Service delivery operations are vital for the success of Digital Service Providers (DSPs). However, most DSPs struggle with the conventional service delivery process leading to high customer churn and reduced NPS. Based on the engagements with various DSPs, we identified the common challenges that the conventional enterprise service delivery process faces as below:
These challenges lead to a 50-60% reduced number of order completions per month, thus resulting in dissatisfied customers. Hence, DSPs need to embrace AI/ML techniques in their enterprise service delivery operations to reduce cycle time by 30% and increase the order completion rate by 2x. Implementing an ML model right at the beginning of the service delivery process assists the DSPs to left shift the operations and gain control of the entire order journey.
Fig 1. AI/ML-powered predictive service delivery operations
In this article, we have detailed a four-step process that the DSPs can adopt to incorporate predictive capabilities and accelerate their service delivery process.
Identifying the build effort type is primary in predicting the accurate order completion date and accelerating the service delivery process. Once the order flows in, classify the required engineering build effort into one of the following- no build, small, complex, and special, based on the historic site survey data. This can be achieved by using Gradient Boosting Classifier and NLP packages like Fuzzy and Genism. Based on the predicted build effort type, the various alerts/reports can be sent to the operations team to accelerate the process and complete the orders within the customer commit date.
Once the engineering build effort type is predicted, it can be fed to the ML model along with other order details like address and service type. The ML model can use these inputs to analyze the granular address details and forecast the order delays. The milestone SLAs and order completion date can be dynamically recalculated based on the order delays.
Fig 2. ML model for prediction of order delays and order journey milestones
While the DSPs find ways to provide efficient service delivery by dynamically predicting the milestone SLAs of the order journey, understanding the customer emotions, and prioritizing the orders also plays a vital role in reducing customer churn. Since the customer cases can be from a variety of sources that vary in format, collate them in a common database and feed them to the ML model. Schedule the model to work on the customer cases periodically, extract the latest cases and predict the sentiment. Based on the predicted emotions, the high-priority unsatisfied customer reports can be sent for the operations team to re-prioritize and accelerate the orders.
Build a digital experience dashboard such that it provides the ability to track all in-flight orders and focus on numerous KPIs. It is possible to develop unique reports on the milestone buckets to provide real-time and in-depth visibility into the key steps along the order journey. The end-to-end dashboard boosts the entire service delivery process and reduces the DSPs’ effort by 80%.
By implementing predictive capabilities in enterprise service delivery operations, as explained in this article, a leading DSP in North America gained control of the entire order journey. They also experienced the following benefits:
I thank Senthil Murugan – Sr. Technical Lead, Mohana Priya – Software Engineer, and Priyankaa A from Strategic Insights for their contributions in shaping up this article.
By Boominathan Shanmugam