PPC Seasonality Adjustments: Why They Might Hurt Your BFCM Campaigns

During the fourth quarter, pay-per-click (PPC) managers frequently consider applying PPC seasonality adjustments to prepare for major sales events like Black Friday and Cyber Monday (BFCM). The reasoning often suggests that anticipating a significant rise in conversion rates necessitates instructing automated bidding systems to bid more aggressively to capture that demand. However, a comprehensive three-year study challenges this common practice, suggesting it may not only be less effective than believed but could also actively harm campaign performance and profitability.

Analysis of thousands of advertiser accounts over three consecutive BFCM periods reveals that manual seasonality adjustments frequently lead to inflated costs and diminished return on ad spend (ROAS). This data-backed guide breaks down the findings, offering practical insights to help you manage your holiday campaigns more effectively and efficiently.

Understanding the Data: A Three-Year Performance Analysis

To rigorously assess the true impact of these adjustments, industry research meticulously compared two distinct groups of advertisers during the BFCM period, which was consistently defined as the Wednesday before Black Friday through the Wednesday after Cyber Monday. Performance for both groups was measured against a pre-BFCM baseline each year.

• Group 1: Advertisers who relied solely on automated Smart Bidding without manual seasonality adjustments.
• Group 2: Advertisers who did apply manual seasonality bid adjustments.

Across all three years, the performance data consistently revealed revealing patterns that challenge the conventional wisdom surrounding manual bid adjustments for predictable, high-traffic events like BFCM.

Finding 1: Smart Bidding Already Accounts for Holiday Spikes

One of the most significant findings was that Google's Smart Bidding algorithms are already highly proficient at handling the BFCM surge without the need for manual intervention. For the group of advertisers who allowed the automated system to operate autonomously, conversion rates still experienced substantial increases, demonstrating the algorithm's inherent capability to detect heightened purchase intent.

Conversion Rate Increase (Without Manual Adjustments):

• Year 1: +17.5%
• Year 2: +11.9%
• Year 3: +7.5%

This consistent uplift proves that the algorithm effectively identifies and capitalizes on higher consumer purchase intent, adjusting bids accordingly. It performs its core function efficiently, leveraging years of historical data to model highly predictable events such as BFCM, making external prompts largely unnecessary.

Finding 2: How Manual Adjustments Inflate Ad Costs

When you apply a seasonality adjustment, you are essentially instructing the bidding system to immediately increase bids by a specific percentage, based on your own forecasted conversion rate. The study demonstrated that the system tends to take this instruction literally, often without first validating it against real-time market data. This frequently leads to a dramatic and unnecessary inflation of cost-per-click (CPC).

CPC Inflation (No Adjustment vs. With Adjustment):

• Year 1: +17% vs. +36.7%
• Year 2: +16% vs. +32%
• Year 3: +17% vs. +34%

In every instance, advertisers employing manual adjustments observed their CPCs increase at roughly double the rate of those who trusted standard Smart Bidding. This occurs because the manual adjustment is layered on top of the algorithm's own automated response to market changes, resulting in an excessive and ultimately more costly bid increase.

Finding 3: The Impact on ROAS from PPC Seasonality Adjustments

When CPCs rise at a significantly faster rate than conversion rates, profitability inevitably declines. The data concerning return on ad spend (ROAS) clearly illustrates the detrimental financial impact of over-bidding. While the group without adjustments maintained stable or even improved ROAS, the group utilizing manual adjustments experienced significant drops in efficiency.

ROAS Change (No Adjustment vs. With Adjustment):

• Year 1: -2% vs. -17%
• Year 2: -1.5% vs. -10%
• Year 3: +5.7% vs. -15.7%

The group that trusted the algorithm successfully preserved its efficiency, providing strong evidence that a more hands-off approach can often be more profitable during predictable peak seasons. Conversely, forcing the system to bid higher based on a forecast often resulted in paying substantially more for each conversion, eroding profitability.

The Core Problem: Overestimating the Conversion Lift

The fundamental issue with manual seasonality adjustments lies in precision. When you set an adjustment, you are making a specific prediction—for example, that your conversion rate will increase by 40%. If the actual increase is only 30%, that 10% gap directly translates into wasted ad spend. Experts note that the bidding algorithm assumes your forecast is perfect and does not hedge the bet against real-time performance.

This problem is particularly acute for an event like BFCM. As one of the most predictable retail events of the year, Google’s models are already trained on immense volumes of historical data for it. As the study concludes, seasonality adjustments are most effective for short, unexpected events that the algorithm cannot anticipate, such as a spontaneous flash sale or a sudden surge in interest driven by an influencer. For BFCM, the system is already well-prepared.

The Trade-Off: Weighing Revenue Growth Against Efficiency

Despite the observed negative impact on ROAS, the study did find that using manual adjustments often led to higher overall revenue growth, particularly prominent in the first two years of the analysis.

Revenue Growth (No Adjustment vs. With Adjustment):

• Year 1: +25% vs. +50.5%
• Year 2: +30.3% vs. +52.8%
• Year 3: +33.8% vs. 39.9%

This presents a clear strategic choice for advertisers. If the primary goal is to maximize raw revenue and aggressively capture market share, potentially regardless of the immediate cost efficiency, then proactive bidding via adjustments could be a valid approach. However, if the goal is to drive profitable growth and maintain a healthy ROAS, the data strongly suggests that relying solely on Smart Bidding is the more prudent strategy.

Practical Takeaways for Your BFCM Ad Strategy

Based on this comprehensive multi-year analysis, here are actionable recommendations to optimize your holiday PPC campaigns:

• Trust Smart Bidding for Predictable Events: For major, well-established holidays like BFCM, allow the algorithm to manage bid adjustments. It possesses access to vast real-time signals and historical data, far exceeding what any individual manager can process.
• Focus on Other Optimizations: Instead of laboring over bid adjustments, strategically allocate your time to other high-impact areas. This includes improving ad copy relevance, optimizing landing pages for conversion, managing inventory feeds for Shopping campaigns, and refining audience targeting.
• Use Adjustments for Unexpected Spikes: Reserve seasonality adjustments for truly unforeseen or non-recurring events. A few examples include a new product unexpectedly going viral, a surprise mention in major media, or a short-term, unprecedented flash sale that lacks historical data for the algorithm to learn from.
• Define Your Primary Goal: Clearly articulate whether your objective is maximum overall revenue or maximum profitability (ROAS). If efficiency and ROAS are paramount, avoid manual seasonality adjustments during BFCM. If capturing every possible sale is the absolute priority, you might consider them, but be prepared for a potentially significant drop in ROAS.

Ultimately, the evidence indicates that for predictable peak shopping periods, the most effective action is often to meticulously prepare your campaigns and then trust the automated systems to execute their job. This data-driven approach can significantly protect your budget and enhance overall campaign profitability during critical sales events.