Sentiment analysis is among the more advanced applications of artificial intelligence (AI), and the industry has gained tremendous traction amid the pandemic. In 2020, the estimated valuation of the global sentiment analysis market was revised from $1.6 billion to $4 billion. And illustrating its potential, Google came out with its own Google Cloud Sentiment Analysis solution for healthcare customers.
There are several ways you can leverage sentiment analysis as part of your CX management toolkit. Here are the top use cases:
- Customer feedback in call centres – One of the most important and low-hanging use cases for implementing sentiment analysis is for recording customer feedback. During a post-call feedback survey, the customer might enter a generous 4 out of 5 – but their voice and tonality during the call is the real key to how they feel. Sentiment analysis can check for specific keywords with positive/negative connotes to capture more accurate feedback
- Understanding trends from social media – Social media offers a vast repository of publicly available information, from which you can gain essential brand-related insights. To take a simple example, social media chatter after a major product launch will reveal signs of adoption, acceptance, or resistance. A sentiment analytics tool uses AI to parse massive volumes of information and uncover the most dominant trend
- Intervening during a problematic call – Problem callers are an unfortunate reality in every contact centre, and they require deft handling so that the quality of CX isn’t damaged and there is no long-term impact on brand reputation. Sentiment analysis studies the tone, articulation, and even acoustics of interaction to indicate the underlying sentiment and predict where the conversation is headed. A supervisor can then intervene in real-time, handling the dispute before it occurs
- Multilingual CX management – When you cater to customers from around the globe, it can be difficult to always build specific linguistic competencies in-house. A sentiment analysis engine, with its AI algorithms trained in the lexicon and nuances of a specific language, can help to better understand customers from a different region and background
- Service request prioritisation – The first-in, first-out (FIFO) approach to handling requests isn’t always effective if you’re looking to deliver the best possible customer experience. Sentiment analysis uses AI to understand the criticality of a request and the urgency felt by the customer. It can even assign a churn risk score, which lets you push a request to the top of the queue and pre-emptively prevent customer churn
- Feature upgrades and product enhancements – Sentiment analysis on customer feedback can indicate user pain points and the features needed to address them. It can process hundreds of comments across online forums, identifying which elements of the UX or product experience impacted users the most. This can help you come up with new product versions that are more in alignment with customer needs
- Agent experience improvement – Happier agents always result in happier customers, which is why it is so important to leverage sentiment analysis for internal communications as well. You can identify which employees are disengaged, thereby affecting the quality of service they deliver. You can also detect specific knowledge and skill gaps to be addressed via training, leading to a more effective CX for the end customer
This is just the tip of the iceberg. As sentiment analysis and AI algorithms become more accurate and capable of multichannel data ingestion, you can look forward to industry-specific use cases, more real-time applications, and even prescriptive insights.
Finally, it is important to know the right time for implementing sentiment analysis as part of your CX strategy – here are 4 tell-tale signs to watch for:
- You are collecting feedback from customers across the purchase journey, right from the first website visit to post-purchase calls. This means there is a lot of unstructured data to parse, which is impossible to process manually
- You have crossed the 500 brand mentions per month threshold. Beyond the first few hundred mentions, it will become impossible for your CX team to keep track of online sentiment and stay abreast of every new mention
- Mixed-sentiment comments are increasingly common, making it difficult to quantify feedback. For example, if a customer appreciates product quality but expresses dissatisfaction about the delivery timelines, you need sentiment analytics to avoid qualifying this data incorrectly
- You have a sprawling CX strategy without clear prioritisation. When large teams, multiple possibilities, and a layered technology stack are at loggerheads, sentiment analysis can help zero in on the real voice of the customer and define the agenda
