Carry out Root Cause Analysis (RCA) using Freshworks Analytics to move beyond surface-level insights and uncover actionable, fundamental factors to address and enhance employee experiences.
Note: Freddy Insights is currently available as part of Freddy Insights for users with users with a Freddy Co-pilot license.
TABLE OF CONTENTS
Benefits of Root Cause Analysis
- Identify the Underlying Factors
By pinpointing the fundamental root causes, organizations can address the real issues rather than just the symptoms. - Understand Causality
RCA helps determine these relationships, allowing organizations to identify the most influential factors and prioritize them for action. - Enable Problem-Solving
RCA provides the basis for developing targeted strategies, interventions, or solutions to address underlying problems or leverage positive factors driving desirable outcomes. - Improve Decision-Making
RCA enhances data-driven decision-making processes by offering a comprehensive understanding of the factors contributing to specific insights or outcomes. - Enable Proactive Measures
Organizations can be equipped to take proactive measures to prevent the likelihood of similar issues by enabling preventive actions, process improvements, or targeted interventions based on RCA insights.
How to use Freddy Insights?
Access Freddy Insights by clicking the Freddy icon. This opens Freddy insights from where you can dive deep with insights. You can also use prompts to drill down further on your reporting data. Based on your role, Freddy generates insights relevant to you. Tap on the insight to get a deep-dive into the leading root cause. Root causes are also rendered in an intuitive tree chart representation, with each node (box) representing data. Similarly, find a summary that helps interpret the data better. Click on “View all causes" to take a deeper dive into all the other contributing factors.
Here's how you can leverage Freddy Insights to carry out a Root Cause Analysis
Dimension Deep Dive
Analyze metrics through different dimensions to identify those contributing to observed changes. This method helps pinpoint the top contributing dimensions without the need for extensive data slicing and dicing.
Example Insight: "CSAT ratings decreased by 32% on 20th May 2024."
This approach quickly reveals which dimensions are most impacted, allowing for targeted actions and strategies to address specific issues or capitalize on opportunities.
1. Region: CSAT ratings decreased by 28% in the US region on 20th May 2024.
2. Priority: CSAT ratings decreased by 21% for high-priority tickets on 20th May 2024.
3. Channel: CSAT ratings decreased by 30% for email tickets on 20th May 2024.
Example of enabling Root Cause Analysis
User enters the Freddy module and discovers proactive insights on the left bar.
On clicking the insight, the user will see a chart, visually depicting the insight trend.
Following the trend graph is the Root cause Analysis in the form of a summary in simple language followed by a tree chart which explains the leading cause behind the insight.
Users can further drill down deeper and take a look at the other underlying causes by clicking on “View All Causes”
Explanation of example in detail:
In the above example, the insight is about a surge in resolution time compared to last month.
The first node (box) of the tree chart shows the insight which is Average Resolution Time has gone up from 20 hours last month to 60 hours this month, increasing by 40 hours or 200%.
Similarly, it also shows that the Total Ticket Count in the first node has gone up to 4200 tickets, increasing by 1456 tickets compared to last month.
As we examine the linkages in the connected nodes, the surge in the resolution time is related to the Cloud Security Group. Compared to last month, there has been an increase of 60 hours in the Average Resolution Time and an increase of 1680 in total tickets.
On further examining the Cloud Security Group, the increase in Average Resolution Time is seen in two categories:
Hardware Category
Network Category
Hardware Category
For the total of 1460 Tickets belonging to Cloud Security and Hardware Category, the Average Resolution Time is 120 hours, an increase of 60 hours from last month. Most of the tickets belonged to Urgent and Medium priority where the resolution time has increased.
For the Urgent priority tickets of Hardware Category, the First Response Time was breached for 10 Tickets and the Average Resolution Time for those tickets is 300 hours, which is a 200 hour increase compared to the last month.
Network Category
For the total of 280 Tickets belonging to Cloud Security and Network Category, the Average Resolution Time is 20 hours, an increase of 15 hours from last month.
Additionally, the First Response Time is more than 40 hours for 4 tickets, an increase of 2 tickets compared to last month and the Average Resolution Time for those tickets is 190 hours, which is a 100 hour increase compared to the last month.
FAQs for Root Cause Analysis (RCA)
Frequently used terms for Root Cause Analysis (RCA)
- Tree chart: The visual representation for showcasing the interlinkage of causes
- Nodes: Boxes in the tree chart showcasing data, representing the underlying causes
- Critical Path: The leading/most probable underlying root cause
- Summary: A consolidated summary of all the data in different nodes of the tree chart, in simple language
- Where can I view RCA?
RCA can be accessed from the Freddy module in the product. - Is RCA applicable for all types of insights?
RCA is generated for only Proactive Insights and is not applicable for charts generated through Conversational Analytics. - How long does it take to get RCA for an insight?
This depends on the type of insight as follows:? - What is the refresh frequency for RCA?
The refresh rates for RCA depends on the refresh frequency of Proactive Insights. It follows the following pattern:
Note: RCA is pre-computed and data point may vary with current number (based on the above refresh frequency) - What types of fields and metrics is RCA currently applicable for?
- What do the terms “unknown” and “others” mean in RCA?
Unknown: It refers to the empty value in any field
Others: This refers to the multiple values in the data of any field without any significant portion from a particular value - What all tickets are part of the leading cause underlying data?
All the tickets present in the leaf node (first node) of the critical path of the tree chart. - What all tickets are part of the view all causes underlying data?
All the tickets of that day/week/month for which the insight is generated - Can I add or edit any particular field in the treemap for RCA?
No, currently only the fields identified by the AI-model are part of the tree chart. - Is RCA available for all insights?
No, there are insights for which RCA is currently unavailable.
Eg: Insights related to trending employee issues - Does RCA support multi-language?
It does not currently support but is something that we are expected to have for the future roadmap.
You can also check out Proactive Insights and Conversational Analytics from Freddy Insights here