Retail customer support
OP360 closed 14% more chats in six weeks.
8Flow showed OP360 team leads and QA how their teams actually work across chats and systems, then pointed to the coaching and automation moves worth making.
OP360-run chat support program for a large retailer.
The starting point
Before 8Flow
Nobody could see how people actually worked across chats and systems.
Manual searches, repeated workflows, and inconsistent knowledge-base use were inflating chat times.
QA reviewed chats mostly at random instead of focusing on the ones that needed attention.
The findings
What 8Flow surfaced.
Every finding below comes straight from measured workflow data.
QA signal
QA stopped reviewing at random.
Comparing fast and slow chats flagged 262 conversations worth a closer look, and showed what to fix in each.
- 38% of analyzed chats were flagged for review.
- 70% of flagged chats needed coaching on overlong responses.
- 32% had wrong resolutions or the wrong article.
Automation
Repeat work became an automation queue.
The most frequent manual workflows were measured, ranked, and the first two automated, with no extra lift from the client.
- Manual search patterns visible per workflow.
- The two most frequent workflows automated first.
- Savings measured in hours, not anecdotes.