The sun and heat have come back to Portland which means it’s the dry season again. Time for great growth in the garden but also a lot of watering. With the experience gained from the past few years, I have a pretty good idea of what to generally expect though there are always surprises.
Figuring out your seasons for your Shopify store is even more important. Some months might be slow, some might be high, and some are difficult to predict. That’s what seasonality or a seasonal analysis is for.
You can use some complex statistical models or AI to predict seasonality but oftentimes your gut plus looking for patterns is all you need.
One quick pattern to look for is the changes in the number of new customers who order each month.
In Repeat Customer Insights this is easy to find in the Cohort reports but you might be able to query for the data in other systems.
What you’re looking for is large changes in the numbers based on the month. Are there higher, lower, or an equal number of customers ordering compared to last month? The next month?
e.g. a typical Black Friday / Winter holiday season would show up as a large spike in November, an on-going high volume of orders, and then a drop in the last week of December. You might also detect a large dip in January that picks back up in the next month.
But everyone’s seasons will be different, some dramatically so.
Once you look at a few years of data you should have a gut idea of your seasons.
Write it down.
That’s going to be information that is useful for all kinds of marketing, inventory, and staffing plans. After the first time doing the seasonality analysis, you can just check it once every year to see if there’s been a change or shift.
Analyze your customer’s behaviors before they defect
Your customers aren’t yours forever. Some might have defected today, never to be seen again.
You need to analyze your customer behavior so you can reach them before they defect.
Topics: Seasonality Cohort analysis