AODC day 1: Turning Search into Find

This week I’m at AODC 2010: The Australasian Online Documentation and Content conference. We’re in Darwin, in the “top end” of Australia. This post is my summary of one of the sessions at the conference. The post is derived from my notes taken during the presentation. All the credit goes to Matthew Ellison, the presenter. The mistakes and omissions are all my own.

Matthew Ellison presented the first session of the conference. He called it “Turning Search into Find”. Tony Self, conference organiser extraordinaire, performed the introduction: “Matthew is from the UK. I must apologise for that”! I guess this gives you an idea of the informal nature of this conference. 🙂

Matthew’s talk covered these topics:

• Why search is important.
• Why search doesn’t always find, and what the obstacles are.
• *Innovative search techniques that clever people are using on the web.
• The top 10 factors that will make your search more effective. Sometimes we have control of or input into the choice of the search tool.
• Some practical pointers towards implementing a good search.

News flash: Matthew can use his phone as a remote clicker to move through his presentation.

AODC day 1 - Matthew talking about "Turning Search into Find"

Why search is important

Matthew pointed out that search is not necessarily the best tool for finding information, but it’s the one that most people want to use. They’re accustomed to using it, from their frequent use of Google and other web searches. Gone are the days when people are accustomed to using the index at the back of a book. Matthew quoted a study where half the people were given a tool with an index at the back and half had the search only. Results showed that the people who used the index were much more effective in finding the information. But when asked, the ones who used the search were more satisfied with the tool.

Many help systems now don’t have an index or table of contents at all. So the search had better be good!

Difference between find and search

Many tools have changed the word “find” to the word “search”. Even Windows did this a while ago. As Matthew said, the difference between the two terms is interesting. It’s a pity we can’t guarantee that people will find the information any more, just that they can search for it!

Matthew asked us to name some problems we may find with search. We came up with these:

• Too many hits.
• Synonyms. Search works well if you use the right word. But if you use the wrong word, you don’t find the information.
• Stop words. The search tool overrides your terms because it thinks your term will return too many results. This means sometimes you can’t find what you’re looking for.
• Complex search parameters, such as quotes, AND, OR etc. These conventions should be common across all searches.
• You can’t ask questions.

Innovation in search techniques

Now Matthew showed us some innovative approaches that may help to improve the situation.

Google Suggest

A new development has appeared in Google search over the last 18 months: “Google Suggest”. Matthew calls it predictive search. As you start to type your search term, Google predicts and suggests what you want.

Personally, Matthew finds this has more impact than Google Wave, even though Google made far less fuss about it.

As you type, predictive search suggests the most common keywords that people have used that match your term. Then you can select the term from the dropdown list.

This reminded Matthew of the old experience of using an index but better, because not only does it give the first match alphabetically, it also gives the most popular match.

In an even more recent development, Google Suggest also takes into account your own recent searches.

Matthew had some fun asking us to guess the Google search suggestions for some phrases. Some of them were:

• “What is” yields “What is my IP address?”
• “How much wo” yields “How much wood would a woodchuck chuck”
• “I like to ta” yields “I like to tape my thumbs to my hands to find out what it’s like to be a dinosaur”

BBCI-Player

Provides an auto-suggest for its search.

Confused.com

Offers a list of choices based on what you type in. There is some synonym matching too. For example, if you type “IT”, it offers a list of jobs starting with “Computer”.

Railsaver.co.uk and British Airways (BA.com)

The dropdown suggestions also give you results where the middle of the word or phrase matches your search term. This is useful where you don’t know the official name of the station or airport.

Back to Google search

Google search does this now too. For example, the term “and bec” will bring up “posh and becks”. Google will also offer you alternative spellings.

Need to balance lots of functionality with ease of use

Many searches require you to understand boolean parameters. You need to know the difference between AND and OR.

Two online bookshops have different ways of balancing ease of use with useful functionality in their searches.

