conducting an eye exam with an eye chart
PHOTO: National Eye Institute, National Institutes of Health

Three minutes. That's how long it takes before someone complains how awful search is during one of the employee focus groups I run for clients on their digital workplaces. It comes up even if it isn't what I asked about. Everyone grumbles and empathizes and the conclusion is invariably “it should just work like Google.”

Trust me, it will never work like Google. For an intranet or digital workplace manager, it is tempting to blame the search engine or feel it is something for IT to solve. Trust me, that will never work either.

What I want to share here is a diagnostic tool that breaks down the underlying causes of search failure. Many of the elements that cause failure are ones intranet managers, content owners, knowledge managers and even IT professionals can improve without changing the search engine. 

New research by Paul H. Cleverly and Simon Burnett attribute 62 percent of enterprise search dissatisfaction to non-technical factors: information quality and search literacy.

This isn't to say you should ignore your search engine, but for many organizations search expertise can be hard to find, so people end up doing nothing.

Related Article: 6 Ways Enterprise Search Is Nothing Like Google Search

Searching, Step by Step

To be more precise, we should talk about findability rather than search. This is because search is often a combination of searching and browsing. For example, a user might navigate to the HR section and then do a search within that sub-site, or they might search for "policies" and then navigate to "HR policies" in a policies center.

sample enterprise search
[Figure 1] A simple decomposition of enterprise search

We can break down the search process into four basic steps:

  1. Content is published.
  2. The search engine indexes it.
  3. A query retrieves a selection from the content.
  4. The user selects from the search results.

This greatly simplifies what happens, but from a diagnostic point of view it gives us four useful starting points for things that might go wrong.

A Search Diagnostic Tool

For each step in the process, there are things that need to go right, such as metadata, security settings and results presentation (see column three in Figure 2) and then underlying symptoms (the last two columns). Note all the ones that aren’t colored green are not primarily a technical issue!

It’s impractical to go through the diagnostic for all the content in your digital workplace. Instead I suggest you use the tool to check for systemic issues that might broadly apply to sets of content when you get feedback that “search isn’t working.”

Employee satisfaction surveys often rate search poorly. Using focus groups to dig deeper into what’s happening can help here: “Can you remember a recent time you tried to search for something? How did you search? Did it exist at all?”

enterprise search diagnostic
[Figure 2] An enterprise search diagnostic

Common Reasons Search Fails

Content Failures

It may sound obvious, but the big issue in digital workplace search is often that the thing somebody is searching for just doesn’t exist [1.1]. On the web, somebody, somewhere probably has put the answer there, but in the enterprise this isn’t necessarily true. So if a question is frequently asked, the solution might just be to get someone to write the answer (there’s a diagnostic tool for that too: Clear Knowledge Management Roadblocks).

Metadata [1.2] can often be poor or lacking. Just using good writing principles for headlines and subheads can help, as can clear filenames (if you ever shared a document called “Proposal draft” or “Announcement,” I’m looking at you).

Language [1.3] can also present a barrier. A technical document may be written in jargon (“variable performance-related pay”) when a user searches in plain English (“bonus”). Even harder, we may expect everything to be in our language and overlook other languages (“2016 sales results for Spain” wouldn’t necessarily find a document called “Resultados de ventas de Espana 2016”).

Related Article: Your Intranet Is Only as Good as Your Metadata

Indexing Failures

Search retrieval works so quickly because a crawler creates an index first, and your query is actually run against the index. So the first failure point here [2.1] is that the content needed isn’t indexed. Unlike the web, a great deal of enterprise content might have security controls in place, blocking the indexer from seeing it.

More fundamentally, it may exist in a system the crawler can’t access, such as a network drive or an application. I sometimes see HR departments move all their guidelines into an employee self-service system, but if there is no connector with the enterprise search engine then routine content like “Parental leave policy” won’t get indexed. Nor will all those documents in Dropbox if it’s only shadow IT.

Next we need to consider the index itself [2.2]. This is definitely in the technical realm, but check to be sure document content is indexed and not just the title. You may also need to define words that are specifically meaningful to your organization. For example, if you have a product called “Teams,” then the indexer needs to know it is more significant than casual usages of "teams."

Related Article: Revisit These Search Fundamentals

Retrieval Failures

Largely we rely on the search engine technology to get this right [3.1], and do all the good stuff like sensible ranking and knowing that “bicycle” and “bike” are the same. Martin White has a useful summary of 10 options for enhancing search engines.

However, too many results can be a symptom of duplicate content or ROT (Redundant, Outdated, Trivial), meaning a clean-up is in order. It may also mean we don’t have good refiners, to whittle down results to the last six months, or only show sales collateral (see Metadata [1.2]).

Retrieval also relies on user search skills though. Google is so good we’ve got lazy. But enterprise search sometimes needs very good search skills, such as the use of logical operators (AND, OR, NOT). If that’s unrealistic, consider ready-made search interfaces.

Related Article: Relevance Engineer: A New Profession in Search of Candidates

Search results

Finally we get to the results page (I know, I know, Google gets there in about 0.47 seconds).

You’d think if the answer was on the page we’d be successful, but if you’ve ever done observational user testing you’ll know that sometimes people seem fly straight past the answer and onto the phone.

So the layout of the results page matters [4.1]. The good news is you can often change it. Usually, the more like Google, the better, as this is what people have already learned. Make it so that the format matches the results [4.2]: show images and videos as thumbnails, people as a contact card and, heck, even just show the answer itself rather than a link.

Hits on documents can make scanning of the results harder [4.4]. If the answer is on page 52 of a document, consider breaking it into HTML pages. If the document exists but isn’t shown, ask if the security settings on it are right [4.3].

Finally, users may find the right result, but carry on searching because they don’t trust it [4.5]. Governance and training can help here — make sure it has things like owner and expiry details. Ratings and feedback can help too.

This post was partly inspired by an old LinkedIn thread, which Paul Culmsee analysed in forensic detail on CleverWorkarounds.

My thanks to Martin White for commenting on an earlier version of the model. For a much more detailed analysis of how search works, I definitely recommend the Search Insights 2018 whitepaper.

(Icons designed by smashicons from flaticon)

I plan to keep refining this tool, so any comments or questions would be most welcome.