3 Metrics to Measure Customer Happiness on Your Website

Happy customers make for happy marketers. But how do you measure customer happiness when it comes to the web?

Search marketers are all about metrics – using metrics to guide keyword selection, bidding strategy (for paid search), and to measure performance. Most commonly, online marketers look at keyword, bid price (if relevant), and conversion rate. In fact, SEO professionals are justifiably urged to use SEM metrics on keywords to value them and identify more.

This type of analysis is excellent, but insufficient for a sophisticated online marketer – and even small businesses – to maximize the impact of search on their business growth. By correlating additional metrics to conversion, marketers can refine their content and bidding strategies to get the biggest bang for each word.

There are numerous challenges with relying solely on conversion.

  1. Conversion rate is typically 1 percent implying that the amount of data needed before you can make any decision and have confidence in it, is large.
  2. Conversion analysis is dependent upon attribution methodologies. Online marketers frequently spend considerable time determining the optimal attribution methodology for their efforts. Google Analytics supports six attribution models. The net result being that the attribution model you select materially impacts the way you will value different SEO and SEM initiatives relying solely and directly on conversion. Each method has it’s own biases.

An alternate approach would be to look at metrics beyond conversion rate per keyword. These metrics can be used to determine whether the visitors to your website were “happy” with their experience and if that happiness will likely result in a conversion in this visit or later.

Attribution methodologies are still impactful – they just provide fewer lenses through which to assess your keywords. With this “happiness” approach, you can distinguish between the value of different keywords and SEO strategies by correlating bounce rate, time on site, and product or category page views to conversions and then determine what search initiatives are driving the most visitors who will likely convert at some point.

Some of the commonly used happiness metrics are:

  • Bounce rate: What percentage of the people bounced quickly (had just one page view on your site)? A high bounce rate may imply “unhappy user”.
  • Page views per session: Number of pages a used viewed in a session. Session is typically defined as 30 minutes of no activity by the same user.
  • Time on site: Amount of time spend on your site as measured in seconds.

While these metrics exist, and at high level they may map to user happiness, they don’t always work. We recently analyzed the data across a number of customers in an attempt to understand what kind of information can be extracted from these signals. Some of the findings were quite startling.

We took a sample of the data across a number of web publishers amounting to hundreds of millions of user visits. For each visit, we computed metrics around bounce rate, number of page views, time on site and conversion rate. For the purposes of conversion rate, we used last click attribution methodology.

Once these metrics were computed, we looked at the correlation between each of the happiness metrics and conversion rate. Here are some of the findings:

  • Correlation between bounce rate and conversion rate. High bounce rates in general are a negative online marketing signal – not only have you paid for the visit (in some way), but you can damage your site quality score and domain authority when bounce rates get too poor. But there is an intersection between bounce rates and conversions that an online marketer can use for a more nuanced view of their traffic. Following graph shows the relationship between conversion rate and bounce rate.


While a low bounce rate implies higher likelihood of conversion, after a certain point, it doesn’t matter. While one should always try to minimize bounce rate, unless it is significantly small, it may not have any impact on conversion rate.


Finding the bounce rate per search term in Google Analytics.

  • Correlation between conversion rate and number of page views on the site: It is a common wisdom that the longer a user stays on your website, more like (s)he is to convert. Many marketers use number of page views as a proxy to conversion rate. While this is true in general, our analysis of millions of user interactions revealed a very interesting story. The following graph shows how average conversion rate varies with the number of page views on the site.


This graph shows that a small number of page views is correlated to small conversion rate. As the number of pageviews (which is also a proxy of time spent on site) increases, so does the conversion rate. However, after a while, you see a negative correlation between conversion rate and number of page views.

One hypothesis is that visitors staying for too long are probably lost and need of help and unable to make up their minds. Marketers should specifically analyze the queries of visitors who are spending too much time on the site, and see if they need to change the site navigation, give some promotion, or if there are other ways to help visitors in a way that will result in conversions.

Similar analysis can be done by looking at specific source of traffic (paid, natural, affiliate) or specific page types (product, category, search pages etc.). You should optimize search strategy based on keywords that drive the optimal number of product or category pages views.

For example, at 10 product page views, the probability of conversion rises precipitously – rising about 100 percent from 0-10 page views. From 10 pages views onwards, the conversion average continues to rise, but nowhere near as dramatically – with a stronger impact for sites where there is considerable differentiation between products (such as fashion versus home furnishings).

Mapping your keywords’ performance against conversion, bounce rate, number of product or category page views, and time on site will result in a clear understanding of the relative impact of your head, torso, and tail terms as well as guide your investment in bidding, content, and keyword generation.

Over three years and hundreds of billions of consumer interactions, we’ve identified that these three indicators – bounce rate, time on page, and number of pages visited – correlate to higher visitor happiness, which correlates to more sales and more loyalty.

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