Google Ads has been introducing automated features, such as automated bidding, to its platform, which has sparked mixed reactions from advertisers. While automated bidding has improved over time, it remains overwhelming due to the numerous types and pros and cons. Online advertising has become increasingly complex due to competition and numerous routes, making it difficult to efficiently and effectively run ads.
Index finger is pushing ‘Bid’ button on computer keyboard
However, there are proven options that can help get your e-commerce store in front of the right people quickly and for a low cost. Google Ads offers features that can save time, making it a valuable tool for businesses looking to reach their target audience.
In this post, we will explore the good and bad sides of automated bidding on Google.
The Good Side of Automated Bidding
Automated bidding in Google Ads is lauded for its efficiency, which is one of its most significant advantages. This efficiency manifests itself in several ways:
Time Savings: Advertisers are relieved from the constant need to manually adjust bids for keywords or ad groups. Automated bidding takes care of bid adjustments in real-time, freeing up time for other strategic activities.
Resource Allocation: With the automated system managing the bidding process, small teams or solo advertisers can manage larger campaigns or multiple campaigns simultaneously without the need for additional staff.
Rapid Response to Market Changes: Automated bidding can respond to market conditions and search behavior changes much faster than a human can, adjusting bids accordingly to maintain competitive ad placement.
Consistent Optimization: Unlike manual bidding, which relies on intermittent attention, automated bidding continually optimizes bids 24/7, including times when manual management would not be feasible.
The efficiency gains from automated bidding can make a significant difference in campaign performance, particularly for advertisers with limited time and resources. It allows for the allocation of human attention to more creative and strategic tasks, such as ad copywriting and overall marketing strategy, while the system handles the intricacies of bid management. Automated bidding saves time by adjusting bids in real time, allowing advertisers to focus on other aspects of their campaigns.
2. Data-Driven Decisions
The data-driven decision-making aspect of automated bidding is a substantial benefit for advertisers using Google Ads. This advantage is rooted in the following aspects:
Advanced Algorithms: Google’s automated bidding utilizes sophisticated algorithms that can analyze vast amounts of historical data and contextual signals within milliseconds to determine the optimal bid for each auction. This level of analysis is beyond human capability in terms of speed and scale.
Machine Learning: Automated bidding systems use machine learning to predict how different bid amounts might impact conversions or conversion value. Over time, the system learns which patterns lead to the best outcomes for your specific goals.
Performance Targeting: Automated bidding strategies are designed to help achieve specific performance goals, such as maximizing conversions or targeting a specific return on ad spend (ROAS). By using historical data, the system can predict future performance and adjust bids to meet these targets more effectively than manual bidding.
Real-Time Adjustments: With automated bidding, decisions are made in real time for each auction based on the latest available data, including changes in user behavior, competitor activity, and fluctuations in the market. This ensures that bids are always optimized for the current conditions.
In essence, automated bidding leverages Google’s extensive data and machine learning capabilities to make informed, strategic decisions at scale, which can help advertisers improve the performance of their campaigns and achieve better results with less manual effort.
Google’s algorithms analyze vast amounts of data to optimize bids for each auction, which can lead to better performance than manual bidding.
3. Advanced Learning
The advanced learning capabilities of automated bidding in Google Ads represent a considerable advantage, particularly in terms of how it enhances campaign performance over time.
Machine Learning Algorithms: Automated bidding employs sophisticated machine learning algorithms that continuously improve by learning from a wide array of signals, such as user behavior, time of day, device, and more. This allows the system to make increasingly accurate predictions and adjustments (Google Ads Help).
Auction-Time Bidding: One of the most powerful features of Google’s automated bidding is auction-time bidding, which uses machine learning to tailor bids for every auction based on a multitude of signals that manual bidding cannot process at the same speed or scale.
Conversion Optimization: By analyzing past performance data, automated bidding algorithms can predict which clicks are likely to lead to conversions and adjust bids to capture those opportunities, potentially improving the conversion rate and ROI of campaigns.
Adapting to Changes: The advanced learning aspect of automated bidding means it can quickly adapt to changes in the market, competitive landscape, and user behavior, ensuring that campaigns remain optimized even as conditions evolve.
Advanced learning through automated bidding thus provides a dynamic and responsive approach to bid management that can help advertisers achieve better results by making more informed, data-driven decisions in real-time.
