Performance drops in PPC don’t come with explanations.

One week, your conversion rate and ROAS look fine. The next, they soften, sometimes during a “busy” season, sometimes during a known lull, sometimes right after you made changes you thought would help.

If you spend time in PPC communities, this uncertainty is familiar. In Reddit threads across r/PPC and r/GoogleAds, advertisers repeatedly ask some version of the same question:

• “If I cut spend, will I hurt learning and slow future growth?”
• “If I keep spending, am I just bleeding budget because demand is gone, or because the auction got more expensive?”

These aren’t beginner concerns. They come from people managing real budgets, trying not to do long-term damage based on short-term signals.

And here’s the uncomfortable truth: Not every drop is a problem.

Sometimes it’s seasonality, and other times it’s the start of something bigger.

Why raw PPC metrics mislead you

If you read enough PPC threads on Reddit, a familiar pattern shows up:

“Nothing major changed… but performance is clearly worse. What am I missing?”

That question comes up constantly, and Cory Lindholm explains why: most PPC metrics are descriptive statistics, not explanations.

CTR, CPC, CPA, and ROAS are averages by default. They summarize what happened, but they don’t tell you how reliable those numbers are, or whether a change is meaningful enough to act on.

In his PPC Town Hall masterclass, Cory points out a common trap: averages often lie, especially when PPC data is skewed by outliers. A single bad day, tracking issue, promo spike, or unusually expensive auction can distort an average just enough to make a normal fluctuation look like a trend.

That’s why Cory emphasizes looking beyond the mean; using medians, moving averages, and normal ranges, to understand what “typical” performance actually looks like.

Without that statistical context, it’s easy to mistake noise for insight.

And when that happens, seasonality and real decline become almost impossible to tell apart.

What seasonality actually means (and what it doesn’t)

In some Reddit threads, seasonality is often treated as a catch-all explanation: “It’s December,” “It’s summer,” “This always happens.” But that shortcut is exactly what creates bad decisions.

As our CEO, Fred Vallaeys, has pointed out repeatedly, seasonality is not “performance going down.” It’s the presence of repeatable patterns in your data, patterns that show up again and again over time.

Two important implications follow from that:

• First, seasonality isn’t universal. Q4 is not automatically “good,” and summer isn’t automatically “bad.” Every business has its own demand cycles, shaped by products, customers, buying timelines, and even conversion lag.
• Second, a one-time dip, even a sharp one, doesn’t qualify as seasonality. Without repetition, you’re looking at a fluctuation, not a pattern.

This is why assumptions are dangerous. As Fred puts it, assumptions aren’t analysis.

Until you separate repeatable cycles from underlying trends, you can’t tell whether a dip is normal, or a signal that something has actually changed.

How to approach seasonality analysis in practice

Once you accept that raw metrics aren’t enough, the next question is practical: how do you actually separate seasonality from real change?

Fred’s approach is deliberately simple. Instead of relying on intuition, he runs a seasonality decomposition on historical PPC data to see what’s really driving performance.

The method starts with weekly data, not daily fluctuations.

Seasonality depends on repetition, so at least a year of consistent history is needed to identify reliable patterns. Clean data matters here, as missing weeks, tracking issues, or inconsistent naming can distort the result.

From there, the data is decomposed into three components:

• Trend (baseline): the underlying direction of performance over time
• Seasonality: repeatable cycles that show up consistently
• Residual: noise, outliers, and one-off events

This makes it possible to answer questions that dashboards alone can’t: whether the baseline is actually moving, whether a dip fits a known cycle, or whether recent changes are just randomness that shouldn’t trigger action yet.

The key shift is mindset.

As Fred often emphasizes, analysis replaces assumptions. You stop asking “what should I change?” and start asking “what’s actually changed?”

Running a Quick Seasonality Analysis with ChatGPT

If you want to test this approach without building your own models, you can run a basic seasonality decomposition using ChatGPT.

The process is a simple 3-step process:

• Step 1: Export weekly PPC data (at least one full year)
• Step 2: Clean obvious issues like missing weeks or tracking gaps
• Step 3: Upload the file and ask ChatGPT to perform a seasonality decomposition on a key metric like conversions or conversion value

This produces a clear split between trend, seasonality, and residual noise, enough to validate whether a dip fits historical patterns or signals a real shift.

Fred has documented this step-by-step, including prompts, caveats, and ways to extend the analysis across segments and channels.

If you want the full walkthrough, read Fred’s guide on running seasonality analysis with ChatGPT.

Making seasonality analysis repeatable with Seasonal Performance Trends

While ChatGPT is useful for one-off analysis, seasonality isn’t a one-time problem. Performance changes continuously, and re-running exports and decompositions quickly becomes manual and error-prone.

That’s why Optmyzr’s Seasonal Performance Trends was built around the same analytical principles, without the DIY overhead.

The tool decomposes key PPC metrics into:

• Seasonal Trends: repeatable ups and downs based on historical cycles

Seasonal Trends Chart

• Change in Baseline: the underlying direction of performance, independent of seasonality

Change in Baseline Chart

Instead of guessing whether a dip is “normal,” you can see:

• whether today sits in a typical seasonal trough
• whether the baseline is flat, improving, or declining

As the analysis is always on, it becomes easier to explain performance shifts, plan budgets, and decide when optimization is actually needed.

What most advertisers should stop (and start) doing when performance dips

When PPC performance drops, the most common mistake is acting before understanding what changed.

Too often, teams react to a downward trend line by:

• cutting budgets aggressively
• restructuring campaigns mid-cycle
• chasing efficiency during periods of naturally low demand

When the dip is seasonal, those reactions can do more harm than good. And when the baseline is actually declining, delaying action because “this always happens” can be just as costly.

The shift experienced PPC managers make is simple but powerful:

• Stop reacting to surface-level metrics
• Start diagnosing which part of performance moved

That diagnosis requires separating baseline movement from seasonal cycles.

Once that’s clear, decisions about budgets, bids, and structure become far less risky, and easier to explain to stakeholders.

Identify seasonality before you optimize with Optmyzr

When performance dips, the hardest part isn’t making changes, it’s knowing whether you should. Seasonal Performance Trends helps you separate repeatable seasonal patterns from true baseline movement.

That’s how you can tell when a dip is expected and when it’s time to intervene.

So if you want fewer reactive decisions and more confidence in how you manage performance shifts, book a 14-day free trial and test out our tools!