Introduction
We can’t deny the fact that the economy today is driven by experience, and customer satisfaction is no longer just a metric. It has become a focal point of every business that aims to achieve success. From star ratings to smiley surveys, companies are using various methods to collect customer feedback and Customer Satisfaction (CSAT) data; however, the question here is whether they are utilizing this data to initiate actual improvement.
Artificial Intelligence (AI) is no longer just a catchphrase. It has evolved into a revolutionary tool that transforms disordered and cluttered feedback into ordered actionable insights.
According to statistics mentioned in the Statista report Artificial intelligence (AI) in telecoms – statistics & facts, globally 43% of contact centers have already adopted AI technologies, leading to a 30% reduction in operational costs[i].
The CSAT challenge: Sea of data and lack of insights
Customer feedback is vigorously collected by every business via surveys, emails, and social media. However, as easy as it is to collect the data, it is equally difficult to analyse and interpret it.
Just imagine a situation where you are the CX manager for an online cosmetic brand, and you have collected.
- 1000 CSAT survey -post purchase
- 900 reviews of the products
- 1500 social media comments
That is about 3400 sets of feedback, which comprises of emotion, slang, and nuance. However, analysing and interpreting that feedback would take a number of analysts and weeks.
That is the CSAT conundrum, where you have the required data regarding your valued customers but no method to effectively and expediently extract it.
Presenting AI: The Feedback Revealer
AI converts raw data into organised intelligence. It transforms the way business processes feedback. It analyses, comprehends, and orders customer responses in a manner that humans can’t compete- it’s all done within a few seconds.
As per Microsoft’s article AI-powered success—with more than 1,000 stories of customer transformation and innovation– each dollar spent by the adopters of AI solutions and services generates an additional $4.9 in the global economy[i].
1. Sentiment Analysis: Paying attention to sentiments and not only to words
The 3-star reviews have a lot of other meanings. It might show little dissatisfaction or conceal intense frustration.
Natural Language Processing (NLP) is utilized by AI-powered sentiment analysis to recognise emotions, such as discontent, annoyance, frustration, and anger in the customer’s interactions.
AI-powered sentiment analysis uses Natural Language Processing (NLP) to detect emotions — joy, anger, satisfaction, or disappointment — within customer comments.
For example, AI can analyse a review like:
AI can interpret a review with mixed reactions:
‘The product is amazing; however, the delivery is really slow’
Here, two sentiments are involved-
Product quality (positive)
Delivery time (negative)
What we want to highlight here is that the tone matters. We can’t label everything as good, bad, and neutral. AI helps in recognising what’s working in our favour and what’s not.
2. Systematic gathering- from cluttered to organised
AI automatically categorizes feedback into subjects or groups, such as “customer service,” “product quality,” “post-sale issues,” or “delivery,” making it easier to analyse recurring themes and pinpoint areas for improvement.
So, when 2000 survey responses are received, a breakdown can be achieved by AI
- 20% mentioned customer service response time
- 30% mentioned delivery charges
- 25% mentioned product quality
- 25% mentioned payment methods
Then you receive a clear picture of a persistent issue. A map of what troubles your customers the most.
As per Markets and Markets stats Interconnected segments adjacenies in market ecosystems AI market in customer service will grow to $1,384.688 million by 2029, with a compound annual growth rate (CAGR) of 23.93% from 2024 to 2029[i].
3. Proactive Approach- Acting before the reaction
Recognising customer behaviour patterns at the earliest helps in identifying churn risks and rising dissatisfaction among the customers, e.g.
- If feedback related to “delivery issues” increases by 20% in a month. The managers will be alarmed by the AI to analyse.
- If keywords such as “stop,” “shift,” or “refund” rise. AI anticipates a probability of churn.
4. Automated Workflows: From Vision to Action
The tasks of AI is not limited to analysis- it takes actions. As it’s unified with CRM and helpdesk systems, automatically AI:
- Identifies low CSAT scores for instant evaluation
- Generate support tickets for pressing issues.
- Forward feedback to the appropriate departments.
- Generate tailored answers or follow-up surveys.
The consequence- Faster resolutions of customers’ issues, real-time closure of the feedback loops.
5. The tech-enabled collaboration- Human empathy joins machine intelligence.
AI can empower but can’t be a replacement for human touch.
After analysing and comprehending, it can identify what customers feel, but the empathetic response can only be carried out by humans. AI proficiency with human perception together can deliver the best customer experience approach.
- Trends identified by AI- Human devise better plans
- AI categorises problems-Human design tailored solutions.
- AI recognises tone- Humans offer empathy.
The Future of Feedback:
The prospect of CSAT is not limited to gathering feedback; however, for the future, it includes closing the loop promptly.
Envision this:
- Experiences shared by customers are analysed in a few seconds.
- Transferring issues to the concerned teams
- Real-time issue resolution
Noticeable improvements observed by the customers, leading to loyalty. With the assistance of AI, your effort is not restricted to gathering stars and scores. AI helps in comprehending the narrative and converts that information into insight.
Frequently Asked Questions on CSAT and AI
1. What does the term CSAT mean, and what is its significance?
The term CSAT (Customer Satisfaction Score) is a metric to understand how satisfied customers are with a company’s service or product. It helps companies in identifying areas of improvement.
2. Is AI efficient enough to identify customer sentiment and not just a “positive” or “negative” marking?
Indeed! AI models that are advanced can recognise customers’ emotions like anger, frustration, or discontent. This helps in achieving a better understanding of customers’ thoughts.
3. Does AI play a role in enhancing CSAT analysis?
AI uses Natural Language Processing (NLP) and sentiment analysis to evaluate customers’ feedback. Behaviour patterns, emotions, tones, and keywords are recognised and analysed. This analysis delivers actionable insights.
4. Is human involvement going to be replaced by AI in CSAT analysis?
No, AI improves human analysis as it efficiently handles a huge amount of data. Insights are interpreted and evaluated by humans to create strategic decisions.
Sources:
Artificial intelligence (AI) in telecoms – statistics & facts
AI-powered success—with more than 1,000 stories of customer transformation and innovation
Interconnected segments adjacenies in market ecosystems