Borders UK (alas, now out of business) had a search that allows you to enter the title, author or ISBN. It used predictive technology. It also categorised the results into groups, showing a group of all the books that match the results, and another group of all the people whose names match.

Blackwells offers a very simple search and also a separate advanced search, where you can fill in a lot of detail.

Faceted search

Faceted search is an alternative to a table of contents. The search classifies information by specific characteristics (facets). People can select what they’re interested in and drill down, in any order, as opposed to a table of contents which presents the information in a specific structure.

Examples:

• http://facetmap.com/browse/ — browsing and selecting wines by type, region, price, etc.
• http://www.sportsshoes.com — a wonderful example of faceted search. Incredibly sophisticated search that works really well. Essential for this type of online vendor.

Matthew introduced the concept of the “scent of information”: If people can see that they’re getting nearer to the information that they’re want, they’re quite happy to keep combining facets to narrow down their search.

What turns “search” into “find”?

Matthew gave some hints about how to make a search as useful and effective as possible:

• “Stop” words let you exclude specific words from the index. This is useful to reduce the number of irrelevant hits. On the other hand, it may cause problems, for example if you want to search for “sort by date” and the word “by” has been excluded.
• More useful is the ability to exclude certain topics from the search. For example, it makes sense to exclude popup topics or context-sensitive topics from the search results.
• The search results should include an extract from the destination page. This is called “synopses” or “context”.
• Boolean search (using AND, OR and NOT) gives the user the power to increase or decrease the number of results returned. Interesting: Google uses an implied AND, whereas most help tools use an implied OR by default. Bear this in mind, that your users may be used to one or the other way of searching. For example:
  • Adobe AIR Help and WebHelp default to OR. Users can explicitly type AND or OR.
  • Same for MadCap WebHelp.
  • ComponentOne NetHelp defaults to OR and does not allow users to enter specific boolean terms.
  • Etc.
• Phrase matching allows users to enter phrases in quotes.
• Fuzzy matching — it would be great if the search knew a bit about linguistics and could offer related words. Google is really good at this sort of thing.
• Faceted search and search filtering. A while ago, Microsoft had the concept of “Information Types”, but this never really came to anything. MadCap Flare’s WebHelp and DotNetHelp do support “concept keywords” and “search filters”.

The techniques we can use in user assistance

Here are some examples of the kind of faceting could we use in user assistance:

• Role (administrator or user)
• Work role (accounts or human resources)
• Experience (beginner, advanced, etc)
• What kind of information do you want? (Step by step, conceptual, etc.)

Ranking, such as by number of occurrences of the key word, or by metadata.

Metadata is the key to flexible and effective search. So the search looks not only at the content, but also at other information that the author has added to the topic. This can help with synonym matching, ranking, etc. RoboHelp 8 has some great tools for adding search keywords manually and for auto-adding index keywords as metadata.

Predictive search is great. This reduces the number of keystrokes the user has to make. There’s no excuse for our help not to use auto-suggest. It provides a better “scent of information”.

Worth thinking about: Predictive search may have a negative aspect, in that it channels us all towards the same search and therefore maybe the same content. This could cut out other content that people may have found by entering less popular search terms.

Matthew’s presentation also contains references to ways of implementing predictive search. For example, Google custom search and technologies such as PredictAd. The latter works in a very similar way to Google Suggest. Matthew spoke to the PredictAd developers and they said there’s no reason it shouldn’t be used for user assistance or documentation.

Adobe Forums used to have an awesome predictive search. (Adobe Forums don’t use this technology any more.) They categorised the search results, similar to the way Blackwells do. It was powered by technology from Jive Software: Clearspace. Matt’s presentation contains a basic specification of how it works. I’m sure he’d send it to you if you’re interested.

During question time, Choco recommended that we look at eBay for a good example of faceted search.

My conclusion

This was a great presentation full of information, fun and interactivity. Thank you Matthew!

Update on 30 May 2010: Matthew’s slides for this presentation, “Turning Search into Find”, are now available for downloading from the Matthew Ellison Consulting web site.