You Have Intelligent Search. So, What Do Your Users Think?

By Ed Downs, Sinequa

There’s so much more to the word “content” than how we use it in the technology and knowledge management (KM) fields. In the corporate world, content refers to documents, news sites, and customer conversations in multiple media and social network discussions. That’s content as a noun. But content is also an adjective, as in “are you content with your meal?” And con- tent is a verb, as in “to content yourself with your meal.”

Content analytics brings these three forms of the word together. It’s about figuring out if your employees are content to content themselves with your content. This article briefly explores content analytics as it is used in enterprise search. It looks at how it benefits an organization that has deployed enterprise search in the pursuit of better KM and provides best practices for adopting usage analytics.

Content analytics comprises a “family of technologies,” according to Gartner, “that processes digital content and user behaviour in consuming and engaging with content.” In other words, content analytics is about how users interact with content. For this reason, some refer to the category as “user or usage analytics.” Here, we’ll refer to it as “content usage analytics.”

We see content usage analytics out in the open in consumer search. For instance, we can find the most popular search term on Google (It’s “Facebook,” if you’re curious). We can find out if “Taylor Swift” searches are trending past searches for “Kim Kardashian,” and so forth. These metrics show how users engage with content, in this case, websites for celebrities.

An Example of Content Usage Analytics in Action

In the corporate setting, content usage analytics is comparable to Swift versus Kardashian. Only it’s about finding relevant domain expertise and available information. A content usage analytics tool, such as Sinequa’s Usage Analytics, provides employees with data about the top search queries in the enterprise and accompanying data points.

For example, a content usage analytics tool could tell you that your enterprise’s most popular search query for the last month was “How do I file an expense report?”

For that query, the tool could also use data visualization widgets to tell you how many employees clicked on each document shown in the search results and how many users didn’t click on any documents. It would show that employees most frequently clicked on a document called “Expense report policies.” It could show how many employees exited the search at that point.

The content usage analytics tool can also report data on the overall usage of the enterprise search platform. Data points in this context might include the search engine’s bounce rate, the number of new users in a given time period, how many searches were refined once results were returned, and more.

This type of analysis and reporting is almost always available with search tools but may not be available in other data analytics systems. Business intelligence (BI) and visualization tools that are not part of search engines often do not offer as much understanding of usage and user behavior.

Why Does Content Usage Analytics Matter?

Content usage analytics is a critical part of an intelligent search program. Knowing how many people are using a search tool can help justify the investment in search technology. Or, if the numbers are not impressive, content usage analytics can contribute to a dialogue about getting more employees to adopt search for their work.

Content usage analytics adds data to help an enterprise improve at KM. After all, what could be more important to a KM initiative than some facts about what knowledge employees seek? The process could be an eye opener, especially if a KM project was launched with assumptions about what kind of knowledge mattered most to the organization. Content analytics will reveal what people care most about.

The relevance of intelligent search will also improve when content usage analytics are applied to tuning search results. For example, if the query “How do I file an expense report?” is getting a high number of exits, or if the top documents returned in search results are being ignored.

Content usage analytics will tell you how well the enterprise search tool has indexed the content relevant to this search phrase. As relevance improves, employee productivity (and satisfaction) should go up.

The content usage analytics process might also help the organization develop better content. A lot of effort and expense goes into creating content. If content analytics shows that expensive, carefully produced content is being ignored or underutilized, that is a valuable insight to use when thinking about refreshing content.

Best Practices in Content Analytics

As organizations adopt content usage analytics, best practices are emerging to facilitate the best possible outcomes. For instance, defining usage analytics requirements for each search-based project makes sense. That might mean setting key performance indicators (KPIs) for bounce rate, new employees added, and so forth. From there, another best practice is to set up regular monitoring of usage analytics. Having the usage data is only a first step in making an intelligent search initiative successful. Usage data should drive action, e.g., evaluating search relevance and executing a plan to improve relevance for the most common search terms.

Content usage analytics is a key success factor in implementing intelligent search as part of a broader KM initiative. It provides vital feedback on how well KM is doing and shows where there is room for improvement. When content usage analytics are leveraged, knowledge managers and search administrators can tune search results and make the search experience more relevant to KM and the overall goal of increasing organizational effectiveness and employee satisfaction.