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    Sentiment Analysis: A Look at Your Customers’ Emotions

    Every day, buyers leave reviews, helpful comments, and experience reports online—whether about a product, service, process (such as a purchasing process or delivery), or a brand. But how can one determine whether these customer reviews are predominantly positive or negative? This is exactly where sentiment analysis can assist. In this article, we will discuss how sentiment analysis helps brands summarize, analyze, and evaluate customer feedback emotions across various channels.

    Visual Representation of Sentiment Analysis

    Table of Contents

    1. Definition: Sentiment Analysis
    2. Prerequisites for Sentiment Analysis
    3. Use Cases for Sentiment Analysis
    4. How does sentiment analysis work?
    5. Challenges of Sentiment Analysis Solved Thanks to AI
    6. Examples: Sentiment Analysis in Application
    7. Conclusion: The Use of Effective Sentiment Analysis Pays Off

    Definition: Sentiment Analysis

    Sentiment analysis describes a method that summarizes and analyzes customer reviews, comments, and relevant customer data. Each comment is examined to determine whether it expresses a positive, negative, or neutral emotional tone. The analysis can then reveal whether a customer had a positive experience with a product, service, process, or brand or whether something important was missing.

    Comments like “Great quality and super comfortable!” are classified as positive sentiment, while comments such as “The fabric feels cheap!” indicate a negative sentiment and prompt companies to improve their product.

    It is important to note that multiple sentiments can be expressed within the same comment, making the analysis more complex. For example, a customer review might state:

    “The delivery was faster than expected (positive), but the packaging was damaged (negative).“

    With the support of AI (Artificial Intelligence), even complex reviews with multiple sentiments are captured and analyzed. Moreover, AI identifies the specific topics that a review addresses. Topics such as customer service, product quality, delivery & shipping, price-performance ratio, and usability are often evaluated in customer comments.

    Ultimately, this allows brands to not only analyze customer feedback as a whole but also to specifically evaluate individual topic areas. This enables companies to make improvements in these specific areas to demonstrate to customers that their opinions matter.

    Visual Representation of Sentiment Analysis

    Prerequisites for Sentiment Analysis

    Sentiment analysis is an essential business intelligence tool in marketing that helps companies improve their brand image through the evaluation of customer comments.

    Customer Surveys for Measuring Satisfaction/Dissatisfaction

    The goal of every company is to increase customer satisfaction. But how exactly can customer satisfaction be measured? This is where customer surveys come into play. Frequently asked questions during online shopping include: “How satisfied were you with the purchasing process?” or “Would you recommend our brand to a friend or colleague?” After the buyer gives a rating from 0 to 10, they can leave a comment. The average rating serves as an indicator of how satisfied customers generally are. This is known as the Net Promoter Score.

    The Net Promoter Score (NPS) measures customers’ willingness to recommend a product to friends or colleagues. The rating given on a scale from 0 to 10 is categorized into one of three NPS categories: Promoters, Passives, or Detractors.

    Promoters are customers who give a rating of 9 or 10. They are extremely satisfied and happily recommend the product to family, friends, and acquaintances without hesitation.

    Passives give the product a rating of 7 or 8 and are only moderately satisfied with their purchase. They may still look at competitors and, if convinced, switch to them instead of remaining loyal to the brand.

    Detractors give a rating between 0 and 6 in the survey and represent permanently dissatisfied customers. They can negatively impact the popularity and reputation of the brand through their negative comments. Nevertheless, the feedback from dissatisfied customers is valuable, as it helps brands identify exactly what needs improvement.

    Would you like to learn more about how to calculate your NPS score? Then click here.

    So far, so good. In the next step, after the rating, the customer can leave a comment. This is the real gold!

    How Can Customer Feedback Be Collected?

    Each brand naturally has its own channels that it can easily access, such as its online shop, emails, or even offline in physical stores. Here, the company can effortlessly integrate customer surveys at all touchpoints along the customer journey. In-store, for example, a flyer with a QR code can direct customers to an online survey.

    Additionally, companies also offer their products on third-party platforms like Amazon or Zalando, where customer feedback is available in the form of product reviews.

    Moreover, customers often have the option to leave a review on comparison platforms. Well-known sites include Trustpilot, Google Reviews, Yelp, Facebook Reviews, Idealo Ratings, and Check24 Ratings.

    How can a holistic view of customer feedback be created? Currently, a vast number of customer comments are spread across various channels. As a result, there is often a lack of a clear overall picture of customer emotions.

    A solution to this problem would be a single platform that consolidates all data and feedback from different channels, enabling analysis and ultimately deriving improvements. Platforms like zenloop make this process possible.

    The following illustration places customer feedback at the center. Processing this feedback allows different departments—whether customer service, sales, or logistics—to be informed and make targeted improvements based on it.

    Visual Representation of Sentiment Analysis

    Ultimately, sentiment analysis is essential for companies to quickly and easily draw conclusions about customer perceptions and identify the type of emotions, such as complaints or joy.

    It’s not just about understanding how the customer perceives the product but, more importantly, about recognizing their needs and desires through sentiment analysis. The goal is to enhance and strengthen the customer experience.

    Use Cases for Sentiment Analysis

    Sentiment analyses play a central role in marketing analysis and the optimization of customer experiences. With digitalization, thousands of new reviews, customer comments, and feedback pour in daily through social media and online shops.

