the CPC to bring the actual KPIs ​​closer to target values.

With the new feature now available on the MGID platform, you can skip drilling down to the analysis of how every single traffic source is performing and focus on the upper levels of information. Let’s find out what exactly this solution can do for you.

Supported types of rules

To better address different optimization problems, we developed three types of rules with the following functionalities:


Blocks the traffic source provided that the specified condition is met. Blocking is available at the widget and sub-source level.


Changes the CPC coefficient based on the target value of the specified parameter. Note that the coefficient itself is determined by the rule.

Fixed Bid

Changes the CPC coefficient to the value specified by you, provided that the specified condition is met.

Tips and optimization strategies

Creating a rule, make sure that you properly set up the condition of your rule, so that our algorithm can deliver accurate results. For a condition itself, you can choose one of the following parameters, analyzing which a rule will carry out optimization:

  • Conversion Cost
  • Conversion Rate
  • Number of Conversions
  • Revenue
  • Profit
  • ROI
  • EPC

Operators will be available to set the condition to lower, higher, between, and not between. The rule-based optimization by MGID will implement one of the three above-mentioned actions anytime the specified conditions are met. For example, you can create a rule that will blacklist any widget that brings in conversions of higher than $2.

You also have to specify the period during which the system would determine if the condition is met. It is for this period that the actual values ​​of the parameter will be calculated to compare them with the condition and determine what actions should be taken. For example, when the rule selects widgets that have exceeded the limit on the number of clicks or the amount spent, it will take into account the data for this particular period, and not for the entirety of the campaign’s run.

One of the common optimization strategies is to set a threshold for launching the autoblocking rule at the x2-x3 rate of the target conversion cost per widget and x1-x2 rate per sub-source. Also, note that we added the possibility of manual intervention in the auto-blocking rule, and you can exclude certain traffic sources from the automated optimization process.

Another optimization strategy we highly recommend is using the Auto-Bidding rule together with the Auto-Blocking rule. This way, the auto-blocker could immediately exclude evidently non-performing sources, and then the auto-bidder could control the coefficient (increase and decrease it) where there is at least some result.

Final thought

Rule-based optimization by MGID allows you to save time and resources on campaign optimization, cut out underperforming traffic sources, and adjust the bids for the rest of them. Rules are created at the campaign level, run continuously, and reanalyze the data after every statistics update, so you don’t have to update it manually.

Setting the conditions for optimization, you can automate tracking particular KPIs of your campaigns, such as Conversion Cost, Conversion Rate, Number of Conversions, Revenue, Profit, ROI, and EPC. Once the parameter reaches the specified value range, the rule will implement the optimization action: block the underperforming traffic source, change the CPC coefficient to the value specified by you or adjust it based on the target value of the specified parameter.