Ai in CX, where and how

Like any other progressive technology company, CX frontrunners are experimenting with the power of AI in automation for a few years now. When we say automation, we jump to the intend of automation replacing the manual work to save costs. Understandably, it can be viewed narrowly through this lens. New generation automation comes with tremendous capabilities to run and make logical decisions in lesser time than the human mind

Why AI?

To offer an optimal customer experience across touchpoints, the system has to go through the history of the customers’ datapoints. The data points get broken down to multiple data sets. Throughout the customer journey and across all interactions, the data can get structured and unstructured, even to a level of an orphaned component. To a human, individually to act on it can be unattainable. With the help of AI, using logical workflows the data science can structure the data to a meaningful analysis to humans. By setting multilayered workflows, the system sends out substantial customer-centric information that can be personalized and actioned.

How does AI do it?

AI runs on human set algorithmic logics. In the front end, you set workflows. In the backend, the system runs the data clusters following the logics and give away the mathematical and logical output for the workflow you set.

In the context of CX

Personalization

CX companies use AI where and when necessary across the operations. For example, you can use AI to pull data from anywhere in your database and understand the history of the customer. By running preset logics on a customer dataset, you can create logics to personalize a recommendation.

Analytics

Using speech analytics, AI can capture data inputs like audio logs or checkboxes, or even speech-based inputs. AI can also do sentiment analysis at the end of the call based on speech recognition. To state an example, AI can analyze deeper: based on the last call’s NPS score, and a review in social, AI can help you correlate the real reason behind the sentiment.

Load balancing

AI can analyze the call volumes and patterns and recommend the number of frontline delivery executives that are required for a time slot.

Chatbots and Voicebots

Chatbots and voice bots are built based on a mixture of AI technologies like Speech recognition, Natural Language Processing and Machine Learning. The bots refine the conversation to the intent and point them towards the right answers. Depending on the depth of the flow in the design, a bot can resolve to answer the level of queries. It can be start-with-a-bot and end-it-with-a-human, or a 100% bot-based-self-serve. 

The future is predicted towards AI replacing most of the routine CX tasks. AI augments human-made logics to run faster and to act in real-time. At what percentage CX companies leverage the power AI, lies on the hands of the governing leaders. 

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