Service delivery operations are vital for the success of Digital Service Providers (DSPs), and most of the DSPs, struggle with conventional processes which leads to customer churn and reduction in NPS. The rising complexities in the enterprise service delivery process exponentially increases the challenges in fulfilling orders as shown below:
- Multiple siloed systems and manual handoffs increasing the chances of human errors
- Dependency on third party processes and SLAs for order completion
- Lack of strategies to forecast the order delays
- Lack of mechanisms to track milestones and the entire service delivery flow in real-time
To overcome these challenges and provide efficient service delivery within the committed date, DSPs need to embrace AI/ML techniques and bring in predictive capabilities within their enterprise service delivery operations. Leveraging an AI-powered predictive service delivery framework enables proactive order fallout management, prediction of milestone SLAs, and dynamic order delivery recalculation. This, in turn, helps the DSPs to accelerate the orders and boost order completions by 2x.
Download this insight to know more about:
- How DSPs can implement an AI-powered predictive service delivery framework to accelerate the service delivery process and reduce customer churn
- The best practices to build an ML model for prediction of the engineering build efforts, potential delays, dynamic order journey, and prioritization of orders based on customer emotions
Business benefits achieved by a leading DSP in North America after successfully implementing AI-powered predictive service delivery framework – 30-40% acceleration of orders and interval reduction resulting in increased order completion by 2x.
- Senthil Murugan
- Mohana Priya
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