MPB scales recommerce customer support with Fin AI Agent, resolving up to 10,000 conversations monthly
As MPB expanded into new and existing markets, it needed to handle unpredictable volume surges—reaching 40,000 conversations in peak months—without simply growing headcount. Existing rule-based automation required ongoing maintenance and could not handle the nuance or complexity of MPB's unique customer journeys.
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 contacts via live chat
Fin was introduced as the first point of contact in live chat, handling every inbound conversation.
Tools used
Fin AI AgentIntercomFin GuidanceLLMsCX Score
Outcome
Fin AI Agent now resolves up to 10,000 conversations per month and has doubled its resolution rate from 25–30%, while MPB maintains an 83% Customer Experience Score and delivers multi-lingual support in English, German, French, Dutch, and Italian without hiring native speakers in each market.
What failed first
MPB's prior rule-based Intercom automation helped streamline common queries but required ongoing maintenance effort and could not handle the nuance or complexity of their unique customer journeys.