    But where exactly does sentiment analysis provide real value and save valuable time?

    • Strengthening Brand Awareness

    Brand awareness plays a crucial role in the success of a company. Sentiment analysis captures customer reviews and shows how the brand is perceived in the public eye. Each positive comment about a brand and its products acts as free advertising. This process not only strengthens brand awareness but also enhances brand image and customer loyalty.

    • Early Detection of Dissatisfied Customers

    Naturally, complaints from dissatisfied customers are not enjoyable, but they provide valuable insights into where problems and optimization potentials exist. Sentiment analyses help identify these critical customer voices and initiate targeted actions using the closing-the-loop approach. Thus, it can be said that it is very significant and helpful for a company when customers express their dissatisfaction in comments.

    • Understanding Sentiment Trends in Target Groups

    Opinions about a product can vary depending on the region or target group. With sentiment analysis, it is possible to identify where similar sentiments are found in different regions and how they change over time. This helps companies tailor specific products to the individual needs of their customers.

    How does sentiment analysis work?

    Sentiment analysis works through the use of technologies from artificial intelligence (AI). It involves natural language processing (NLP). Such tools capture written information like customer reviews and comments and identify whether the emotional expression conveys positivity, negativity, or neutrality. There are different approaches to interpreting the text according to its sentiment:

    1. Approach: Rule-Based Sentiment Analysis
      In this method, predefined dictionaries are used in which individual words are assigned to one of the three sentiment categories—positivity, negativity, and neutrality—allowing the overall sentiment in the text to be determined.
      Examples: “great” = 0.9, “bad” = -0.7, “okay” = 0.1
    2. Approach: Machine Learning Approaches
      In this approach, computer software is trained by feeding it pre-classified data sets. In these data sets, it is already known whether the emotional tone is positive, negative, or neutral.

    Through repeated training with such examples, the computer model learns to capture emotional sentiment with high precision in new, unknown data sets.

     

    Challenges of Sentiment Analysis Solved Thanks to AI

    The classification of emotions can be a significant challenge for machines. Despite technological advancements, human language presents a greater difficulty.

    Interpreting Sarcasm Correctly: When a sentence contains positive words like “great,” “fantastic,” or “good,” the computer would typically classify the sentiment as positive based on its stored data sets. However, thanks to modern technology and rapid advancements in AI, even sarcasm can now be detected.

    Understanding Negations Correctly: An example would be: “The quality of the product is not bad.” Here, AI recognizes during sentiment analysis that, in this context, the word “bad” is perceived as positive to neutral rather than simply negative.

    Multipolarity: Another challenge, briefly mentioned in the “definition of sentiment analysis,” is multipolarity. This refers to the fact that a customer comment can express multiple sentiments simultaneously, conveying both positive and negative emotional tones.

    Different Languages: Linguistic diversity poses a significant challenge for many brands. Companies often face the task of understanding and responding to customer feedback, such as comments, in various languages. In German, there are also many nuances, slang, and different meanings of words that AI can fortunately recognize and interpret correctly.

    Examples: Sentiment Analysis in Application

    Often, it is the look at concrete results that truly convinces—especially when companies have successfully implemented sentiment analysis. And that’s exactly what we can offer you in this blog post:

    Example: Thalia

    The challenge for Thalia was to systematically consolidate customer feedback into a single system and develop targeted measures from the evaluations. With the help of zenloop’s CX management platform, Thalia achieved outstanding results. They were able to maintain a Net Promoter Score (NPS) of +78 consistently by implementing sentiment analysis. zenloop’s platform is based on three central pillars of effective CX management: Listening, Understanding, and Acting. The pillar of “Understanding” focuses heavily on sentiment analysis to structure and evaluate Thalia’s entire customer feedback, which significantly improved the customer experience. Thalia’s success story is also a perfect example of “Closing the Loop” in customer experience management. By using zenloop, Thalia was not only able to systematically collect customer feedback but also derive concrete measures from it and implement them directly.

    Through this closed feedback loop, Thalia not only optimized their NPS but also significantly increased their Trustpilot rating and regained customers who might have otherwise churned.

    Do you want to learn more about Thalia’s success story with zenloop and how sentiment analysis achieved impressive results through our collaboration? Then click here.

    Example: vertbaudet

    A key component for vertbaudet was sentiment analysis, which allowed the company to gain deeper insights into customer satisfaction. Within 8 months, vertbaudet was able to capture over 6,500 customer comments through surveys on their online shop and marketing emails. Here are some examples of improvements that vertbaudet implemented:

    • Addition of payment methods after continuous feedback was submitted
    • Online purchasing as a guest, without needing to create a customer account beforehand
    • Introduction of free shipping for orders over €75, which reduced the number of complaints
    • Activation of printable vouchers

    After implementing all these improvements, vertbaudet was able to increase their NPS by a full 12 points and reduce customer service inquiries by 35%. vertbaudet also represents a perfect example of true “Closing the Loop.”

    Would you like to learn more about vertbaudet’s success story? Then click here.

    Conclusion: The Use of Effective Sentiment Analysis Pays Off

    In conclusion, it is worthwhile for companies to invest in sentiment analysis. It enables the targeted collection of customer feedback and provides valuable insights to enhance the customer experience through customer comments. Overall, successful sentiment analysis helps strengthen relationships with customers and address their needs.