Nobody likes a slow website and as marketer/web analyst you probably know that page speed matters a lot. I had many discussions with clients/management about loading times and I realized that sometimes it’s hard to convince them to allocate more resources on optimization operations.
The problem is that they don’t know the potential outcome of those activities and that’s why I want to show you how I try to estimate the financial impact of slow pages using Google Analytics and Google Tag Manager.
Disclaimer! I know that there are many page speed KPIs but I won’t consider them this time simply because I’ll explain how to quickly estimate the revenue loss caused by a slow website.
1. Implement the Google Analytics Event in Google Tag Manager
So first of all, you have to download the JSON of the recipe and load it in your Google Tag Manager account.
2. Trigger the event only on the landing page
As you can see in the screenshot above I changed a bit the trigger of the event because I decided to fire the event only on the landing page. Mainly for two reasons:
- I realized that in this way it’s easier to bucket the Sessions
- I don’t want to send too many hits to my Google Analytics
Once you have done the adjustment, you can publish the changes you made.
3. Build the segments and analyze the data in Google Analytics
After a couple of days, you’ll have the first results. Now you have to decide the different buckets.
I used regular expressions to create 3 different segments in Google Analytics:
- loading time of the landing page between 0-3 seconds
- loading time of the landing page between 4-6 seconds
- loading time of the landing page between 7-9 seconds
4. Estimate the revenue loss
As you can see below, the page speed of the landing page has an impact on the CR of the three groups.
Now you can apply the CR of the best segment to the other two segments and estimate the potential uplift if you would optimize the page speed of your webiste.
Session X CR of fast landing pages = potential uplift/revenue loss
I recommend to also check the different Google Analytics reports and try to find where you have the main issues (devices, pages, geographies,..) and combine this estimation with an in-depth analysis of the performances of your website.