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SAMBA verbessert den Kundenservice durch KI-gestütztes Qualitätsmanagement

SAMBA verbessert den Kundenservice durch KI-gestütztes Qualitätsmanagement

Automating call evaluation for around 60,000 calls a year, saving hundreds of hours, and helping supervisors coach agents more effectively

SAMBA is a not-for-profit federal employee benefit association serving federal employees, annuitants, and their families with affordable insurance and benefit plans. With more than 200,000 annual calls, a 30-agent customer service team, and strict call-answering SLAs, SAMBA needed to keep improving contact center quality management without adding operational complexity. Download the full case study to see how SAMBA uses cloud-based IVR self-service and AI Quality Management from Enghouse to improve efficiency, expand automated call evaluation, support targeted agent coaching, and maintain reliable service at scale.

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Industrie

Government / insurance

Ziele

Increase efficiency while supporting a growing member base
Meet strict federal and insurer call-answering SLAs
Improve contact center quality management through deeper evaluations, AI call quality monitoring, and more targeted agent coaching

Wichtigste Ergebnisse

Moved from manually evaluating 0.01% of calls to around 30% of 200,000 annual calls, with the flexibility to scale evaluations to 100%
Saved more than 200 supervisor hours per year (equivalent to five full working weeks) on manual call evaluations
Saved 400–500 hours per year by reducing on-site maintenance and day-to-day IT management through a cloud-based solution
Maintained strict SLAs while managing complex call flows and a growing customer base

Customer service at scale for a growing member base

As a small organization with large operational demands, SAMBA needed technology that could support high call volumes without adding complexity. The telephone remains the main way members interact with the organization, so SAMBA needed a platform that could help its small team keep service personal, compliant, and reliable as call volumes and service expectations increased.

Balancing efficiency, empathy, and strict service requirements

With 700–1,000 calls a day and time-sensitive SLAs, SAMBA needed to guide callers quickly to the right queue, support self-service where appropriate, and record interactions for compliance. It also wanted a more complete view of customer conversations so supervisors could identify training needs and coach agents more effectively.

Using AI to evaluate more calls and improve coaching

Enghouse Interactive AI Quality Management helped SAMBA move from manually assessing only 1–2 calls per month to evaluating around 60,000 calls per year. With the option to extend AI-supported evaluation across every customer interaction, SAMBA can continue to scale quality management as its needs evolve. By using AI analytics, transcript search, and sentiment insight, the team can identify coaching opportunities faster and give managers a clearer view of service quality.

Building a long-term platform for reliable, flexible service

SAMBA has worked with Enghouse Interactive for more than a decade, evolving its contact center environment through cloud migration, IVR self-service, and AI-powered quality management. Moving to the cloud also reduced the need for on-site maintenance and day-to-day IT management, helping SAMBA save time while maintaining reliable service. The result is a scalable platform that supports business continuity, reduces management time, and gives SAMBA room to add new capabilities as member needs change.

Now we have introduced AI Quality Management I can’t imagine going back. If a company cares about customer service, then they should care about their employees being trained right. AI makes evaluation quick and easy: it cuts through the static and identifies opportunities for training and improvement.

Bill Martin IT Director
SAMBA

Laden Sie die vollständige Fallstudie herunter und erfahren Sie, wie Enghouse AI Quality Management dieser Stadtverwaltung dabei geholfen hat, die Qualitätsüberwachung auszuweiten, Zeit für die Vorgesetzten einzusparen und die Unterstützung der Mitarbeiter zu verbessern.

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