Sales slumps and spikes are a nearly unavoidable part of doing business. But if you’re ignoring when and why they happen, you’re missing out on opportunities to mitigate or leverage their effects.

There’s a term for these predictable fluctuations: seasonality. The word “seasonality” is a really a misnomer: It implies that these patterns are related to seasons or the weather, when, in fact, they could be the result of any number of factors. For instance, a public company that sees a drop in the value of its stock following the release of earnings reports each quarter is experiencing seasonality. And seasonality can also occur in any time interval, whether it be annually or daily.

Therefore, seasonality is different for each and every business. The key to understanding the patterns your particular business experiences is to use your data to predict when these fluctuations will happen, and then strategize accordingly.

What Seasonality Can Tell You

So where do you start? Well, a time-series analysis — which looks at data points over a certain time interval, such as your historical sales data over the past year — will help you recognize patterns in your data and extract meaningful information. The analysis will reveal recurring peaks or dips, such as the nearly inevitable spike in fourth quarter sales for retail businesses due to holiday shopping activity.

But because seasonality goes far beyond Christmas gift purchases, you can use your time-series analysis to drill down on specific periods of time or to identify products that might be affected. For instance, a moving company’s seasonal analysis might show that few moves occur in the winter, which might lead the company to lend out its trucks and staff for delivery services during that time to keep revenue up. Or, a retailer could determine the seasonality of specific product categories it sells (e.g. activewear, handbags, outerwear) and then ramp up its marketing efforts just prior to the peak season of those items — and minimize ad spend when demand is low.

Even daily fluctuations fall under seasonality. A podcast producer, for example, could look to see when episodes are being downloaded. If listeners are downloading episodes mostly at 8 a.m. and 8 p.m., the producer could assume the seasonality of downloads is commute-related, and market accordingly.

Looking at Trends vs. Seasonality

Your time-series analysis doesn’t just take seasonality into account. It can also show you the overall trends your business is experiencing.

It’s important to note, however, that seasonality can obscure these trends. For instance, in an unadjusted view of your sales data, you might see a steep upward trend during the holiday season — but has that trend accelerated from the previous holiday season or stayed the same?

You won’t know that answer until you seasonally adjust your data, meaning that you remove the regular peaks and valleys from the sequence of data points altogether. Once you remove that component, you leave behind data that does not change based on season, weather or other recurring factor.

Why is this important? Trends, left unanalyzed, will fool you into misreading your data and making poor decisions. Let’s say you’re selling a product and business has been down. You’re thinking about reworking your product until November rolls around and, surprisingly, your sales begin to climb again. While you might be tempted to stick to your current product offering — maybe it’s back in style — your seasonally-adjusted data tells a different story.

In fact, what looked like an upward trend was just a seasonal effect. Your seasonally-adjusted data indicates that your business’s downward trend is continuing unabated. You smartly decide to rework your product offering.

Making the Most of Seasonality

As your time-series analysis might have shown you, tracking seasonality is tricky business. It encompasses weather patterns, business practices, holidays and more. Figuring out how to leverage it takes a deep understanding of its effects, as well as knowledge of your company and the markets you serve.

Luckily, if you have the right data, the ability to identify patterns is well within reach. It’s how you address those predictable fluctuations — whether through marketing campaigns or administrative changes — that can make or break your business.

Photo by JTGF

Brittany-Marie Swanson
Author
Brittany-Marie Swanson

Web marketing manager at DataScience.com.