This article explores how advanced control strategies, like PID controllers, improve Search Engine Marketing (SEM) by dynamically adjusting bids in real-time, demonstrating more efficient and cost-effective advertising through a practical example.
Unlock the potential of your Search Engine Marketing (SEM) campaigns by leveraging advanced control strategies like PID controllers. In this article, we define the problem of traditional bidding methods, introduce how PID controllers make real-time bid adjustments, and demonstrate their successful application in Apple Search Ads. By utilizing real-time performance data, our approach achieves more cost-effective bid adjustments and leads to better overall campaign performance.
Introduction
Search Engine Marketing (SEM) is vital for businesses to increase their visibility on search engines and attract potential customers. Companies invest significant resources in SEM campaigns to drive traffic and boost sales. However, managing these campaigns efficiently is challenging due to the fast-changing online market and the competition for ad placements.
Automated bidding is a method used in SEM to automatically adjust bids for keywords based on predefined rules and algorithms. These systems are popular because they save time, reduce manual effort, and optimize ad performance by making real-time adjustments based on historical data. However, traditional automated bidding systems often struggle to keep up with rapid market changes, as they heavily rely on past data and set rules.
One of the main challenges in SEM is optimizing the advertising budget to achieve the best results. This involves continuously adjusting bids for keywords to remain competitive while keeping costs under control.
Advanced control strategies can be used to tackle this issue. Techniques from engineering, such as Proportional-Integral-Derivative (PID) controllers, offer a way to create more adaptive and responsive bidding systems. These systems can adjust bids in real-time based on current performance, ensuring more efficient use of the advertising budget.
Problem Definition: Real-Time Budget Control
In Search Engine Marketing (SEM), one of the biggest challenges is managing the daily spend for each ad group to ensure that the allocated budget is used effectively. The online market is highly dynamic, with keyword prices and competition levels constantly changing. This makes it essential to continuously adjust bids to stay competitive, maximize ad visibility, and control costs.
The problem can be defined as follows:
- Objective: Ensure each ad group spends its allocated budget efficiently while optimizing performance metrics like cost per acquisition (CPA).
- Constraints: The total spend must not exceed the budget allocated for each ad group.
During our campaign management, we noticed that traditional automated bidding systems, which adjust bids based on historical data and predefined rules, were often struggling in this fast-paced, changing environment. They lacked the agility to react to real-time market fluctuations in keyword prices and competition, often resulting in inefficient budget usage and missed opportunities.
To address this, we needed a solution that could dynamically adjust bids in real-time, responding quickly to changes in the market. This is where PID controllers come into play, offering a more adaptive and responsive approach to managing SEM campaigns.
Proposed Solution: Applying PID Controllers
When managing SEM campaigns, there are two main approaches to adjusting bids: feedforward and feedback control. Feedforward control involves setting bids based on predicted market conditions and historical data. While this can be effective, it often falls short in rapidly changing environments because it doesn’t account for real-time performance data.
Feedback control, on the other hand, adjusts bids based on the actual performance of the ads, enabling more dynamic and responsive management. This is where PID controllers come into play. A PID controller continuously monitors the difference between planned and actual spending, adjusting the bids to minimize this gap and ensure that the budget is spent efficiently.
Here’s how a PID controller works in simple terms:
- Proportional Control: This part of the controller makes immediate adjustments based on the current difference between the planned and actual spending. If the actual spend is too high or too low, it adjusts the bids to bring the spend closer to the target.
- Integral Control: This component accounts for accumulated differences over time. If there has been a consistent deviation from the target spend, it makes adjustments to correct this long-term trend.
- Derivative Control: This part predicts future spending patterns based on recent changes. It helps to smooth out the adjustments and prevent over-correction.
By combining these three elements, a PID controller can make bid adjustments that are both responsive and stable. It ensures that each ad group spends its budget efficiently, adapting quickly to market changes without the need for constant manual intervention.
Implementing a PID-based bidding system requires integration with existing SEM platforms and tools. This involves setting up real-time data feeds for monitoring the spend, configuring the PID controller, and automating the bid adjustments through API integration based on the controller’s output. By leveraging existing infrastructure, advertisers can enhance their bidding strategies without significant overhauls.
Preliminary Results: Apple Search Ads Use Case
To validate the effectiveness of our proposed solution, we implemented the PID controller-based automated bidding system for the Apple Search Ads network. The evaluation spanned four weeks across six different countries and involved a total expenditure of approximately €200,000.
The evaluation used a pre-post analysis setup: for the first two weeks, bidding was managed manually without automation, establishing a baseline for comparison. In the following two weeks, we activated the PID controller-based automated bidding system while maintaining the same budget as in the pre-automation period to ensure a fair comparison.
We measured the primary metrics of the automated bidding system primarily using average cost per install (CPI) and average cost per acquisition (CPA). The PID controller-based bidding system delivered promising results, with an average 6% decrease in both CPI and CPA. The automated system optimized bids more effectively, reducing the cost associated with acquiring new app installs and customers, indicating a more efficient allocation and utilization of the advertising budget. The consistency of the expenditure across both periods ensured that the improvements were due to the automation itself rather than differences in budget allocation.
These results highlight the advantages of using PID controllers for automated bidding in SEM. By dynamically adjusting bids in real-time based on performance data, the system achieved more cost-effective results compared to manual bidding strategies. The observed decrease in CPI and CPA underscores the potential of PID controllers to enhance the SEM campaign efficiency. The automated system’s ability to respond to real-time market conditions and adjust bids accordingly led to more effective budget utilization and improved campaign performance.
Conclusion
In this article, we explored the application of PID controllers in SEM, focusing on dynamic budget management within given constraints. By leveraging PID control for real-time spend management, advertisers can implement a more responsive and efficient bidding strategy.
In our online use case with Apple Search Ads, we observed significant improvements in cost-effectiveness through dynamic, real-time bid adjustments. Our approach achieved better results in terms of CPI and CPA when compared to manual bidding strategies.
Looking ahead, integrating advanced control strategies, such as adaptive control, could further enhance the effectiveness of automated bidding in SEM. Adaptive control systems can automatically adjust the PID parameters in response to market changes, offering greater precision and adaptability. This capability is valuable, especially in highly dynamic and competitive markets, where constant tuning of control parameters is crucial for maintaining optimal performance. Additional improvements could include the use of machine learning models for advanced acquisition forecasting or model-predictive control.
In conclusion, the application of PID controllers in SEM represents a significant step forward in optimizing advertising spend. By combining real-time control with advanced optimization techniques, advertisers can create a more responsive and efficient bidding strategy, ultimately maximising the return on marketing investments.
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