September 29, 2024
content strategy
In the modern dynamic business landscape, customer experience (CX) has become a crucial success factor.
Customer experience encompasses all interactions and experiences a customer has with an organization or brand at every touchpoint, from the first awareness about a product to after-sales services.
Today, when customers are ready to switch to competitors at the taiwan email list slightest dissatisfaction, it is essential for organizations to understand changing customer needs, explore innovative ways to maintain their loyalty, and stay ahead of the competition.
What is Verbatim Analysis?
Verbatim analysis is a strategic method used to extract meaningful insights from raw, unstructured customer feedback.
These comments, called “verbatim,” are word-for-word extracts of customer feedback about their experience with a product or service. By analyzing these verbatim words, companies can gain a qualitative understanding of their customers’ sentiments, preferences, and expectations.
Unlike traditional quantitative metrics like Net Promoter Score (NPS) or Overall Satisfaction Index (OSAT), which quantify the level of customer satisfaction, verbatim analysis goes further by providing detailed information about the specific problems customers encounter or address their pain points.
This process allows companies to fine-tune their strategies based on customers’ real needs.
Why is verbatim analysis essential?
Many companies often ignore verbatim analysis due to lack of resources and time. However, this raw data is more authentic and explicit than simple numerical scores.
Verbatim comments reflect the voices of customers in a candid and unfiltered way. By examining this data, companies can uncover crucial insights that would not be gained through closed-ended surveys.
1 – Deep understanding of customers.
It allows you to go beyond the numbers and understand customer sentiments, preferences, and expectations in a more nuanced way.
2 – Identifying pain points
Verbatim words can reveal specific problems that customers are experiencing, giving businesses opportunities to make targeted improvements.
3 – Spotting emerging trends
Textual analysis helps identify emerging customer trends and behaviors, allowing businesses to proactively adapt.
4 – Personalization opportunities
By understanding the expressions and language that customers use, businesses can personalize their interactions and improve the overall customer experience.
Textual sources
Textual words come from a variety of sources, ranging from satisfaction surveys to social media, discussion forums, complaint emails, and customer reviews on e-commerce platforms. They can be classified into two main categories:
Textual copies are requested:
Collected directly by the business through questionnaires or satisfaction surveys.
Spontaneous text comments:
Customers share their opinions without being asked, usually on social media or online review platforms. These comments can positively or negatively influence the brand's online reputation.
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Text analysis is based on two main methodologies: manual analysis and automated semantic analysis.
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How to analyze texts?
1 – Manual text analysis
Traditionally, manual analysis involves reading each customer's reviews one by one and then grouping them by topic or theme. Each comment is coded with a number or label representing a specific topic.
For example, responses to an open-ended question such as "Are you satisfied with the product you purchased?" could be grouped into topics such as product quality, price, or delivery time.
This method, although detailed, is time-consuming and best suited for small text samples. In addition, it is often done in Excel spreadsheets, which limits the ability to capture the contextual nuances of feedback.
2 – Semantic analysis of texts with AI
Unlike manual analysis, semantic or lexical analysis uses artificial intelligence to analyze texts in real time. Specific AI software automatically groups comments into categories based on their content and criteria defined by the company. The algorithm can also classify the comments into categories based on the content of the comments and the criteria defined by the company.
