Search engine behaviour
Age matters when searching. Young people, for example, have 7 types of browsing behaviour:
- Power users tend to make use of advanced search features.
- Developing users display unplanned search paths
- Social users share links with friends using social media accounts.
- Specific users focus on specified topics of interest.
- Rule-bound users are influenced by rules, for example, trust and are likely to revisit specified sites over and over again.
- Visual users love pictures and videos to find information.
- Nonmotivated users only search when necessary. Emotions are low, for example, excitement and contentness.
We generally read SERP results in an F shape. Each SERP result snippet’s title tag (top part of the F shape) is read first followed by the URL and the description tag. More broadly we also scan sites using an F shaped pattern so top left to top right, middle left to the middle of the screen, then top left right down to the bottom left. This F shaped pattern is a dominate behaviour across a lot of digital products. Put your navigation menu, for instance, on the left hand side or along the top of your screen to increase its visibility.
User behaviour analysis
Young, or more specifically inexperienced, search engine users place more weight on SERP cues, for instance, keywords being highlighted in bold than their older counterparts. Age however does not determine how good of a search engine user you are (i.e. a 50 year old is not a better searcher than a 25 year old).
If the search engine user knows the topic they devote more time to analyse the document’s contents. This directly impacts keyword formulation, as such, topic knowledge means we are likely to search this topic more than others. So if someone is interested in fashion, for example, they are likely to search a lot of fashion-based web sites because they know a lot of keywords related to fashion (the topic).
Title pages alter SERP scanning and click-through rates. If an exact match has been returned then this search engine user is also likely to click on this document because it directly matches their query. It is, however, worth noting that most search engine users are not good at formulating keywords in the first instance. This is one of the main limitations of web search: Human beings are complex computer users.
When we examine search engine results we constantly try to make sense of the results. Although on most occassions we tend to make the correct relevant judgement at times our quick scanning can create a false positive. We do sometimes do not click on some results because our brain has literally processed that specific link as being irrelevant or, conversely, useful.
When we look at web search engine results we also integrate our own lives in to that search. As our “real life” changes dramatically so does our search behaviour this, therefore, explains why we sometimes carry out more in depth searches than others (i.e. our search behaviour changes with our environment).
Returning users practice “selective disregard” when it comes to toolbars, search and menus. This localised learning allows our brain to ignore specific regions of a site but we know they are there. This partly explains why organic traffic is higher than pay per click (PPC): Some people have selective disregard for PPC and therefore mostly look at organic traffic.
The number of tabs opened also influences user behaviour, in particular, initial scanning of web sites. The more tabs we have open the less concentrated we become. Tabs also influence user selection behaviour, for example, clicks which can cause us to jump around from site to site, or, as Peter Morville calls this, in web search, pogosticking between the SERP and individual results (back and forth).
Posted by Gerald Murphy
- Nielsen, J. Pernice, K. and Teague, J.C. (2010) Q&A with Jakob Nielsen and Kara Pernice: An Interview by Jason Cranford Teague. [Online] [Accessed on 10th February 2014]
- Kuhlthau, C.C. (1991) Inside the search process: Information seeking from the user’s perspective. Journal of the American Society for Information Science. 42(5) pp. 361–371