SEM Performance Analysis: The Search Data Map in Action

Leigh Anne Cronin / 18th April 2018 / Comment / PPC

In a previous post, we showed you how to investigate a shift in performance using our Search Data Map. Now let’s dig into scenarios with three common trends that can impact performance and how this map can help you understand these trends and optimize your account.

Scenario 1: Drop in Conversion Rate

search engine marketing drop in conversion rate

You’re focused on selling coffee pods and you’ve noticed a drop in your conversion rate yet you have not made changes to your site or product offers. What could have caused this drop? Bids? While your bids have a major impact on traffic, they do not affect the likelihood that someone will convert once they’re on your page. When it comes to paid search, the metrics to review pre-click and post-click performance are very different. This is because your bids, ad copy and average position impact the probability of a user to click on your page. Once they’ve clicked on your ad, the site experience — including site speed, interface and product features and pricing — impact the possibility for conversions.

While this pre-click vs. post-click data analysis is a general best practice, there may be times when certain pre-click metrics affect user trends on site. For example, if your ad copy calls out unique offers, such as $20 off all orders over $100, this can increase your average order spend or rate of conversions.

So, what else could have caused this drop in conversion rate? You may be capturing traffic that is not relevant to your offerings. Using the search terms report, you can find what terms were used when your ads were featured. For example, if you sell coffee pods but are seeing traffic from terms like “coffee mugs” or “coffee filters” these searchers are unlikely to convert since they are not looking for the products you offer. If this happens, it is best to add “mugs” and “filters” as negative keywords to ensure your ads are not shown in these irrelevant searches.

Scenario 2: Lift in Revenue

search engine marketing lift in revenue

As a sports apparel company, you’ve recently seen a large lift in revenue coming from your women’s volleyball department. As you dig into this category’s performance, using the map, you see that this lift in revenue was driven by a lift in conversions. While your conversion rate remained the same, your number of clicks significantly increased. There are two primary areas to look when you see a lift in clicks: click through rate and impressions. Your click through rate has remained relatively flat, while your impressions have seen a large jump, causing this increase in clicks. Whenever there is a change in impressions, it is important to look at your impression share. While your impressions have increased, you find that your impression share remains the same. How could this happen? When impressions increase and impression share remains the same, this means that there is an influx in users searching for these terms.

Seasonal products, like sportswear, often see shifts in impressions as there are higher demands for these products during certain times of the year. It is important to be aware of these trends to ensure you bid up during these times to capture as much traffic as possible. Looking at historical performance is a key tool to forecast bid and traffic trends as your competitors will also be boosting their bids to take advantage of this high-volume period. If you are new to paid and are unsure how seasonality will impact your business, Google’s Keyword Planner and Google Trends are easy tools to provide insights into historical performance using your keywords, locations and timeframe.

Scenario 3: Drop in Cost

search engine marketing drop in cost

As a clothing retailer, you’ve noticed a large drop in your paid search spend. While the number of clicks to your ads remains strong, your average CPCs have declined. Using the search data map, you know that this drop in CPCs can be caused by a change in click through rate, bids or shift in spend. As you analyze further you find that your click through rate has remained flat and you have not made any changes to your bids. So, this decrease in cost has come from a shift in spend.

To see where this shift occurred, compare your current campaign and ad group traffic to its previous performance (ie. week over week, or WoW) to see where you have changes in traffic. When you look further, you see that the “shoes” campaigns, which typically drive a significant portion of your clicks, saw a drop in clicks. While the “pants” campaigns, which historically drive a low number of clicks, saw a lift in clicks WoW. How can this shift in traffic impact a drop in CPCs and overall drop in spend? Well, as you know from using our ROAS calculator, your keywords can have a different value based on how they perform. The “pants” campaigns typically drive a lower revenue per click (RPC), so you bid relatively low on their keywords. The “shoes” campaigns, on the other hand, drive a much higher RPC, so your bids here a relatively high. With this influx in traffic coming to the “pants” campaigns and a drop in the “shoes” campaigns’ traffic, your overall spend will drop since the “pants” campaigns have lower CPCs.

search engine marketing campaign performance overall

search engine marketing campaign performance for shoessearch engine marketing campaign performance for pants

Now that you know what caused this shift in spend, it is important to find out why your clicks in the “shoes” campaigns dropped and lifted in the “pants” campaigns. As you know from the previous post, a change in clicks can occur when there is a shift in impressions and CTR. You know from your initial search that you did not have a change in CTR, so these changes in clicks must be from a change in impressions. Since your bids did not change, this change in impressions can either be from a change in impression share or the number of users searching for these items.

Historically, a drop in cost can usually be attributed to a drop in bids.

In conclusion, this map is here to help you find the source of your performance changes. Once you know where to look and how these data points relate to one another, you can find the underlying factors that cause these changes and react appropriately.

By Leigh Anne Cronin