We need a taxonomy of web user engagement
There are lots lots and lots of metrics that can be used to assess how users engage with a website. Widely used ones by the web-analytics community are click-through rates, number of page views, time spent on a website, how often users return to a site, number of users.
Although these metrics cannot explicitly explain why users engage with a site, they can act as proxy for online user engagement: two millions of users accessing a website daily is a strong indication of a high engagement with that site.
Metrics, metrics and metrics
There are three main types of web-analytics metrics:
- Popularity metrics measure how much a website is used (for example, by counting the total number of users on the site in a week). The higher the number, the more popular the website.
- How a website is used when visited is measured with activity metrics, for example, the average number of clicks per visit across all users.
- Loyalty metrics are concerned with how often users return to a website. An example is the return rate, calculated as the average number of times users visited a website within a month.
Loyalty and popularity metrics can be calculated on a daily, weekly or monthly basis. Activity metrics are calculated at visit level.
So one would think that a highly engaging website is one with a high number of visits (very popular), where users spend lots of time and click often (lots of activity), and return frequently (high loyalty). But not all websites, whether popular or not, have both active and loyal users.
This does not mean that user engagement on such websites is lower; it is simply different.
What did we do?
We collected one-month browsing data from an anonymized sample of approximately 2M users. For 80 websites, encompassing a diverse set of services such as news, weather, movies, mail, we calculated the average values of the following eight metrics:
- Popularity metrics: number of distinct users, number of visits, and number of clicks (also called page views) for that month.
- Activity metrics: average number of page views per visit and average time per visit (also called dwell time).
- Loyalty metrics: number of days a user visited the site, number of times a user visited the site, and average time a user spend on the site, for that month.
Websites differ widely in terms of their engagement
Some websites are very popular (for example, news portals) whereas others are visited by small groups of users (lots of specific-interest websites were this way). Visit activity also depends on the websites. For instance, search sites tend to have a much shorter dwell time than sites related to entertainment (where people play games). Loyalty per website differed as well. Media (news, magazines) and communication (messenger, mail) sites have many users returning to them much more regularly, than sites containing information of temporary interests (e-commerce site selling cars). Loyalty is also influenced by the frequency in which new content is published. Indeed, some sites produce new content once per week.
High popularity did not entail high activity. Many site have many users spending little time on them. A good example is of a search site, where users come, submit a query, get the result, and if satisfied, leave the site.
This results in a low dwell time even though user expectations were entirely met.
The same holds for a site on Q&A, or a weather site. What matters for such sites is their popularity.
Any patterns? Yes …
To identify engagement patterns, we grouped the 80 sites using clustering approaches applied to the eight engagement metrics. We also extracted for each group which metrics and their values (whether high or low) were specific to that group. This process generated five groups with clear engagement patterns, and a sixth group with none:
- Sites where the main factor was their high popularity (for example as measured by the high numbers of users). Examples of sites following this pattern include media sites providing daily news and search sites. Those are sites where users interact in various ways with them; what is common is that they are used by many users.
- Sites with low popularity, for instance having a low number of visits. Many interest-specific sites followed this pattern. Those sites center around niche topics or services, which do not attract a large number of users.
- Sites with a high number of clicks per visit. This pattern was followed by e-commerce and configuration (accessed by users to update their profiles for example) sites, where the main activity is to click.
- Sites with high dwell time and low clicks per visit, and with low loyalty. This pattern was followed by domain-specific media sites of periodic nature (new content published on a weekly basis), which are therefore not often accessed. However when accessed, users spend more time to consume their content. The design of such sites (compared to mainstream media sites) leads to such type of engagement, since new content was typically published on their homepage. Thus users are not enticed to reach (if any) additional content.
- Sites with high loyalty, small dwell time and few clicks. This pattern was followed by navigational sites (the front page of an Internet company), which role is to direct users to interesting content or service in other sites (of that same company); what matters is that users come regularly to them.
This simple study (80 sites and 8 metrics) identified several patterns of user engagement.
However, sites of the same type do not necessarily follow the same engagement pattern.
For instance, not all mainstream media sites followed the first pattern (high popularity). It is likely that, among others, the structure of the site has an effect.
… So what now?
We must study way more sites and include lots more engagement metrics. This is the only way to build, if we want, and we should, a taxonomy of web user engagement. With a taxonomy, we will know the best metrics to measure engagement on a site.
Counting clicks may be totally useless for some sites. But if not, and the number of clicks is for instance way too low, knowing which engagement pattern a site follows helps making the appropriate changes to the site.
This work was done in collaboration with Janette Lehmann, Elad Yom-Tov and Georges Dupret. More details about the study can be found in Models of User Engagement, a paper presented at the 20th conference on User Modeling, Adaptation, and Personalization (UMAP), 2012.
Photo credits: Denis Vrublevski and matt hutchinson (Creative Commons BY).