Setting Smarter Search Bids

Google Ads automated bidding


Google Ads automated bidding is an enterprise-class solution that helps advertisers automatically set bids based on performance goals. Smart Bidding is a set of automated bidding strategies that use machine learning to optimize for conversions or conversion value. Smart Bidding sets precise bids for each and every auction to help drive higher conversion volume or conversion value at a cost efficiency that is comparable to or better than existing performance goals. It offers three core capabilities:

  • True auction-time bidding
  • Adaptive learning at the query level
  • Rich user signals and cross-signal analysis

Let’s explore each of these in more detail.

More than 80% of Google advertisers are using automated bidding.

Source: Google Internal Data, Global, 2021-03-16 to 2021-04-12.

Clock icon True auction-time bidding

For conversion and value-based bid strategies, Smart Bidding offers true auction-time optimization that sets bids for each individual auction, not just a few times a day. This gives advertisers a more precise level of bid optimization and the ability to tailor bids to each user’s unique search context. Rather than only adjusting bids based on aggregate performance across users, Google Ads bidding algorithms also evaluate relevant contextual signals present at auction-time such as the time of day, the specific ad creative being shown, or the user’s device, location, browser, and operating system.

Identifying the conversion opportunity of each and every auction helps to differentiate bids and optimize with a higher degree of precision. Take a finance advertiser, for example. It may be true that iOS users are more likely to open a checking account, or that smartphone users located in cities with higher branch coverage are more likely to visit a bank location. With auction-time bidding, Google Ads can detect the presence of signals like these to more accurately predict conversion rate or value and set a more informed bid for every search query.

Auction-time bidding offers even more bidding frequency and precision

Before auction-time bidding, marketers would typically set bids for each keyword manually.

Manual bidding: Setting a bid manually for each keyword could be achieved by changing bids in the Google Ads UI, using rules-based performance criteria (e.g. when impression share falls below X%, increase bids by Y%) or using the API. Due to time constraints, advertisers may only optimize bids for a subset of their keywords during each round of optimization, such as top performers or by product category.

However, the increasing amount of data available today makes it more complex for advertisers to set manual bids based on each user’s unique context. With auction-time bidding, contextual signals are used to set unique bids for each auction.

Google Ads auction-time bidding: Google Ads Smart Bidding utilizes machine learning algorithms to optimize bids for each and every auction. This is the most precise and effective way to set your bids.

Note

If you’re using Search Ads 360, you can use Floodlight conversions to optimize campaigns using Google Ads auction-time bidding.

Note

You can implement a Google Ads Smart Bidding strategy while using a third-party search management solution or in-house API to dynamically adjust bidding parameters and report across multiple accounts and search engines.

Gear iconAdaptive learning at the query level

Machine learning algorithms rely on robust conversion data to build accurate bidding algorithms that predict performance at different bid levels. While high-volume terms often provide plenty of conversion data for modeling, accounts typically have some low-volume or new keywords with little performance history that must be taken into account. For these low-volume keywords, bidding solutions rely on machine learning models to set bids that are the best estimate of conversion rates at that time.

For example, bidding solutions may test different bid levels to build the conversion rate model for a specific keyword. However, this may result in poor performance while the keyword accrues data, which can be a lengthy process depending on search volume. Another common process for modeling conversion rate performance on low volume keywords is to “borrow” data from the same keyword across match types or from higher-level ad group and campaign performance.

Smart Bidding expands upon this method and improves it by using query-level data across your account. If you’re using cross-account conversion tracking, it can also use query-level data from across your manager account. This gives the bidding algorithms significantly more data to make decisions with, and helps reduce performance fluctuations when keyword-level conversion data is scarce.

Why query-level learning improves your bidding

Google Ads bidding algorithms aren’t limited by where a keyword lives in your account structure. Instead, conversion data is leveraged at the search query level across ad groups and campaigns. This is especially beneficial for optimizing bids on phrase and broad match keywords, where a wide variety of search queries may match to a single keyword. In these cases, having just one keyword-level bid won’t optimize for conversion rate differences across queries.

Furthermore, let’s say you add new keywords or move keywords to a different ad group. Google Ads bidding algorithms don’t have to relearn performance from scratch. Because they learn at the query level rather than the keyword level, if a search query has already been matching to other parts of your campaigns, the algorithms simply apply what they’ve learned about it across your account to make more informed bidding decisions.

Magnifying glass iconRich user signals and cross-signal analysis

Every search query is different, and bids for each query should reflect the unique contextual signals present at auction-time. Signals like time of day, presence on a remarketing list, or a user’s device and location are key dimensions to consider when determining optimal bids. On top of evaluating these signals in each auction, Smart Bidding takes into account additional signals like a user’s operating system, web browser, language settings, and many more to optimize for performance differences across platforms and users. This additional context allows Smart Bidding to more accurately predict the conversion likelihood of each auction and set the optimal bid. The list below summarizes many of the important predictive signals Smart Bidding takes into consideration when optimizing bids. 

