Wave Financial achieves 5x ROI and $1.2M annual savings using Ada's chatbot Mave
Wave Financial's customer support team faced seasonal Q1 spikes of 200-300% in inquiry volume. The company managed these with an unsustainable 'all hands on deck' approach—pulling in staff from other departments and having agents work overtime—leading to longer wait times, negative customer interactions, and missed revenue opportunities.
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 triggers bot
Customer inquiries arrive through Wave's support channels, including during seasonal Q1 volume spikes.
Tools used
AdaMaveEngage
Outcome
Wave achieved a 5x return on investment within 12 months, with $1.20 million in estimated savings from inquiry deflections, a 65% reduction in year-over-year support ticket creation within the first month, and a 70% containment rate during the busy season at launch. Over 500 million interactions have been automated in total.
What failed first
Wave's previous approach of deploying all-hands support during peak season—using staff borrowed from other departments and mandatory overtime—was explicitly described as unsustainable as the company grew.