How Page Views, Time On Site & Bounce Rate Predict For Changes In Quality Score And Revenue
Columnist Benjamin Vigneron explains how you can use website analytics data to derive insights that you can apply to your paid search efforts.
Over the years, Google’s Quality Score secret recipe has become more sophisticated and more challenging to optimize against.
From Google’s blurry definition, we know that Quality Score is mostly a function of your historical CTR (click-through rate), as well as the “quality of your landing page.” However, the landing page quality piece can be hard to quantify.
In this post, I will share a few findings about how engagement metrics (that is, page views, time on site and bounce rate) can be strong predictors for both your Quality Score and your revenue metrics.
Bounce Rate & Time On Site Found To Predict For Changes In Quality Score
When running a multiple linear regression analysis based on daily Quality Score, CTR, page views, time on site and bounce rate across millions of keywords, I found that three metrics out of four were correlated with Quality Score:
- The bounce rate was the strongest predictor for Quality Score, accounting for roughly 2.6 to 3.9 Quality Score points. A high bounce rate (that is, greater than ~40% in this particular case) pretty much guarantees a Quality Score lower than 7. (Note that, per Google, a low bounce rate does not guarantee a boost in Quality Score.)
- The CTR was the second-strongest predictor, accounting for 1.6 to 2.4 Quality Score points.
- The time spent on site accounted for 0.2 to 0.5 Quality Score points.
- Page views seemed to be a strong predictor, too; however, the data were not statistically significant for this particular data set (high p-value).
Essentially, if you want to stay away from high CPCs (cost-per-click) and low impression share due to a low Quality Score, you want to address those campaigns/keywords/product listing ads with above-average bounce rates, below-average CTRs and below-average time on sites, or any combination thereof.
Page Views, Time On Site & Bounce Rate Predict For Revenue Changes
While the Quality Score is a relevant metric to optimize against in order to minimize marketing costs, advertisers typically focus on the end revenue metrics. One of the main challenges often is to address revenue sparsity across thousands or millions of keywords, product listing ads, and so on — and that’s really when those engagement metrics come in handy, as they can help predict for revenue.
Indeed, from the data I collected, I found the following:
- Bounce rates were associated with 61 percent to 100 percent of the average RPC (revenue per click). While a low bounce rate does not guarantee a higher Quality Score, it does seem to guarantee more revenue, and vice versa.
- Page views were associated with 2.2% to 3.9% of the RPC.
- Time on site was associated with 0.4% to 0.7% of the RPC.
In short, if you haven’t collected enough revenue data across certain keywords, product listing ads, devices, times of day or locations, it is definitely worth using those engagement metrics as proxy metrics for future revenue.
In a nutshell, those site-side engagement metrics are a valuable source of information when it comes to both mitigating your marketing costs and enriching your data for making more informed decisions.
Now, you might want to compare those findings with what you are seeing in your own accounts, so feel free to share what you find!
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