Automatic Seasonal Spikes (Forecasting)

The original Seasonal Spikes tool allows you to input manual seasonal sales spikes which is covered in this tutorial. You can also automate seasonality by pulling in past sales curves of products and applying those curves to today's velocity, to PREDICT what this product may do in the future. In this tutorial, we will walk you through how to automate seasonal forecasting.

WARNING: Forecasting for seasonality is an art and a science: partially math and partially knowing your products, your competitors, and your market. Nobody has a "crystal ball" that can predict the future. It is vital that you take ownership of your selected forecast and understand how the numbers are calculated. A "set it and forget it" approach is not an ideal method of forecasting and can lead to overstocking or understocking if you are not monitoring performance. As they say in the financial services Industry, "past performance is not a guarantee of future results."

Other methods for Seasonal Forecasting. If this tool is too complicated for you, or if you feel there are too many variables, please look at some of our other seasonal forecasting options here.


Why does my graph look so high or low?

In this video, let's discuss the most common issues related to a high or a low auto seasonality graph. Typically the cause is related to last year's seasonal period % being too small (you were launching this product, or coming out of a big stockout, etc.) thus throwing off the annual average line. You can edit and manipulate these %s to adjust your graph accordingly.

I just placed an order, then weeks or days later the forecast has changed. Now it says I'm overstocked or understocked!

Likely what happened is your velocity did not change to the degree that the past percentage periods performed, or you have used unrealistic % periods on a "borrowed" or manipulated graph.

How Do the Seasonal %s Recalculate?

The system will recalculate the seasonal periods %s (weekly, monthly, etc. depending on which is selected) each period. 

So, if you have a monthly % chosen, then we are using that % with today's velocity (which is recalculated each day) for the whole month. Then as that month rolls off and a new month rolls on, the % might change since your velocity has changed and units sold in each of the last 12 rolling months have also changed, which means the % across the entire chart has to be recalculated. 


Let's say your “annual average” for last year (the 100% black line) was 1000 units/mo. Let's say that last October you sold 480 units, which is 48% of your annual average. And each month following is its respective % of 1000/mo. 

Now October rolls off and we are looking at November 2022--October 2023. Based on that new rolling 12 months, your new annual average is not 1000/mo but 1200/mo. In November of 2022, you sold 600 units which is 50% of 1200/mo. And in October 2023 (the month that just rolled off) you sold 700 units which is 58%. 

So as months roll off, the % will change. And that's why you see (after a month or two) the dotted line (what it was when you first set up the tool) and a new blue line (the changes in seasonality as old months have rolled off and the annual average and respective %s have also changed). 

Turning on Automatic Seasonal Spikes

From your Forecast page click on the product, you would like to work with and scroll down to the box that contains "Seasonal Spikes."

Click on the blue "Off" button and a window will pop up. Along the top is the option to turn on automatic seasonal spikes.

How to use the Seasonal Graph

The first setting that will show on your seasonal graph here will correspond with the product and marketplace that you selected on the Forecast page. The graph has 3 lines and along the bottom are the time frames that you can see and adjust to set the seasonal trend. You can change the time frames to weekly, bi-weekly, monthly, to quarterly. In our example, we will use monthly

The straight black line represents your annual average sales "normal". From there you'll notice the curve will go above or below the black line representing when you'll have higher or lower than normal sales. The boxes below the graph allow you to adjust how much the seasonality is above or below your normal sales. For example, if you look at May it shows 46% which means you'll sell less than you normally would in that month.

The dotted line shows historical seasonality.If the product has a sales history, SoStocked will automatically plug in the numbers that best represent how the sales fluctuate up and down month to month throughout the year as shown below.

The blue line is Applied Seasonality and corresponds directly with the monthly variables at the bottom. As you change the variables for the seasonality the blue line will move up or down and just the dotted line will show what the historical seasonality was. This is useful when you think you had an off month and want to adjust the seasonal curve to fit something different.

You can adjust the seasonal variables manually or you can set the seasonal trends using the data from a different SKU or ASIN. Click on the drop-down menu and you can select another product and use that historical sales trend or you can select multiple products and have SoStocked work out an average across the multiple products' historical sales trends.

Working Out the Math

214 divided by 161 percent = 133 units. That means you sold on average 133 a week. In the month of August in this case we sold 161 percent of that average.

As you saw on the graph the seasonal trends are set by a percentage above or below a set yearly average shown on the graph as a black line at 100%. So what does that mean when you compare it to the adjusted velocity on your Forecast page? In our example, the adjusted velocity for this product is 11.5.

For a yearly average, we sell 133 a week. But at the end of May, we sold 214 a week which is 161% above average.

SoStocked will take the current adjusted velocity and divide it by the percentage in your seasonal tool to come up with the future predicted velocity and incorporate the automated seasonality.

In our example, the week of May 31 we sold 214 units which is 161% of the average of 131 units. So now it will take the current velocity of 17.34 and divide it by 161% which is 10.7.

You'll find your current seasonality added to the Inventory Timeline based on the current adjusted velocity.

Save this to one product or all products and enjoy the new automated seasonality.

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