Social media channels has grown exponentially in the recent times. With consumers taking to social media to describe how they feel or think about brands, this media has fast become very integral to CEM. Brands must ensure an active social media presence to capture the market. A lot of the data that is available across the social media channels are unstructured and can reveal deep insights that consumers have based on their numerous or first time experience with a brand. NLP or Natural Language processing is the bridge between machine language and everyday speech. Text analysis and sentiment analysis help make sense of all this vast data that is collected.
The challenge faced in gathering true and valuable customer feedback is ensuring an NLP tool that is intelligent enough to make sense of short “texting “ language to vast reviews. Sarcasm and puns need to be analysed correctly to get a correct reading of true sentiments. Unstructured data pool is very vast and requires trawling of each and every single word in correct contexts. It’s a huge challenge that companies are faced with when tackling such data.
A customer satisfaction analysis showed that most customers do not reveal directly that they are frustrated with a brand or have intentions of churn. Customer satisfaction index might be high but so also will be the churn rate. A deeper look into such customer satisfaction management revealed that customers who had jumped to competition were the ones who rated as most satisfied. To get a correct reading of this, the unstructured data was analysed and it showed that customers reveal more on social media than they do in a formal feedback form provided by the company. The importance of NLP cannot be stressed enough.
Sentiment analysis that measures whether the customer sentiment is positive or negative towards a brand is of great value. Consumers are guided by their feelings when it comes to choosing a brand. With so many options available, the only differentiating factor is whether their positive experiences far outweighed the negative experiences with a particular brand. Using an NLP tool businesses can truly assess customer sentiments and understand what motivates their typical consumer profile. They can better experience, resolve problems and ensure lower churn rates.
One of the future trends is self serving customers. These customers prefer to be empowered enough to find answers to their own problems, without relying on customer care centres. Customer engagement can be vastly improved using NLP. When troubleshooting, the NLP tool can easily assess what is required and provide the right answers to customers thus freeing up agents to engage with customers who do directly contact them. However there is a huge risk associated with engaging a poorly managed NLP tool, customer frustration increases if the tool does not correctly asses what is required. Expectations are also set high with technologies like Siri or Voice being commonly used. Consumers are used to superior NLP tools in their everyday lives and take them for granted. Hence NLP tools if employed have to be superior.
Rising expectations and trends like self service on the rise, companies need to hop on the NLP bandwagon soon. Brands that employ such intelligent tools in the right manner ensure superior customer experience that drives engaged customer who are brand loyal and will work proactively to promote such brands.