Technical writers have relied on “lexical search” analytics regarding what keywords have been typed in the search engine on their knowledge base site for analysis. The typical category of analytics includes article performance, search analytics, feedback, and reports for technical writers on their performance.

Analytics helped technical writers enhance content engagement and user journeys to optimize the knowledge base content continuously. This increased the self-service rate and significantly reducing support tickets.

When optimizing knowledge base content using gen AI-powered search, you have to consider two types of analytics: normal keyword search and prompt-based search.

Keyword-Based Analytics Vs. Prompt-Based Analytics

Given the proliferation of GenAI technology, many organizations have deployed ChatGPT-like search on their knowledge base. Customers prefer this over keyword-based search. The tables below show the nature of lexical keyword and prompt-based analytics.

Factors to Consider in Prompt-Based Analytics

Here are the factors you need to look for when enhancing content engagement and user journeys using prompt-based analytics

Prompt Analysis

Technical writers can access the list of questions (prompts) that have been raised by their customers which gives them better clarity on

• What kind of questions that my customers type in
• What are the key business keywords that are often used by your customers, that relate to your business glossary?
• What types of questions are commonly asked, and what types of similar question
• Why are those questions being typed in, and how do they correlate with other business activities
• What information is most commonly been sought

This helps technical writers better understand the customers’ intent, leading to better business outcomes. 

To continue reading about how to optimize content using GenAI-powered search analytics? Click here