Recently I read a Search Engine Journal article about using AI tools to help you simplify the data analysis process of pay-per-click (PPC) campaigns and to identify PPC ad fatigue.
Of particular interest is, “By processing raw performance data from your campaigns, these AI tools can quickly analyze the data and provide insight into not only where the problem(s) can lie, but also glean insights as to why performance has shifted, like: Ad fatigue. Increased competition. A shift in consumer behavior.”
Wonderful right? Sure, if you know what to ask your AI tools to decipher the data. That’s what I’m going to share with you.
Ad Fatigue Clues
When reviewing your PPC campaign report, if you notice any of these things, it’s likely your campaign is experiencing ad fatigue.
- Decline in click-thru rate (CTR): If your ad initially performed well but now shows a decreasing trend in clicks, it may be time to refresh your creative.
- Decrease in Conversion Rates: A drop in conversion rates, even when CTR remains stable, can indicate that users are seeing your ad but are no longer motivated to act.
- Increase in Cost-Per-Click (CPC): As your ad becomes less engaging, you may need increase your CPC to reach the same audience. This can lead to inefficient use of your ad budget.
- Negative Feedback: Receiving negative feedback or an increase in ad reports can also be a sign that your audience is tired of seeing your ad repeatedly.
AI Prompt to Identify Ad Fatigue
Many AI tools, such as ChatGPT, CoPilot, Gemini, and others, can be used in conjunction with your ad report to analyze the data, as well as provide insights as to where the problem lies and why performance has shifted.
Tip: Be prepared to upload a .csv data file into your AI tool with the following data columns: conversions, cost/conv., clicks, impressions, CTR, Avg. CPC, and cost.
The Prompt
analyze the data and provide insight into not only where the problem(s) can lie, but also glean insights as to why performance has shifted, like: Ad fatigue. Increased competition. A shift in consumer behavior.
PPC Campaign AI Analysis Example
I imported an actual Google AdWords Q1 ’24 ad campaign report with the following data set. In ChatGPT, I used the above prompt to analyze the data.
Here’s the response:
The dataset contains the following columns:
- Ad group: Name of the ad group.
- Conversion: Number of conversions achieved.
- Cost / conv.: Cost per conversion.
- Clicks: Number of clicks on the ad.
- Impr.: Impressions, or the number of times the ad was viewed.
- CTR: Click-through rate, representing the percentage of clicks per impression.
- Avg. CPC: Average cost per click.
- Cost: Total cost incurred.
- Conv. rate: Conversion rate, or the percentage of clicks that led to conversions.
To provide insights, I’ll analyze potential problem areas, such as:
- Low Conversion Rate: Identify if specific ad groups have poor conversion rates, possibly due to ad fatigue, targeting issues, or increased competition.
- CTR and Impressions: Evaluate CTR and impression trends to understand consumer engagement levels.
- Cost Efficiency: Assess the cost per conversion and average CPC to determine if certain ad groups are costing more without significant returns.
Here are some insights based on the data:
- Average Conversion Rate: At about 3.34%, this is generally low. It could be due to ad fatigue or misaligned targeting, as consumers may not find the ads compelling enough to convert.
- Average CTR: Approximately 3.9%, which is a moderate rate but could indicate some engagement. If CTR was higher in previous periods, this might suggest ad fatigue or increased competition drawing attention away.
- Average CPC: $1.79, which seems reasonable but varies by industry and ad group. Higher-than-average CPC in certain ad groups without resulting conversions could indicate poor targeting or increased competition driving up bid costs.
- Average Cost per Conversion: At $2.81, this seems efficient but might not justify spending if conversions remain low. Rising costs per conversion could hint at shifts in consumer behavior or ineffective messaging.
- Total Clicks and Impressions: 219 clicks from 5,144 impressions suggest that the audience is seeing the ads, but the conversion performance indicates a need for optimization.
Potential Problem Areas and Recommendations:
- Ad Fatigue: Low conversion rates with moderate CTR could indicate that the audience is familiar with these ads but not moved to act. Rotating creatives or testing new messaging could refresh engagement.
- Increased Competition: Higher CPCs or impressions without a matching conversion rise may suggest that other ads are competing for the same audience, requiring more budget for impactful placements.
- Consumer Behavior Shifts: Lower conversion rates may reflect a change in what this audience is responding to. Testing different ad groups, targeting adjustments, or updated offers might realign the ads with evolving consumer needs.
- Optimizing these areas could help improve overall performance and drive more effective ad spending.
In this example, ChatGPT helped to identify PPC ad fatigue as well as other areas for improvement.
If you’re looking to take your PPC campaign to the next level and stop boring your audience, consider using AI tools to help you discover where you can tweak things.
Want more AI tips?
I have a lot of them, such as:
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- 8 AI Use Cases for Accounting Firms
- 3-Step Process to Craft a Resume Using ChatGPT
- Can an AI tool help me rank higher on search engines?
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- AI Marketer’s Cheat Sheets
