When customer responses and tweets became more frequent and demanding, a multi-national consumer brand had to find an efficient way to handle tweets in real-time without throwing an army of customer support agents at the problem. We provided a Natural Language Processing (NLP) based solution that seamlessly analysed the tweets and either amplified the positive sentiment or mitigated the negative sentiment being expressed in them.

Industry

Retail

Solution Components

Process Intelligence

Natural Language Processing

Machine Learning

Technology Integration

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The Challenge

The customer, a multinational consumer brand, needed a proactive customer support desk to automatically track the user responses on Twitter and respond to them. However, it was critical to understand the sentiment behind the tweets before deciding the nature of the response.

In addition, there was the challenge that some users would tweet using the # tags on the company while others would use some Twitter handles.

The Solution​

We designed a solution using NLP to understand the tweet content and Machine Learning models for the analysis, in order to identify the sentiment associated with the tweet: normal, sarcastic, funny, frustrated. Depending on the sentiment being expressed, the intelligent process design executed an action plan involving the combination of intelligent automation and human agents. The automated responses could be an acknowledgement or a retweet to amplify positive sentiment tweets. Negative sentiment tweets triggered an escalation process to a human agent with the help of tracking/ticketing tools and updating the user back on social media that the issue was being looked into.

The Impact

The sentiment analysis solution became a great social listening tool for the client. Proactively managing the negative tweets and amplifying the positive tweets provided a huge brand uplift, as well improved customer satisfaction – all this without having to add more heads to the customer support helpdesk.

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