Interpreting Percentage Changes in Explore Discrepancies
How should I interpret percentage changes in Explore report discrepancies?
When examining discrepancies in Explore reports, it's important to consider both the raw numbers and the percentage changes. A small numerical change can represent a significant percentage change, and vice versa. Understanding these variances helps determine the materiality of the changes. Additionally, consider the number of agents involved, as this can provide context for the changes observed.
More related questions
Why do Explore reports and exports show different ticket creation numbers?
Explore reports and exports might show different ticket creation numbers due to ticket deletions or recoveries. Deleted tickets are removed from the Tickets dataset, which can decrease the total number of tickets created. Conversely, recovering…
How can ticket solve numbers differ between Explore reports and exports?
Ticket solve numbers can differ between Explore reports and exports due to ticket reopens and resolutions over time. When tickets are reopened, they may be removed from the solved metric or moved to a different period. Additionally, if tickets are…
Why might ticket categories differ between Explore reports and exports?
Ticket categories might differ between Explore reports and exports because the Tickets dataset reflects the current state of tickets. If a ticket's category changes between the time of export and when a report is run, the results will differ. For…
What role do aggregators play in Explore report discrepancies?
Aggregators like median (MED) and mean (AVG) can impact Explore report discrepancies, especially for duration metrics like first reply time. The MED aggregator helps protect against outliers, while the AVG aggregator can be skewed by them. For…
Interested indeflectingover 70% of your Zendesk support tickets?
Zendesk Support Tickets
Zendesk Messaging (live chat)
Join1,000+ companies reducing their support costs and freeing up support agents for more important work
“We needed an AI agent integrated within our current tools. My AskAI was the only solution that wasn't going to disrupt our operations.”
Zeffy
“At the end of last year I was given the challenge - how can we provide the same or better service, without hiring anyone?”
Zinc
“My AskAI blew everybody else out of the water. It made the selection process very easy for us.”
Customer.io($50M+ ARR)
“It now resolves 71% of queries (over 35,000 every month), meaning more time solving complex issues and improving UX.”
Freecash
“We needed an AI agent integrated within our current tools. My AskAI was the only solution that wasn't going to disrupt our operations.”
Zeffy
“At the end of last year I was given the challenge - how can we provide the same or better service, without hiring anyone?”
Zinc
“My AskAI blew everybody else out of the water. It made the selection process very easy for us.”
Customer.io($50M+ ARR)
“It now resolves 71% of queries (over 35,000 every month), meaning more time solving complex issues and improving UX.”
Freecash
Reduce support costs.Spend more time on customer success.
