The Line Doesn't Always Go Up
And that's okay... here's why
Apple’s revenue hits crazy highs every holiday season. In early 2024, the company made about 119.6 billion dollars in Q1.
Once the holidays ended, the number dropped to about 90.8 billion in Q2 and then 85.8 billion in Q3.
If you only see the drop, it looks like a failure.
But this pattern repeats every single year. This is the shape of the Apple calendar.
When you look at any real dataset over time, you will notice something similar. The line never goes straight up.
Sales dip in certain months. Website traffic falls on weekends. Even top companies have slow quarters.
We do not call those failures. We call them patterns. Cycles. Seasons.
So why do we forget this when we stare at our own dashboards?
What’s a “Season” Anyway?
Seasons are changes that show up over and over at the same times.
You see them in everyday life:
Ice cream sales drop in December
Gym memberships spike in January and fade by March
Retail explodes in November and December, then quiets down in January
Travel bookings peak before summer and major holidays
None of these are mistakes. They are the natural breathing of human behavior.
The challenge is this: when you look at a dataset for the first time, you do not know what “normal” looks like. So the first dip feels like a crisis because you have no context.
Is It a Failure or a Season?
Here is a practical way to figure out what type of dip you are looking at.
1. Look at Your Historical Data
Start with the past. Pull data from previous years and see if the dip appears again.
Ask:
Does it happen around the same time each year?
Is the size of the dip similar?
How long did it take to bounce back before?
If the pattern repeats, you are looking at a season, not a failure.
2. Check External Factors
Some dips line up with events outside the dashboard.
Think about:
Holidays and school schedules
Weather changes
Industry cycles
Cultural moments and big events
If ad clicks crash during the Super Bowl, that is not a failing ad. People are simply watching TV.
3. Compare Against Industry Benchmarks
Do not look at your data alone.
Check:
Industry benchmarks
Competitor trends
Market movement
If everyone dips at the same time, it is likely seasonal.
If only you dip while others grow, that is a red flag.
4. Talk to People Who Know the History
People who have been in the company for years know the rhythm.
You will hear things like:
“Q3 is always slow.”
“Engagement drops after big launches.”
“Sales fall during school exams.”
Mix human memory with your data. It gives you strong context. These insights save you hours of digging through data.
How to Use This in Your Actual Work
Once you spot a seasonal pattern, use it to guide your work.
Build simple baselines.
Look at two to three years of data and note when dips and peaks usually happen. Add key factors like holidays or weather. Even one simple note like ‘Sales fall in Q3 and rise again in Q4’ helps a lot.
Add context to your dashboards.
Never show one number alone. Include last month and the same month last year. A month to month dip looks scary until you see year to year growth.
Annotate your charts.
Add small notes like “Holiday spike” or “School break dip.” This helps everyone understand the pattern without guessing.
Lead with context.
When you present results, start by saying what is normal for that season. Then explain what is new or different. This keeps the focus on insight, not panic.
Let’s Zoom Out for a Second
Data is not just numbers. It reflects human behavior. And human behavior moves in cycles.
People travel. People rest. People follow calendars, weather, and moods.
Not every dip needs a fix. Not every slowdown is a crisis. Many dips are simply seasons.
And when you learn to read those seasons, the data becomes easier to explain and easier to trust.
Your job is not to make every month go up. Your job is to know what is normal, spot what is different, and help people understand why.