Quick Website Diagnostics with Search Analytics

I have recently been giving courses directed to web editors on how to successfully apply search technology on a public web site. One of the things we stress is how to use search analytics as a source of user feedback. Search analytics is like performing a medical checkup. Just as physicians inspect patients in search of maladious symptoms, we want to be able to inspect a website in search of problems hampering user experience. When such symptoms are discovered a reasonable resolution is prescribed.

Search analytics is a vast field but as usual a few tips and tricks will take you a long way. I will describe three basic analysis steps to get you started. Search usage on public websites can be collected and inspected using an array of analytics toolkits, for example Google Analytics.

How many users are using search?

For starters, have a look at how many of your users are actually using search. Obviously having a large portion of users doing so means that search is becoming very important to your business. A simple conclusion stemming from such evidence is that search simply has to work satisfactorily, otherwise a large portion of your users are getting disappointed.

Having many searchers also raises some questions. Are users using search because they want to or because they are forced to, because of tricky site navigation for example? If you feel that the latter seems reasonable you may find that as you improve site navigation your number of searchers will decrease while overall traffic hopefully increases.

Just as with high numbers, low numbers can be ambiguous. Low scores especially coupled with a good amount of overall site traffic may mean that users don’t need search in order to find what they are looking for. On the other hand it may mean that users haven’t found the search box yet, or that the search tool is simply too complicated for the average user.

Aside from the business, knowing how popular search is can be beneficial to you personally. It’s a great feeling to know that you are responsible for one of the most used subsystems of your site. Rub it in the face of your colleague!

From where are searches being initiated?

One of the first recommendations you will get when implementing a search engine for your web site is to include the search box on each and every page, preferably in a standardized easy-to-find place like the top right corner. The point of having the search box available wherever your users happen to be is to enable them to search, typically after they have failed to find what they are looking for through browsing.

Now that we know that search is being conducted everywhere, we should be keeping an eye out for pages that frequently emit searches. Knowing what those pages are will let us improve the user experience by altering or completing the information there.

Which are the most common queries?

The most frequently issued queries to a search system make up a significant amount of the total number of served queries. These are known as head queries. By improving the quality of search for head queries you can offer a better search experience to a large amount of users.

A simple but effective way of working with search tuning is this. For each of the 10, 20 or 50 most frequent queries to the system:

  1. Imagine what the user was looking for when typing that query
  2. Perform that query yourself
  3. Examine the 5-10 top results in the result list:
    • Do you think that the user was content with those results
    • If yes, pat your back 🙂
    • If not, tweak using synonyms or best bets.

Go through this at least once a month. If the information on your site is static you might not need to change a lot of things every time, but if your content is changing or the behavior of the users you may need to adjust a few things.

Query Suggestions Help Users Get Unstuck

Several papers at the HCIR09 workshop touched on the topic of query suggestions. Chirag Shah and Gary Marchionini presented a poster about query reuse in exploratory search tasks and Diane Kelly presented results from two different studies that examined people’s use of query suggestions and how usage varied depending on topic difficulty. (Their papers are available for download as part of the proceedings from the workshop.)

According to Shah and Marchionini users often search for the same things. They reuse their previous queries e.g. search for the same things multiple times. Users use their previous searches to refind information and also to expand or further filter their previous searches by adding one or more keywords. There is also a significant overlap between what different users search for suggesting that users have a tendency to express their information needs in similar ways. These results support the idea that query suggestions can be used to help users formulate their query.  Yahoo and YouTube  are two of the systems that uses this technique, where users get suggestions of queries and how they can add more words to their query based on what other users have searched for.

Diane Kelly concludes that users use query suggestion both by typing in the same thing as shown in the suggestion and by clicking on it. Users also tend to use more query suggestions when searching for difficult topics. Query suggestions help users get “unstuck” when they are searching for information.  It is however hard to know whether query suggestions actually return better results. The users expectation and preferences do have an effect on user satisfaction as well. User generated query suggestions are also found to be better than query suggestions generated by the search system. So the mere expectation that the query suggestions will help a user could have an positive effect on his or hers experience…

Query suggestions are meant to help the users formulate a good query that will provide them with relevant results. Query suggestions can also work as with yahoo search where query suggestions both suggest more words to add to the query but also provides the users with suggestions for other related concepts to search for. So searching for Britney Spears will for example suggest the related search for Kevin Federline (even though they are now divorced) and searching for enterprise search will suggest concepts such as relevance, information management and off course the names of the different search vendors.

If you apply this to the enterprise search setting the query suggestion could provide the user with several different kinds of help. Combining the user’s previous searches with things other users searched for but also providing suggestions for recommended queries or concepts. The concepts will be high quality information and suggestions controlled by the team managing the search application. It is a way of combining quick links or best bets with query suggestions and a way to hopefully improve the experienced value of the query suggestions. The next step then is to work with these common queries that users search for and make sure that they return relevant results, but that is an entirely different topic…

Implement Findability in Your Customer Service Interactions

With the rapid rate of change in the global economy, the need for customer knowledge and predictive insight has never been more urgent. The competition is increasing as well as the demand for cost reduction, so whether you are a company fighting for business or a public entity serving the citizens, there is a great deal to gain by introducing Findability on your website.

Using the power of an enterprise search platform to serve your Internet site enables you to take your online service offering to the next level. Due to the “Google-effect”, users have become used to accessing information via a single search box as opposed to “surfing around” to find what they are looking for. A good search system enables your site users to start their journey through your site from the single search box. Accompanied by extreme relevance and navigational tools, users find the information they are looking for with a minimum number of clicks.
Online presence has become a must for companies with a large customer base. With consumers constantly developing a higher degree of online literacy, they expect a higher degree of online service from their vendors— including easy-to-find information and other services such as stock trading and banking facilities. You can easily offer your customers a unified view on your services and information—even if they originate from different source systems—due to the search system’s ability to act as a universal Findability layer.

An increased online service offering will also drive self-service behavior from the user side. By using Search Analytics on the query/search logs you will get a wealth of information about customer behavior. Take customer support as an example. By publishing the most requested support information on your public site, and enabling the users to easily find the information they are looking for, the need for call center support is lowered. This reduces the pressure on the basic customer service functions, allowing you to refocus resources to other value creating activities.

For many enterprises, self service is seen as the solution that can provide customers with the support they need while significantly reducing service costs.

Self-service is regarded as an opportunity to sharply lower customer support costs by deflecting calls. For example, respondents to a Fortune 1,000 survey expect to offload 23% of their call volume to Internet-based self-service (Mastering Online Customer Service, Bruce D. Temkin, Bob Chatham, Hillary Drohan, Katharine M. Gardiner, Forrester, July 2002). And there are proven cost justifications for implementing self-service: Web-based self-service interactions cost 75% less than a phone interaction.

While more traditional customer service interaction solutions tend to be based on a knowledge data base, that needs to be built and maintained, a Findability based solution is more dynamic in its nature and is based on a dynamical index created by the already existing data that resides in the corporate systems. The index can be partitioned into information buckets meeting different user needs and profiles.
So implement Findability in your customer service interactions!