Customer Sentiment Analysis: Redefine your Customer Journey
Customer sentiment analysis – what is it?
Simply put, it is a process by which you can determine how your customers really feel about your products & services by analysing their behaviour and language when they discuss your products on the internet. It is largely driven by automated artificial intelligence keyword processing or NLP algorithms based on machine-learning and big data analytics. But more on that later on.
To give a simple example, you have customers who have reviewed your services on a review website, discussed it on Facebook with friends, searched on google for similar services, and been to your website (and that of a competitor) multiple times. Customer Sentiment Analysis (CSA) helps you to learn what your customer is really thinking of your service. It offers companies more insights into how to effectively manage and target their existing and new customers.
So, what are some of the benefits of Customer Sentiment Analysis?
Well, to start off there are some big benefits for your Marketing & Sales departments, namely:
- You can build and structure your marketing strategies more accurately and hence maximise your ROI.
- Taking into account real customer sentiments means you can better manage customer relationships, and segment customers into groups based on their opinions and feelings towards your company, and its services.
- Better prepare your customer service employees who have real-time data on the opinions and sentiments of customers towards your brand, products and services.
- Improve engagement outcomes with your customers
The different forms of Customer Sentiment Analysis
As the name says this is a manual process carried out by human beings – not so A.I. you may think. However, this is still a necessary process (for now) as A.I. is not always able to detect and determine aspects like sarcasm or ambiguous textual content. This is where human intervention is required.
Well-defined and A.I. enabled algorithms can lead to real-time Customer Sentiment Analysis, which enables your customer service agents to see in real-time what keywords are used by your customers. This information point helps to provide a complete overview for your employees when dealing with a customer.
Natural Language Processing (NLP)
Data mining techniques and analytics for both audio and text can eliminate the shortcomings of the previous two methods. NLP also accounts for the emotions being expressed by your customer and factors this into the final overview.
What would such a situation look like for your company?
1. Your customer calls your call center
2. A.I. analyses the call in real-time: An AI bot is quietly conferenced into the call to analyze the caller’s sentiments and emotions.
3. Business rules trigger actions: Your business rules can prompt the bot to alert a supervisor if the bot’s real-time analysis of the caller’s sentiments indicates a possible customer satisfaction issue.
4. Experts provide real-time support: The supervisor can coach the agent in real-time via a whisper or text chat, and together they can provide the customer with the best possible service.
5. The bot broadens its understanding: The AI bot gets better at decoding sentiment and emotions as it monitors the effect of the supervisor’s suggestions. With ever-improving insight into caller sentiments and emotions, the AI gets even better at deciding when to alert a supervisor.
So, what are the challenges?
Although this form of A.I. is not commonly adopted yet, it is already making big strides. And with every new innovation, there are some challenges that have been identified.
Sometimes users express fake opinions which distorts the value of the information that CSA generates.
Limits of Manual Processing
Using human beings for processing is time and resource-consuming, which does not always have a positive effect on the cost-benefit analysis of CSA.
With over 7,000 languages worldwide, CSA is currently most effective in the big common languages like English, Spanish, Mandarin, and French. This means the technology is not yet viable for every language.
Poor strategy planning
Effective implementation is often a factor why CSA does not meet its full potential. The increase in data, especially sentiment data from social media, means a huge influx of data at a fast pace which not every organisation is ready to handle.
Is Customer Sentiment Analysis viable for your business?
Yes, it is. Every business wants to be better prepared to engage with their customers and truly know what they are thinking to best anticipate and manage outcomes. Yameo is a tech partner to Vonage, one of the leading providers of Customer Sentiment Analysis solutions. Using APIs we can help you get started, or if you are not yet ready, we have a dedicated team ready to brainstorm with you and define your idea.