BSH + Mavenoid

Improved online troubleshooting leads to 70% more customers helping themselves

70% more solved by self-service
16% lessfield-service bookings
85%customer top-ratings

Reduce workloads. Make resources easy to find.

BSH is Europe’s largest manufacturer of home appliances, with brands Bosch, Siemens, Gaggenau and NEFF in the portfolio.

BSH’s call centers were experiencing high workloads and had identified that support requests were often repetitive. A lack of user-friendly self-help options had led to many customers instantly calling customer support as soon as there was a problem with their appliance.

A secondary challenge was that customers often needed to repeat themselves as there wasn’t a smooth transition between self-help and booking a technician.
“The powerful results of our A/B testing show that improving the way we offer troubleshooting to our consumers needs to continue to have a high priority. Mavenoid handles BSH’s internal complexity and quickly adapts to our global needs.”Sebastian Pfahler
Project Lead Contact Center Digitalization, BSH Home Appliances Group

One place for all product questions

Customers no longer have to manually search through articles and manuals for answers. Instead, they can start by simply describing their problem.

Mavenoid helps troubleshoot and solve many issues without the need for a member of the support team to step in. With Mavenoid, BSH has increased the likelihood of a customer solving their own problem by 70%.

For problems that can’t be solved by self-service, Mavenoid assists with booking human service. Technicians get transcripts of what customers have already tried during self-service so they don’t have to repeat themselves.

Mavenoid has initially been launched to support Bosch washing machines in the United Kingdom.
Self-service troubleshooting
Natural language understanding
Smart escalation
Deep product analysis

Key features implemented

AI-guided self-service troubleshooting

Mavenoid helps customers narrow down possible solutions fast with simple diagnostic questions.

Natural language understanding

Advanced semantic search allows customers to explain problems in their own words. The system interprets their intent and guides them to the right content.

Smart escalation

Requests that need a human touch are automatically routed to the right person, along with the case history.

Deep product analysis

Historic support data is used to identify common problems and areas best suited for automation.
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