Welcome!
Welcome to the Marin Software Support Center. We're glad you're here. Here's what you can look forward to:
  • Real-time search so you can find what you need faster than ever.
  • Easy-to-follow video guides for our most popular articles.
  • Interactive simulations and Live Screenshots to make learning easy.
  • Regular content updates to ensure every word you read is accurate and up-to-date.

Bidding and Optimization

Home > English > Bidding and Optimization > Bidding Basics > Bidding Overview

Bidding Overview

Introduction

Bidding forms part of the core of the Marin platforms, both MarinOne and Marin Search. In this article, we'll provide an overview of bidding as a concept, plus go in-depth on how you can use bidding with our platform.

In a nutshell, automated bidding solutions are designed to calculate the optimal bid (cost-per-click) for keywords, ad groups, product groups, and placements, in order to achieve a specific goal. To begin, let's cover the important concepts that influence campaign performance, and then move on to an overview of our bidding solution.

Note: This article includes information on how to use this feature in either MarinOne or Marin Search. The terminology varies somewhat between the two platforms, so we'll indicate where there are differences for clarification.

How often does bidding run?


For standard Algorithmic or Portfolio bidding, Marin Search and MarinOne will calculate bids and push them to the publisher once each day.

For Awareness Targeting, MarinOne will calculate bids and push them to the publisher every 4 hours.

Note: Awareness Targeting is not available in Marin Search.

Volume vs. efficiency


Volume and efficiency have an inverse relationship if all other factors stay the same. This means that generating more volume will come at the cost of efficiency, and increasing efficiency will come at the cost of volume. This is an important concept to understand when setting up Bid Strategies (known in Marin Search as Folders), and evaluating the impact of bidding on overall performance.
 

1b.png

 

Every performance-based Bid Strategy (in other words: non-Awareness Targeting Bid Strategies) will maximize volume based on an efficiency target or budgetary constraint.

Volume can be:
 

  • Conversions

  • Revenue

  • Clicks 

  • Profit 
     

Efficiency can be:
 

  • Cost-per-Lead (CPL) which is defined as [Cost] / [Conversions]. A CPL goal allows you to maximize conversions.

  • Return on Investment (ROI) which is defined as [Profit] / [Cost]

  • Return on Ad Spend (ROAS) which is defined as [Revenue] / [Cost]. A ROAS goal allows you to maximize revenue.

  • Margin which is defined as [Profit] / [Revenue]

 

Although the formulas for ROI, ROAS, and Margin are different, they are all correlated and therefore represent the same thing - profitability. If one goes up, the others go up. If one goes down, the others go down.

 

External factors


From a bid optimization standpoint, external factors are considered to be all changes and outside forces that influence performance besides bid and bid multiplier changes. These factors can be categorized under three themes, and include:
 

  1. Campaign Optimization changes such as ad copy adjustments, geographic and audience targeting changes, and the uncontrollable quality score changes.

  2. Market Conditions in a real-time marketplace, which can be summed up by demand (changes in consumer behavior) and competition.

  3. Website Monetization, which is most often driven by web page design changes that impact the on-site conversion funnel.


These external factors can disrupt the balance between volume and efficiency in the same way that changing a bid can; potentially allowing efficiency to increase while volume increases, or causing efficiency to decrease while volume decreases. It is not possible to conclusively separate the impact of bid changes on performance from other external factors. While carefully constructed tests that attempt to hold some external factors constant can be used, market changes cannot. Tools such as Google Trends can provide directional insight (e.g. estimating consumer demand over time), but are not sufficient in providing a conclusive estimation of impact.

Bidding Algorithms


Marin offers two distinct performance-based bidding solutions -- an Algorithmic approach and a Portfolio approach -- in addition to Awareness Targeting. Each approach has its own benefits and balances market reactivity with statistical significance in order to provide a best-in-class optimization engine. Marin Bidding does not require the use of the Marin Tracker pixel and can, therefore, base bidding decisions on the tracking system (or systems) that you consider to be your source of truth.

Furthermore, Marin Bidding is a transparent optimization solution. The details behind each bid calculation can be viewed by clicking on the Show Details link in the Settings tab of a keyword, ad group, product group, or managed placement.

Algorithmic Bidding

Algorithmic bidding uses a progressive lookback and rules-based logic to calculate bids, alongside a patented Bayesian blending approach for low volume keywords. It is the default solution in the platform and supports automated bid calculations for keywords, ad groups, product groups, and managed placements. When compared to Portfolio bidding, the Algorithmic solution performs best in verticals with large low-volume keyword sets (e.g. Travel).

The algorithmic approach calculates bids in the following manner:
 

  1. A click threshold is calculated for each Bid Strategy/Folder using a cumulative binomial distribution at an 85% statistical confidence level based on the Bid Strategy/Folder's recent conversion rate. This click threshold determines if a biddable object has enough data to stand on its own, or if additional data must be borrowed from similarly-performing keywords in order to reach the desired statistical confidence level.

  2. The algorithm employs a progressive lookback for each biddable object. This determines how far back in time the algorithm needs to look in order to meet the Bid Strategy/Folder click threshold. The lookback period starts at 1 week and expands as it goes further back in time. Once the click threshold is met, the number of conversions is compared to the conversion threshold. If the conversion threshold is not yet met, the object will default to the Bid Strategy/Folder settings for conversion metrics.

    Note: The lookback starts with the most recent date with both click and conversion data and observes excluded date settings.

  3. If a keyword can reach the click threshold on its own, the initial bid is calculated based on the Bid Strategy/Folder target. If a keyword cannot reach the click threshold over its entire lifetime, it borrows data from similarly performing keywords in the same Bid Strategy/Folder and uses the combined data to calculate the initial bid.

  4. Bid rules (e.g. Limit bid change under %) are then applied to the initial bid, resulting in the final calculated bid that is sent to the publisher.

Portfolio Bidding

Portfolio bidding uses predictive modeling and portfolio trade-off logic to calculate bids. This solution only supports automated bid calculations at the keyword level; other biddable objects will default to Algorithmic bidding. Portfolio bidding also allows for strategies not available in the Algorithmic solution (click-maximization, profit-optimization, spend as a constraint), and performs best in verticals with smaller high volume keyword sets (e.g. B2B, Finance, Insurance).

 

There are specific keyword volume and eligibility requirements for using this solution and is therefore not available in the platform by default. Portfolio bidding can be requested by contacting your Customer Success Manager or by filing a ticket for the Marin Support team.

 

The portfolio approach calculates bids in the following manner:
 

  1. Predictive models are built for each high volume keyword and fit against an optimization curve based on the Bid Strategy/Folder optimization criteria. This includes an auction model (Bid vs CPC) and a volume model (CPC vs Clicks).

  2. Bids are calculated using the algorithmic approach for low volume keywords that do not have enough recent performance data to build an accurate keyword-level model.

  3. A tail term model is built for all low volume keywords in aggregate for the purpose of applying trade-offs.

  4. Trade-offs are made across head terms within the Bid Strategy/Folder in a portfolio approach and initial bids are calculated.

  5. Bid rules (e.g. Max Bid Change %) are then applied to the initial bid, resulting in the final calculated bid that is sent to the publisher.


While the algorithmic and portfolio bidding solutions optimize primary bids, Marin’s mobile bidding feature should also be used to handle the automation of mobile bid multipliers.

Bidding Health Recommendations 


If you would like additional recommendations for keeping your bidding running smoothly, check out our Bidding Health Recommendations article.

 

Last modified

Tags

Classifications

This page has no classifications.

 

wiki.page("Internal/Mindtouch_Launch_Sandbox/js.cookie.js")