Next best action recommendations for customer service agents
Companies are increasingly defined by the service they provide to their customers.
Operating in extremely crowded industries, companies such as Amazon and Uber have emerged as market leaders largely thanks to the unrivalled experience that they offer to their customers. And others are following suit with over 70% of company executives labelling customer experience as their number one priority1.
In the pursuit of exceptional customer experiences, companies from large enterprises to small startups are heavily investing in their digital customer touchpoints. Self-service app and web interfaces have enabled customers to access a wider range of options – additional purchases, product repairs or returns and account info changes – without ever needing to speak to a human agent.
However, customers are still landing in the call center. The customer service phone line or live chat option is still seen by customers as the most effective channel, providing a level of certainty that their issue will be solved. And yet, it is also widely viewed as the channel that brings customers the most frustration, while simultaneously providing the highest cost to companies’ customer service departments.
It is unsurprising that the use of data has emerged as a popular choice for companies looking to optimize their customer service operations and improve customer experience. However, in many cases, data is contributing to the problem. Companies are throwing their wealth of data at customer service agents expecting them to make better decisions. In reality, they are overwhelmed.
However, there are a number of ways companies can intelligently inject data into their customer service processes. For example, coupling their wealth of customer information with sophisticated machine learning models, companies can deliver contextual recommendations to customer service agents.
Analyzing individual customers’ data, from account status to recent interactions, in real-time, companies are able to advise agents of the optimal action to take. For example, high-value customers, or those at risk of churn, may receive a higher level of goodwill or compensation for a given issue. This hones decision making without the requirement for agents to interpret a huge amount of customer information during a call or chat session, allowing them to focus on their primary skill of communication.
This approach can deliver exception results. Companies implementing next best action recommendations in their contact centers see revenue gains through increased customer satisfaction and reduced churn. Additionally, empowering customer service agents to take quicker action reduces customer waiting times and peak-time strain on contact centers.
Contiamo is implementing next best action recommendations and a number of other machine learning-backed contact center solutions for a range of large enterprises. To find out how we could help you drive intelligence in your customer service processes, request a demo today.