What we would really like is a way of quantifying aggregate user happiness, based on the relevance, speed, and user interface of a system. One part of this is understanding the distribution of people we wish to make happy, and this depends entirely on the setting. For a web search engine, happy search users are those who find what they want. One indirect measure of such users is that they tend to return to the same engine. Measuring the rate of return of users is thus an effective metric, which would of course be more effective if you could also measure how much these users used other search engines. But advertisers are also users of modern web search engines. They are happy if customers click through to their sites and then make purchases. On an eCommerce web site, a user is likely to be wanting to purchase something. Thus, we can measure the time to purchase, or the fraction of searchers who become buyers. On a shopfront web site, perhaps both the user's and the store owner's needs are satisfied if a purchase is made. Nevertheless, in general, we need to decide whether it is the end user's or the eCommerce site owner's happiness that we are trying to optimize. Usually, it is the store owner who is paying us.
For an ``enterprise'' (company, government, or academic) intranet search engine, the relevant metric is more likely to be user productivity: how much time do users spend looking for information that they need. There are also many other practical criteria concerning such matters as information security, which we mentioned in Section 4.6 (page ).
User happiness is elusive to measure, and this is part of why the standard methodology uses the proxy of relevance of search results. The standard direct way to get at user satisfaction is to run user studies, where people engage in tasks, and usually various metrics are measured, the participants are observed, and ethnographic interview techniques are used to get qualitative information on satisfaction. User studies are very useful in system design, but they are time consuming and expensive to do. They are also difficult to do well, and expertise is required to design the studies and to interpret the results. We will not discuss the details of human usability testing here.