Types of Customer Feedback Analysis

In an age where there is an abundance of data, brands need to apply different approaches to customer feedback analysis in order to understand the next steps necessary to foster their growth.

There are a variety of metrics that can be evaluated from customer feedback such as brand reputation, product perception, and customer satisfaction. Each metric can help brands understand how to meet customer expectations by making the right improvements to their product and learn crucial insights that can increase their market shares.

In today’s post, we will take a look at some of the most prominent types of such analysis.

This type of customer feedback analysis discovers the different ways people talk about a given brand and its products. This gives a broader view of where the company stands and how it is perceived by customers. Quarterly analysis of this kind can help to monitor long-term changes both in brand reputation and sales performance. This analysis is especially helpful when comparing the performances of different versions of products.

Machine learning is used to generate recommendations based on the user experience shared in the feedback. This analysis is useful to compare the product perception across different sales channels. Additionally, it helps improve the understanding of customer demographics and the expectations across these channels. Doing this analysis, performed in real-time, allows brands to adopt and implement new improvements suggested directly by customers swiftly.

This type of customer feedback analysis involves collecting both functional and emotional metrics. This type of analysis is usually done a post product launch. Complete customer satisfaction analysis will often combine online and offline sources of feedback. This analysis is critical for brands to understand areas of improvement in the customer experience journey. Brands often update or remove products from markets based on the level of customer satisfaction provided.

The purpose of this analysis is to better understand the aspects that drive the sentiment of customers. The sentiment analysis metrics can be collated by review and by opinion holder. This analysis usually involves using a knowledge base that maintains several generic profiles under which customers are grouped.

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