Machine Learning – the growing importance and use case for Customer Service
Technology is wielded as a competitive advantage by companies that want to stay ahead of the curve in the consumer space. And customer service is one of the key differentiators for businesses, and technologies such as artificial intelligence and machine learning are dramatically changing the customer experience. Although few things like drone delivery and autonomous vehicles appear challenging in the Indian context, there are other ways in which a customer delight is being achieved.
Machine Learning (ML) is a subset of Artificial Intelligence (AI) and involves computer systems making predictions or decisions without using any instructions, but relying on patterns and inference. Service companies get a high volume of daily requests and they have to depend on technologies like ML to reduce costs and drive a superior customer experience.
Here are some popular applications of ML.
In service delivery, it is important to know process TATs and ML models allow for prediction of specific process TATs that empowers both the provider and the customer with useful information. For example, in industries like food-tech, the ETA or expected time of arrival is predicted with precision using a number of different factors like availability of delivery agents, preparation time, number of orders, traffic and past trends.
Companies that are adopting ML and AI at scale for their inventory management systems are seeing major improvements in their service delivery process. In operations, methodologies such as time series prediction and reinforcement learning systems are being used to re-design the supply chain processes. Consumer goods and tech-driven e-commerce companies are predicting demand across time and geography using this.
With machine learning, a contact center can know caller intent and be ready with help whenever a customer calls for his service request. In the after-sales services industry, ticket allocation engines enable instant allocation of service requests to service agents, basis the agent’s vicinity and performance history. This makes it possible for the right technician, with the right spare part and tools to be present at the right location on time.
A user’s behavior on a website or app can be tracked to make prompts within the session that can drive a sale or provide assistance to the user about the company’s services. A conversation on call, chat or email can also be tracked for specific keywords that trigger assistance along the user’s journey.
Machine learning can be used for predicting failures and taking pre-emptive action, directly impacting the customer experience. For example, if a failure is about to occur in the service delivery process, the system can trigger alerts to agents involved and also to the customer. In after-sales services, failure trends of devices and appliances can be predicted beforehand for planning of inventory and capacity. In e-commerce and logistics, any predicted delay in any leg of the shipment delivery process triggers alerts to the customer about the expected delay.
Each company has a different requirement, and so product and technology teams looking to adopt Machine Learning and Artificial Intelligence should understand their specific requirements and availability of data before undertaking any project. Teams may also require to make significant changes in business process design and take on a Proof of Concept pilot to understand impact on ROI before making any long-term investments.
Article By
Mr Kunal Mahipal, CEO Onsitego