Contextual signals Description Example
Device System can optimize bids based on whether the query is coming from desktop, tablet or mobile

Advertiser: Car dealership

Bids take into account if a search for “car dealer locations” is from a desktop computer or a smartphone.

Location System can optimize bids based on the specific location (down to the city level) the user is located in or searching for, even if their location is set at a higher level

Advertiser: Bank

Even if location is set to New York state, bids take into account if a search for “new checking account” is from different cities within the state (e.g. Manhattan vs. Long Island where branch coverage may differ)

Time of day / day of week System can optimize bids based on the user’s local time of day and day of week in their time zone

Advertiser: Coffee shop

Bids take into account if a user searches at 7:00 AM before work vs. 12:00 PM at lunchtime on Monday

List-based audiences (RLSA, Customer Match, similar audiences) System takes audience lists for search ads into account

Advertiser: Online retailer

Bids take into account if a user has browsed a product during a previous site visit, is on a loyalty program list you’ve uploaded, or has a profile similar to existing customers. It also accounts for how recently the user was last seen.

Actual query System can optimize bids based on the text of the query that triggered the ad, not just the keyword it matches to

Advertiser: Shoe retailer

Bids take into account if a user’s query is “leather boots” or “boot repairs,” even if both queries match to the keyword “boots.”

Ad creative When you have multiple ad creatives eligible to serve for a given search query, system can optimize the bid based on which creative will be shown, including whether it points to a mobile app

Advertiser: Online travel company

Bids take into account if ad shown is the “latest deals” creative or the “popular getaways” creative, or if it points to the mobile site or app, based on which variation has a higher likelihood of converting on the specific query.

Interface language System can optimize bids based on the particular user’s language preferences

Advertiser: Spanish language learning site

For the query “learn a new language,” bids take into account whether an ad is shown to a user whose Google language setting is English or Spanish.

Browser System can optimize bids based on the browser the query is coming from

Advertiser: Software company

Bids take into account if a user searches for “mac software” from Safari or Chrome.

Operating system (OS) System can optimize bids based on the user’s operating system for that query

Advertiser: Phone accessories seller

Bids take into account if a user searches for “phone case” from an Android or iOS device.

Search Network partner System can optimize bids based on which search partner the ad appears on

Advertiser: Consumer packaged goods brand

Different bids placed if query is coming from more relevant searches on an e-commerce site vs. a news site.

Mobile app ratings and reviews System can optimize bids based on app user ratings and number of reviews

Advertiser: Gaming company

Different bids placed based on the rating and number of reviews your app has.

When signals work together

Manual bid adjustments for individual signals like device and location are a great first step to setting more precise bids. However, Smart Bidding goes steps beyond traditional signal analysis. Search context is not defined by just one signal, and Smart Bidding can recognize and adjust for meaningful interactions between billions of combinations of signals that can impact conversion rates.

Evaluating signals individually vs. analyzing cross-signal effects

Individual bid adjustments for signals such as device, location, and time of day look at performance data in aggregate. For example, a bidding solution may evaluate how your mobile conversion rate across users compares to your overall computer and tablet conversion rate, and set a corresponding mobile bid adjustment.

Although this method of aggregating data and evaluating performance averages helps to avoid making bid adjustments with insufficient data, it can also overlook the nuanced conversion opportunity between individual auctions. For example, a mortgage lender might determine that their mobile conversion rates are 20% lower than computer and tablet conversion rates and set a mobile bid adjustment of -20% as a result. However, this doesn’t account for the times of day when their mobile conversion rates are strong, such as in the mornings, when people may be researching loan options on their phones before work.

Furthermore, when you begin to layer on additional bid adjustments (e.g. for location), calculating them individually and then multiplying them together doesn’t account for the interacting effects of these signals. It can even produce unreasonably high bids if you combine multiple, large bid increases with a base keyword bid that’s already high.

Smart Bidding evaluates how signals interact with each other to identify meaningful correlations that impact conversion rates. By seeing which signal combinations are most predictive of conversion performance and adding these to bidding algorithms, Smart Bidding can calculate more holistic bids that account for how certain signals work together

Signals available with bid adjustments Example of exclusive signals for Smart Bidding
  • Time of day
  • Remarketing list
  • Device
  • Location
  • OS
  • Apps
  • Browser
  • Ad creative
  • Language
  • Actual query
  • Search partner

Smart bidding uses combinations of 2 or more signals. For example, it can take into account location, OS, and language before setting a bid at auction time.

 

 

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