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While trying to get a hold of a customer service representative many of us have spent a lot of time waiting which usually leads to frustration directed towards the company. Consequently, long call wait time can have a direct influence on the rate of customer satisfaction. If companies want to reduce wait time and ensure customer satisfaction through efficient service, then companies have two options. First, companies can hire more customer service representatives, which requires a big financial commitment; or, companies can automate parts of customer service, which is financially viable. To fully automate customer service requires machine learning and machines have not mastered languages like humans (see our post on computers learning languages), so a partially automated system using Artificial Intelligence can make customer service representatives more efficient with an automated database.

Where Data Science Comes in

Automation of customer service requires data and management. Each day, a copious amount of data is generated through live chats, texts, phone calls, emails etc. This data is either structured data, has a logical flow, like a spreadsheet with defined rows and columns, or unstructured data, which is not organized, for example data collected from speech data. Data Science is used to sort unstructured data by analysis through automated or manual methods. Such analysis helps in sorting data by ranking, classification and clustering. Ranking, classification and clustering data are important steps to filter data according to its use. These steps provide structure or a logical framework for the accumulated unstructured data.

Once the data is sorted, it can be scored. Data scoring automates data to predict and prepare companies with frequently asked questions. These questions are used to prepare answers to adequately address customer needs. Furthermore, data is used to cluster and break down topics. Through these steps data is sorted and scored to draw meaningful information from unstructured data. The process of data structuring and analysis is crucial to partially automate customer service as the structured data can be used by machines to help customer service representatives to better address customer problems. Consequently, data science has an important role to play in data analysis and modeling to prepare companies. Since companies today rely heavily on data to cope with the dynamic business environment, the ability to effectively interpret data can have a direct influence on customer satisfaction and sustenance of business.

Proper analysis of customer data through the help of data science will allow companies to focus on areas of difficulty and ease to prioritize areas of automation accordingly. To bring it back to the actual customer service call, one key take away from partially automating customer service is that it will reduce call wait time. This will make the existing customer service representatives more efficient by allowing them to address more customers in any given day, as well make the customers happy that their time isn’t wasted.

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