Welcome to the second part of our Deep Dive into Your Performance Metrics, where will break down your reporting even further to show why this metric matters and how you can improve your overall performance and achieve even greater results. Missed part one about Platform Performance reporting? Click here or skip ahead to learn about managing Content Library health.
Let's move into Answer Efficiency.
Why do we measure this?
Answer Efficiency measures how effectively your team leverages AI, library content, and manual responses to complete work. Monitoring these trends can help identify opportunities to improve speed, consistency, and scalability.
Below is an example chart of what you may see in your reporting:
Definitions of what is tracked
- AI-Assisted Answers - AI was used to generate or modify an answer in a project (ex. AI Assistant and AI Draft)
- AI Library Answers - auto-respond or the answer library is used to answer a question in a project
- Manual Answers - a question was answered manually by not using AI or Answer Library
- Average Questions Answered/month - number of questions answered in a period
- Minutes per Question - calculated by determining the total number of minutes in a project divided by the total number of questions answered
We also measure your current metrics against the industry average, shown with up and down triangles and corresponding colors, with green being positive and red showing a decline. This is based on our internal metrics for organizations using Responsive in the same industry classification as your business.
Review these metrics together rather than individually. Increases in AI-assisted and library-based answers often indicate stronger content adoption and improved response efficiency. Manual responses can highlight opportunities to add new content to your library for future reuse.
Find this Data Anytime ~ Reports > Executive Dashboard > Content Library Usage
Suggested actions to take
If you see green indicators, you’re good to go! Keep up the great work.
But what if they are red? The following actions can help:
AI-Assisted Answers & AI Library Answers - These two metrics and how they relate may need some reflection from your team. Take time to reflect on why the usage of Responsive AI tools may be decreasing.
- Are you not receiving high-value responses?
- What situations warrant manual answers?
What can you do now?
Make your prompts more verbose. We use the COAST method which identifies Context, Objective, Audience, Style, and Terms. Not only does using AI lead to stronger content adoption and improved response efficiency, it also gives you more flexibility when using approved answers for different proposal types. This helps your team context-switch and focus on the RFX’s specific ask.
Manual Answers - Manual answers can reflect several things and may not be ‘bad’ per se, but determining this allows you to understand the why behind the data and empowers you to make changes when needed or not. For example, more manual answers may reflect new proposal types that require significantly different or new answers, your new SME has a very different style, and they started over, or you have sunsetted a product and built a completely different product instead. If you are using the same manual answer across multiple projects, consider adding the Q&A to the library.
Average Questions Answered/Month - This is a straight forward metric however it has a lot of powerful opportunities associated with it. When paired with other metrics, this efficiency measure can help you measure projects to answer efficiency, justify new headcount when a certain threshold is met, and even tell you if your RFX’s are getting more complicated over time.
Minutes per Question - calculated by determining the total number of minutes in a project divided by total questions answered.
- Example 1 - ingestion of a RFP takes 20 minutes, 3 answers take 10 min. Your ratio is 30/3 = 10 min
- Example 2 - ingestion of an RFP takes 10 minutes, 3 questions still takes 10 minutes to answer. Your ratio is 20/3 = 6.67 min
Resources
