Loop Earplugs achieves 357% ROI and 25 FTE workload automation with Ada AI agent Aura
Loop Earplugs' rapid growth created overwhelming customer support demands—first response times stretched to 5–6 days during peak periods, ticket backlogs surged past 1,000, and scaling their BPO was resource-intensive with constant training, frequent hiring cycles, and high turnover.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Customer inquiry submitted
Customers submit inquiries via chat, email, or social DMs.
Tools used
AdaAuraAda Academy
Outcome
After launching Aura, Loop achieved a 357% return on investment, improved first response times by 194.52% to a maximum of 2 hours, and had Aura handle the workload equivalent of 25 full-time employees. Even as sales grew 400% over two years, human-agent ticket volume fell by 33% and CSAT reached 80%.
What failed first
Loop's prior support model—a BPO team combined with a scripted chatbot—could not handle the complexity and volume of incoming customer inquiries, especially during peak sales periods.