Automated bidding strategies use machine learning to predict which clicks are likely to lead to conversions, potentially improving campaign ROI.
The Bad Side of Automated Bidding
1. Lack of Transparency
One of the main criticisms of automated bidding in Google Ads is the lack of transparency. Advertisers often encounter the following issues:
Opaque Algorithms: The inner workings of the automated bidding algorithms are proprietary and not fully disclosed by Google. This can leave advertisers uncertain about how their money is being spent and which factors the algorithm is considering when making bid adjustments.
Limited Insights: While automated bidding provides some reporting on performance, it does not always give detailed information about why certain decisions are made, which can be frustrating for advertisers who are used to having granular data at their disposal.
Difficulty in Troubleshooting: When performance issues arise, the lack of transparency can make it challenging to diagnose problems and take corrective action, as it’s not always clear which factors are influencing automated decisions.
Perceived Loss of Control: For advertisers who are used to manually tweaking bids based on their analysis and intuition, the black-box nature of automated bidding can feel like a loss of control over their campaigns.
This lack of transparency can be a significant drawback for those who prefer to have a clear understanding of how their advertising budget is being allocated and for those who want to maintain hands-on involvement in their bid management strategy.
Advertisers may find it difficult to understand how bids are being adjusted, as automated bidding can be seen as a “black box” with limited insights into decision-making processes.
2. Loss of Control
The loss of control is a notable downside of automated bidding in Google Ads, as it can be a significant concern for advertisers who are accustomed to managing every aspect of their campaigns.
Bid Adjustments: With automated bidding, advertisers cannot make manual bid adjustments at the keyword or ad group level. This means they must trust the algorithm to make the right decisions, which can be difficult for those who have developed successful manual bidding strategies.
Strategic Flexibility: Automated bidding may not account for strategic shifts or insights that are outside of historical data and machine learning predictions. Advertisers might find it challenging to implement bespoke strategies that require a nuanced understanding of their business or industry.
Market Segmentation: Advertisers lose the ability to adjust bids based on their analysis of market segments. Automated bidding treats all market segments based on the data it has, which may not align with an advertiser’s unique insights or experiences.
Reliance on Algorithmic Interpretation: Advertisers are at the mercy of Google’s interpretation of what will drive conversions or achieve other set goals. This can be problematic if the algorithm’s interpretation does not align with the advertiser’s understanding of their audience or business goals.
The loss of control with automated bidding means that advertisers must have a high level of trust in the system’s ability to manage bids effectively and must be comfortable with a more hands-off approach to their bid management strategy.
With automated bidding, advertisers relinquish direct control over individual bid adjustments, which can be uncomfortable for those used to hands-on management.
The learning period is a critical phase for bidding in Google Ads, and it can present several challenges:
Performance Fluctuation: During the learning period, the automated bidding system gathers data and learns how to best optimize bids for your campaign. This process can lead to fluctuations in performance, as the system may not initially bid as effectively as it will once the learning is complete (Google Ads Help).
Time Investment: The learning period requires a certain amount of time and data before the automated bidding can perform optimally. This means that advertisers may not see the best results immediately and will need to wait for the algorithm to adjust to their specific campaign parameters.
Changes Reset the Learning Phase: If significant changes are made to the campaign during the learning period, such as substantial changes to the budget or campaign goals, the automated bidding system may need to re-enter the learning phase, thus prolonging the time before the campaign is fully optimized.
Data Requirements: For new campaigns or those with little historical data, the learning period can be particularly challenging, as the system has less information to learn from. This can result in less-than-optimal bidding decisions being made during this initial phase.
Understanding these challenges is important for advertisers so they can set realistic expectations and plan for potential performance issues when transitioning to automated bidding or when starting new campaigns using this feature.
Automated strategies require a learning period during which performance can fluctuate. This period may lead to suboptimal results until the algorithm has enough data to make accurate predictions.
Hand holding auction paddle on smartphone. Auction online, vector illustration
In conclusion, while automated bidding in Google Ads can lead to more efficient and potentially more effective campaign management, it also introduces challenges such as reduced transparency and control. Advertisers must weigh these factors to decide whether automated bidding aligns with their campaign goals and management style.