AI based Multilingual Transfer Desk Agent via Email Analysis
Company to Customer communication is very important, and these days most of this communication is done via email correspondence. These correspondences are very important for the companies since they can contain valuable information. On the contrary, they can also contain banal and unwanted information. Resolving this is a very hard task since; an organization on an average receives thousands of customer emails per week, which can be a very gruelling task. Our goal is to build a model that analyses the content and the context of the emails and prioritize them.
United Kingdom
Client
Asset Management Firm
CLIENT
Our client is an asset management company based in the UK with extensive operations all over the world. Due to the precarious and very hectic nature of this field of business, they needed a system that will be a one-stop solution to their customer communication needs. Most of the communication between the company and their customers are done via email correspondences, by implementing this model we can increase the efficiency of the process tenfold.
CHALLENGE
Generally, a textual data models pose arbitrary and minor challenges but since this model has a very complicated method of organization, there are are some challenges that have to be addressed.
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Method of Sorting - The manner of how the mails are going to be sorted and analyzed should fit the nature of how the data is being requested by the client.
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Limits of Tonal Analysis - The tone regarding text always varies, especially when it comes to communication between a company and their client. This issues has to be tackled so that we can easily identify the customer’s concern.
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Data Size & Churn Rate Concerns - The amount of messages can some times pour over to hundreds of thousands, so the churn rate has to be highly efficient.
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Multilingual Support - The syntax for various languages drastically differ, so our email support system should be all-encompassing.
SOLUTION
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Chains of communication between the customer and the client is parsed and a sample set is created.
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Sentimental analysis is run on the text to derive the nature of the messages and their priority conditions.
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Comparing this new derived conditions with the client’s communication patterns, the exact agent who has the ability to resolve this issue is found out.
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Cross-interference between the agents and the customers are reduced via proper task resolution methods.
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One time resolution is aimed for most processes reducing redundancy.
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Then the model is streamlined to cover issues regarding similar textual tonality, efficient and multiple processing strategies.
RESULT
By integrating this model to their current workflow, the client saves a lot of money and resources. The model also increases the acumen for customer relations simultaneously increasing the induction and retention. Agent training methodology can also be derived from the model which can boost productivity even more. The rate of miscommunication between the agents and the customers is drastically reduced. The manner of how the data is processed leads to an one time resolution by it’s state-of-the-art prioritization methods. The boost in customer churn and satisfaction rates will be extremely profitable to the company in various levels.