IT customer feedback analytics with AI.

In the present era, customers are the gateway to a successful business. Without customers, a firm cant take hold. This shows how important it is to satisfy the customers needs and do their best. For this, simply developing products and platforms wont serve customer requirements. The higher importance here is to capture and analyze customer feedback. This is a pivotal part that most organization needs to consider. Customers feedback indicates whether they are satisfied or not with the product/platform they use. If the satisfaction level is low, this indicates that there is some improvement required. 
Many organizations collect customer feedback but fail to analyze actionable insights from them. That is, most feedbacks do not result in any actionable requirements. While giving feedback, customers wont provide a score only; they also mention "why" they gave that score. So, this "WHY" part contains the actionable data that we need. Therefore, the problem here is not the lack of data, but the data is not analyzed and proceed properly. This is a significant area that needs focus. But the traditional method of feedback analysis wont be applicable anymore. The use of advanced technology is a must. Thats where artificial intelligence and machine learning comes into the picture.
AI in Customer Feedback Analysis:
The advancement of technology has brought light onto many organizations, and AI is the pivotal player in that sector. One such area where AI played its principal part was customer feedback analysis. Natural language processing(NLP) and machine learning(ML) were the major AI technologies used for feedback analysis. Both these technologies enable to perform automated feedback analysis for predictive analytics and actionable insights.
The customers' feedback can be collected from multiple sources such as email, surveys, support tickets, etc. All these feedbacks act as data for analysis, and these can be converted into actionable insights by using NLP and ML. Machine learning technology is capable of providing predictions based on historical data. This enables understanding the customer behavioral pattern, that is, whether the customer will stay longer, how likely the customer will churn, or will they visit in the future. On the other hand, natural language processing(NLP) is performed to capture insights from data such as the sentiments of the feedback and to group string text to uncover patterns and trends. Apart from that, we can set a score on every feedback issue with the implementation of AI. If the score set is low, built-in AI-based models will automatically trigger a ticket to the IT operators team based on the low score. Or else, there will be virtual L1 agents who will respond to these issues. 
AI functionalities in Customer Feedback Analysis:
The implementation of AI, ML, and NLP enables capturing feedback data, extracting insights from this text, and turning them into actionable form. The following are some major functionalities AI can perform for customer feedback analysis:
1.Text Analysis:  The AI-based platform enables analyzing and performing text analysis to evaluate customer thoughts and feedback. This analysis can be done by understanding the frequency of the words used or grouping up of words. For example, a customer file a feedback that "happy with the service" or "the problem is not fixed yet". For this feedback, we can interpret that the customer is satisfied with the service or frustrated that the issue is not resolved. In this way, based on the text pattern we can feedback data into actionable insights.
2.Sentiment Analysis:  Sentiment analysis is used to understand the feelings on hold for the business. It can divide the feedback into different categories, such as positive, neutral, or negative feedback based on text/words. Based on the negative review, the organization can work on them by understanding customers' pain points. This enables in building up the customer to business loyalty.
3.Automated Feedback Report Generation: Every feedback is analyzed using text and sentiment analysis. Based on this analysis a report is generated which is an automated feedback, that enable the IT operators to understand the feedback pattern, emotions of the customer, common issues faced, etc. 
4.Improved Customer Experience: With the implementation of AI, customer experience(CX) is improved drastically. The use of text and sentiment analysis enables businesses to understand the journey of their customers seamlessly, and also based on the report generation, IT operators can take right action to remediate the issue. This enables us to understand the customer trend patterns or any other issues in real-time. If any problems are found, these can be resolved by using virtual agents or automated ticket generation. Also, with the use of AI-based models, the service can be ensured to customers 24/7. This helps in reducing the time for manual working hours.
Capturing feedback and providing the right action to customers is a key requisite. This can improve the business and also customer loyalty. Algomox AIOps provides an AI-based platform for the organization to perform automated analysis on customer feedback. To know more about Automated feedback analysis using AI, visit our Cognitive Engagement Console page.