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.
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.
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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.
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Frequently asked questions
What did this team achieve with this AI workflow?
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% c…
What tools did this team use?
Ada, Mave, Engage.
What results were reported?
Year-over-year support ticket creation reduction (first month): 65%; Interaction containment rate during busy season at launch: 70%; inquiry deflection savings (March 2020 – March 2021): $1.20 million; Q1 customer support volume spike: upwards of 200-300% (source-reported, not independently verified).
What failed first in this deployment?
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.
How is this customer support AI workflow structured?
Customer inquiry triggers bot → Mave handles FAQs 24/7 → Tier- and region-based routing → Complex inquiries to human agents.
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