One window, one screen: The path to agent productivity
10
Tips for omnichannel and digital assistant success
•
Enterprises will need a well-defined process automation roadmap. They should plot the most significant areas of the business that are suited for automation and assign a focus group of business process owners to discuss and prepare a future blueprint with strategic objectives in mind. Also, ensure support for human interaction when necessary and that data privacy standards are being met with transparency about how that is being achieved.
•
Chatbots can provide measurable improvements in availability, accuracy, and efficiency, as they can operate 24 hours a day, 7 days a week and can quickly leverage data from various sources, such as knowledgebases and historical and annotated call data. While chatbots can be very effective, they aren’t a customer support panacea. They excel at automating the handling of routine questions, but when it comes to more complex problems, they must transfer the interaction to a live agent. That’s why the goal of chatbots shouldn’t be to replace agents, but to free agents to address the most serious and complicated customer problems.
•
Contact centers that have yet to invest in chatbots should start small. Test and use results to expand outward across the enterprise and customer engagement channels. Also, look beyond point solutions to strategize how to engage the wider enterprise and to interact with users through the entire customer journey from the initial contact to after sales care. This will help to enable frictionless customer experience.
Connecting the dots with artificial intelligence Artificial intelligence will become more pervasive throughout enterprises and contact centers. A customer can engage in hundreds of micro moments along their journey. When it comes to optimizing each micro moment, the key is ensuring that the consumer journey is tailored to the platform or device they are using. AI contextualizes and dives into the micro moments, targeting customers’ needs along the journey. To be successful, customer touchpoints must be embedded with machine learning algorithms that can be used to identify the patterns in the customer data from different sources and can help correlate a customer behavior to a matching persona in real time and then be layered with AI to enable automation and intelligence throughout an enterprise. This can provide a holistic customer experience throughout the customer journey, from the first touch to the final sale and beyond. For instance, the traditional approach to predicting customer lifetime value is based solely on customers’ historical data. But customer lifetime value models powered by artificial intelligence take into consideration a combination of factors, including the monetary value of the purchase and inference of future actions, for example, to make better predictions.
© 2022 Omdia. All rights reserved. Unauthorized reproduction prohibited.
