Any long term business owner can tell you that sales slumps and spikes are virtually a guaranteed part of doing business. It's GOING to happen. But, if you’re ignoring when and why they happen, you’re likely missing out on ways to leverage or mitigate their effects.
In the analytics world, we have a term for these predictable fluctuations. It's called: seasonality.
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 can you learn from seasonality?
To get started you can run what is called a time-series analysis — which looks at data points over a certain time interval (key being interval, or repeat), such as your historical sales data from the same points over the past year. Doing this will help you understand patterns in your data and pull out useful information.
The analysis will reveal recurring peaks or dips, such as the nearly inevitable spike in fourth quarter sales for a retail business due to holiday shopping.
But because seasonality goes far beyond 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 real estate company’s seasonal analysis might show that few listings occur in the winter, which might lead the company to shift focus to filling their pipeline for spring/summer. Or, a store could determine the seasonality of specific product categories it sells (e.g. fishing poles, sleeping bags, snowshoes) 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 band, for example, could look to see when tracks are being downloaded. If listeners are downloading tracks mostly at 8 a.m. and 8 p.m., the band could assume the seasonality of downloads is commute-related or work-out sessions, 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, vacations, 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.