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. This is the traditional set of analytics that technical writers use to improve the quality of their knowledge-base content and improve their writing skills to enhance their knowledge-based experience.
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Technical writers with data-centric thinking have pushed their capabilities and learned new skills in terms of continuous quality improvements. Analytics helped technical writers enhance content engagement and user journeys to optimize the knowledge base content continuously. This relentless focus on content quality produced a more significant business impact by increasing the self-service rate and significantly reducing support tickets.
Now, let’s look at the factors you need to consider while optimizing the knowledge base content using analytics from gen AI-powered search. There are two types of analytics when it comes to search: when the user does a normal keyword search for the article and prompt-based search.
Table of Contents
- Keyword-based analytics vs. prompt-based analytics
- Factors to Consider in Prompt-Based Analytics
Keyword-Based Analytics Vs. Prompt-Based Analytics
Given the proliferation of GenAI technology, many organizations have deployed ChatGPT-like search on their knowledge base to answer the questions raised by their customers. Many modern-age customers prefer ChatGPT-like search compared to traditional Google-like search based on keywords. The analytics involved in analyzing “prompts/questions” which have different characteristics compared to keywords. The tables below show the nature of lexical keyword and prompt-based analytics
|
Characteristics |
Keyword-based analytics |
Prompt-based analytics |
|
Search query |
Keywords |
Prompts |
|
Length of query |
Shorter |
Longer |
|
Context present |
No |
Yes |
|
Query aggregation |
Grouping by keywords |
Not applicable |
|
Relationship with another query |
No |
Yes |
|
Types of Analytics |
1. Search analytics 2. No-result search analytics |
1. Topical analytics 2. Unanswered question analytics |
|
Analytics outcomes |
Know the types of keywords that are used to look for information |
Address knowledge gaps |
|
Customer intent is known for query |
No |
Yes |
Also Read: Exploring AI Search Analytics: Unveiling Trends & Patterns
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.
Topical Analysis
Topical analysis refers to the process of analyzing questions (prompts) to determine the topics they cover. This allows technical writers to get into the general themes customers are most frequently searching for. This analysis can be used to identify trends, understand customer needs, and guide content creation or product improvements.
- Holistic topical analysis
Analysis of all questions leads to knowing what questions are trending and helps to delve into “why”. These topics can be correlated with any recent product launches, any changes to products or services, and so on. Introducing a time dimension to this topical analysis offers a perspective on trends and patterns in how your customers utilize your knowledge base.
- “Unanswered questions” topical analysis
If you can filter out only those questions (prompts)where the GenAI-based assistive search could not respond because of lack of information or context, then undertaking topical analysis on these “unanswered questions” leads to creating new knowledge base content. This analysis will reveal the new information your customers are seeking but that you currently lack content for.
Citation Analysis
Usually, source articles that are used to create a response via GenAI are displayed as citations. These citations provide a trust factor to the customers. If citation articles for each response can be analyzed holistically, then we can understand the high-value content of your knowledge base. This analysis helps technical writers identify critical articles of the knowledge base and take steps to ensure those articles are always up to date. Stale content in the knowledge base can also be identified in the knowledge base using this analysis.
Usage Analysis
Search volume along with positive and negative feedback sliced across different time dimensions shows the usage statistics of your GenAI assistive search tool. Successful responses and “unanswered question” responses show the adoption of the GenAI assistive search tool as a primary means to interact with your knowledge base. The list of all questions, along with its feedback, can also be made available to technical writers if they choose to dive into individual questions over time!
Conclusion
It is high time for technical writers to learn new skills in evaluating responses to the GenAI-based assistive search capability. Additionally, technical writers need to upskill their data literacy skills and have an analytical mindset to understand trends in search analytics to add more value to their customers by producing trustworthy content. The GenAI-powered search analytics reveal the customer’s intent to the technical writer and help technical writers produce more engaging content for better comprehension.
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