One of the most important and worthwhile analyses we do when we onboard new clients is our Seasonality Analysis, which allows us to understand trends and shifts during the year, not only for the site as a whole but at the product category and the product level as well.
Most of our clients experience the major seasons of Holiday including:
- Valentine’s Day
- Back to School
- Halloween
- Christmas
- New Years
And it’s especially important for us to know our clients’ particular seasonal trends for the purposes of planning budgets and campaign strategy.
It’s common for new clients to experience a major disconnect between their site’s seasonal performance and their paid strategy. We often find that previous paid marketing efforts operated in a silo, based not upon market or consumer trends based only on paid marketing performance.
In other words, the paid campaigns were built upon and evaluated only against themselves. This can mean a lot of missed opportunities. Below we’re covering 4 situations where a potential client can benefit from understanding seasonal trends before implementing a PPC Campaign.
Client A – The Needle in the Haystack
Let’s say that around Valentine’s Day, Client A’s Plush Toy sales start to pick up considerably, which is confirmed in a Google Analytics look at product performance over the last three years during the month before Valentine’s Day.
But on the paid side, Client A has only a single shopping campaign covering all toys. This exact scenario is common. The campaign may even be performing well and hitting its goals for Client A. But breaking out Plush Toys into a separate campaign and focusing budget and marketing towards these in-season products can mean the difference between making goals and creating double-digit YoY revenue and ROAS growth.
Client B – Inventory Management & New Product Categories
Our Seasonality Analysis doesn’t end at the client onboarding, either. It’s a great opportunity to prepare your efforts pre-season and understand their effect post-season—developing insights that will better prepare you for next season. In fact, right now may be the perfect time to run seasonal analysis for Back to School (BTS).
One way we might do this is by using this tool before Back to School for Client B. This report shows what product categories did really well in the three years prior, from July through September. It serves as a guide to which product categories we need to focus the marketing budget on during this period.
As we plan BTS with Client B, however, we discover that one major category that did well before is not going to be well-stocked this year. We will also need to find more product categories in order to achieve the YoY revenue growth goals. Fortunately, Client B has a new product line that they hope sells well and will be promoting heavily with sales throughout BTS.
With this new information in mind, we still take into account the product categories that will be ready to go and did well in prior years with the right amount of budget and campaign support while also implementing budget and campaigns to support the new product line.
In early October, after a successful BTS, we will use the same tool to understand how product categories performed against prior years and how our new product category performed against the other categories so that we can better plan for the next BTS season.
In this example, the new product line did well and has shown an opportunity for growth, which indicates that we are going to have to allocate more budget toward it next season. You can imagine how helpful this information may be to Client B for their planning purposes, especially if you have rigid budgets that need to be planned a year in advance.
Client C – Adapting to New Trends
Seasonality Analysis can also help identify any trends affecting your business because of COVID-19. For instance, one of our clients, Client C, is showing a particular surge in performance during a season that is typically their off-season. Below we can see the percent of revenue for each month (each row represents a category). This table is looking at June 2018–May 2019.
This table shows the following data:
- Each category performs relatively consistently. Since we’re looking at larger revenue figures, however, smaller shifts in percent represent larger dollar amounts.
- For the most part, we see November–January represents more of a peak season and April–May drops off the map, with the remaining months fairly consistent.
Now, if we look at June 2019–May 2020, we see a very different story:
In this case, we still see December as the peak month, except now the months that were previously a low season (March–May) are now comparable with their highest performing month in December.
We can see a wave of increased performance rolling across the table above. In a situation like this, where we can’t entirely predict how long or how high this wave will last, it’s important to do a few things:
- Remain highly agile in adjusting campaign budgets and bids so you’re not limiting your impression share.
- Create campaigns for products that may not have been profitable before.
- Keep tabs on impressions and impression share to monitor demand, at least weekly, since there are no comparison in prior seasons.
Client D – Take the Tool for a Test Drive
To make our Seasonality Analysis easy for you to report and use for your paid marketing efforts, we have created a simple tool. All you need is access to your Google Merchant Center account and Google Analytics. We recommend using Google Merchant Center to pull ID and Product Category data because your shopping campaigns are either structured using these IDs or their corresponding product categories.
Organizing this data by categories from your Merchant Center account will then make it a lot easier for you to optimize your campaigns. We also recommend this strategy because many accounts might not have Enhanced Ecommerce set up and therefore could be missing product categories in their Google Analytics, or they could be using it and the result may be too granular or to divergent from your Merchant Center structure.
We recommend looking back at a full year’s worth of data at a minimum, but three years is ideal (and it has to be complete years). You may have sampling occur, so it may require breaking up your look-back dates in Google Analytics and several downloads. But the more data, the more data you have with which to understand the trends.
You can access our seasonality tools, along with instructions for how to cut up your seasonality data, in less than 30 minutes! Just click below to get started!
We also included an instructional video reviewing the Client B example above that has even a little more detail!
If you should have any questions or would like us to do a more thorough analysis, please feel free to reach out.
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