Automatic bid management with Pareto Ads

Introduction

Also known as KBA, Keyword Bid Adjustment is a bid control system that uses Pareto artificial intelligence to find the best value range for a target based on its performance in paid media.

To find this optimum point, the algorithm uses more than 40 performance metrics, making a decision based on data and with a statistical confidence of 95% by default.

How can I implement the KBA?

The changes are implemented automatically and do not require the approval of the account manager. Every day, you'll see cards like this in the Done column of your Marketing Board:

For the correct implementation of the KBA, the campaign must have:

• Minimum of 15 days history;
• Use the Manual CPC or Optimized CPC bidding strategy;
• Campaign types: Search, Shopping and Display (traditional or gmail).

NOTE: If your campaign uses Google automated bid management, our recommendation is that you implement a campaign AB Test with the Pareto Ads system or wait at least 30 calendar days after changing bid strategy to implement the Pareto KBA.

Among the optimization possibilities, there are 3 main KPIs in the Pareto KBA: CPC, CPA and ROAS.

Differences between KBA and Smart Bidding

The two bid management systems have the same value proposition, which is to deliver automated bid control based on segmentation performance, through artificial intelligence and based on campaign metrics.

The main difference with KBA is the transparent delivery of changes and a greater sense of control over results. This is possible because the history of changes to the CPC Manual is recorded within the Marketing Board and on the Google Ads platform itself. With this information, the user can understand the path taken by the algorithm and thus choose the confidence level for the adjustment:

• Aggressive: reduced statistical confidence for short-term decision-making (up to 60% confidence). Used to avoid the cost of segmentations that don't deliver performance.
• Intermediate: reduced statistical confidence for decision making in the medium term (by up to 85%). Used to identify segmentations with conversion potential, which have not yet had the minimum amount of investment needed to generate results.
• Safe: use of standard statistical confidence of 95% to ensure safer changes in decision-making. Used to increase performance in the long term, where changes are made less frequently, but based on more consolidated values.