Service Level Agreement (SLA) Challenges

  • Revenue loss/churn due to missed SLAs 

  • Lack of visibility into customer profile for upsell support 

  • No visibility to service degradation root cause  

  • Low transparency due to static SLA reporting available for enterprises make it harder to backup committed SLAs 

  • Raw data complexity: Presenting raw typical SLA KPIs is difficult to interpret by non-technical resources. Translating this data into actionable insights is challenging. Dependency on network organization for root cause identification 

  • Visibility into real customer experience requires agent installation on the device which is costly, non-viable for a lot of devices (iPhone, IoT devices...)


B-Yond’s AI Solution

Network data driven – not dependent on device-agent data – predictive analytics for SLA forecasting. Recommendation Engine for next best action (highest return and impact).

Key features include:

  • Machine learning (ML)-based root cause analysis (RCA) providing insights for actionable path to resolution 

  • Revenue impact analysis 

  • Increased transparency through dashboards tailored for business, customers, and operations  

Business Benefits

Real-time visibility into actual and forecasted SLAs for compliance and revenue assurance 

Proactive and predictive account maintenance 

Data driven up-sell and cross-sell  

Reduced mean time to resolution/repair (MTTR) with root cause analysis and optimized data communications with operational divisions