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.
Natural Language Processing
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 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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