A good marketer or marketing software would constantly change keywords bids and of ad groups based on past performance of these campaigns to achieve marketing objectives. There are 2 phases with different strategies for bid changes.


Experimenting before you change keywords bids

In this phase, you are trying out a new campaign and you hardly know the conversion ratio. Your objective here should be to learn as fast as possible by spending the least amount of money. Focus on getting at-least 10 conversions for your campaigns. If you are not getting any clicks because of poor average position, you should increase the bids so as to get enough clicks. An average position of 2-4 is a healthy sign of experimenting phase.

Day to Day pursuits to learn when to change keywords bids

You are already receiving conversions on a daily basis and would like to optimize the campaigns for conversions. In this phase, you should change your bids only based on the conversion ratio. Follow these steps for bid optimization

  • Find conversion ratio of every ad group or keyword. (I personally prefer single keyword ad groups). Use a time period which has enough conversions (last 30 days is a good number to start)
  • From your target cost per conversion, calculate the average CPC needed for individual ad groups using the formula average CPC = cost per conversion target * conversion ratio. for eg: if the cost per conversion target is 250 & conversion ratio of ad group is 10%. The average CPC = 250 * 10% = 25
  • From this average CPC calculate the max CPC bid. usually, the max CPC bid is 15-25% more than average CPC. max CPC = average CPC * 1.2. in the above example max CPC = 25 * 1.2 = 30

This strategy helps to get conversions from every single keyword or ad group at the same cost per conversion

I would suggest not to look at any other number when changing bids but the conversion ratio and target cost per conversion. The above example is what is popularly known as rule based bid optimization.



CEO and co founder of AdNabu. Exploring the intersection of data and marketing