customer_support · energy · workflow
tado° uses Fin AI Agent to maintain CSAT stability through 400% peak-season volume spikes
tado° faced seasonal support volume spikes of up to 400% and a growing, multilingual customer base across six languages. Historically, CSAT scores dropped during the busy winter heating season due to longer wait times, while the team needed to scale without driving up overhead costs.
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 · Proactive at-risk customer guidance
Proactive support capabilities identify areas where customers are at risk of running into difficulty and guide them through product setup.
Tools used
IntercomFin AI AgentWorkflows
Outcome
With Fin AI Agent, tado°'s CSAT scores remained stable and even improved year-over-year during peak season, despite conversation volumes spiking up to 400% and reaching an average of 11,000 customers per week.
What failed first
For years tado° consistently experienced CSAT drops during its busy winter heating season, with longer wait times frustrating customers dealing with heating problems in cold weather.
Results
Time saved11,000 per week
Volumeup to 400%
Running since2019
Grounding & classification
Source type: vendor customer story
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