Businesses are always looking for ways to attract and on-board new customers. But some overlook the importance of retaining their existing customer base. Focusing on customer retention is equally important to growing your business. As each new customer is an additional individual to target rather than replacing a lost consumer.
💡 Read Sentiment Analysis 101: Everything You Need to Know
Sentiment analysis is critical in understanding how to keep your existing customer base engaged with your company. Studies by Deloitte have shown that customers are likely to spend up to 140% more with you if they have a positive experience. In contrast, a negative experience leads to them spreading the word to, on average, 16 people.
By using sentiment analysis to find out how customers feel towards your business, you can implement strategies to improve your customer satisfaction score and loyalty and maximize your profits.
Understanding Sentiment Analysis
Sentiment analysis is a strategy that automatically detects emotions and feelings. In customer retention, sentiment analysis uses natural language processing (NLP) or machine learning algorithms. It is used to understand whether a customer’s feelings toward your business, brand, or products are positive, negative, or neutral.
Read Meeting Customer Needs: 7 Ways to Get to Know Your Customers Better
Sentiment analysis consists of two parameters to assess a customer’s feelings towards a product or business. Firstly, polarity determines whether the exhibited emotions are positive or negative. The second is magnitude, which assesses the strength of those feelings shown by the customer.
An excellent illustration of this is the sentiment analysis undertaken by Nike when its sponsored NFL player Colin Kaepernick led the movement to take a knee during the US national anthem before games. Nike could make informed decisions about its commercial relationship by observing public opinion toward Kaepernick.
Several challenges lay with assessing customer sentiment using text data. Tone can sometimes be difficult to detect verbally, a challenge amplified with the written word. For example, sarcasm can be extremely difficult for sentiment analysis tools to pick up on. In addition, while the use of specific terms, such as “love” or “hate,” makes polarity easy to discover, other more oblique phrases, such as “not bad,” that lie in mid-polarity, can be left out. The result is a diluted and inaccurate sentiment score.
As any data analyst will tell you, analytical outputs are only as good as the data provided. So having clean, precise source data with clearly defined polarity and magnitude is the best way to receive an accurate picture of customer sentiment towards your business, services, and products.
Real-World Applications
Asking open-ended questions to gain feedback is one of the best ways to gain a true sense of how your business is performing in the eyes of your customers. However, open ended answers are much more challenging to analyze than closed questions with multiple choice answers.
Answers are often messy and difficult to analyze quickly as individuals have to scour sometimes lengthy explanations to pick out keywords. Sentiment analysis can do this automatically by scanning written comments for particular keywords that ascribe sentiment. The algorithms can then provide the comments with a sentiment score that gives a quick picture of customer sentiment.
Read Top 5 Examples Of Sentiment Analysis
In addition, to give a more organic sense of customer sentiment, media analysis tools can pick up on mentions of your company or brand across social media channels. That can provide further insight into how a wider number of people feel towards your business. The wider the sample of data to assess, the more accurate the outputs will be, enabling better-informed business decisions aimed at retaining customers.
Businesses of every type, from those that offer custom made t-shirts to global multinational drinks corporations, stand to benefit from sentiment analysis to help inform customer retention. It’s much easier to give customers what they want through a data-driven approach than by applying random guesswork and uninformed logic.
Effective Sentiment Analysis Strategies
Understanding and implementing effective sentiment analysis strategies is vital for improving customer retention. These strategies involve preprocessing and cleansing data, creating a comprehensive sentiment dictionary, and choosing the right sentiment analysis tools.
Preprocessing and data cleansing
Before effective sentiment analysis occurs, preprocessing and cleansing the data is essential. Preprocessing gets all of your data in a format ready to be analyzed. Techniques include lowercase conversion, punctuation removal, tokenization, word replacement, and eliminating stopwords. Automation ensures data integrity. This ensures that no rogue information is included in your analyzed data and allows you to get the best-quality outputs to best inform your customer retention strategies.
Creating a comprehensive sentiment dictionary
Create a dictionary of predefined words, accounting for all possible synonyms with which each word is associated. A lexicon is then applied, which ascribes a sentiment value by assessing several features based on mentions of words in the dictionary. This is the process by which sentiment analysis automatically determines the polarity and magnitude of customer sentiment.
Choosing the right sentiment analysis tools
Picking the perfect sentiment analysis tool is a key decision for customer retention strategy. Efficient sentiment analysis tools help monitor online discussions, assess text messages, and evaluate social media sentiment. By thoughtfully selecting the right tool, businesses can streamline their sentiment analysis endeavors, make more informed decisions, and, ultimately, enhance customer retention. For instance, Determ offers a user-friendly interface, advanced sentiment analysis algorithms, and a complete Sentiment trend dashboard where you can track sentiment through time, through different sources, based on location, key opinion leaders and track positive and negative conversational trends. It’s a friendly and effective choice to boost customer retention efforts!
Conclusion
In summary, sentiment analysis is a valuable ally in the quest for improved customer retention. By implementing techniques like data preprocessing, creating robust sentiment dictionaries, and carefully selecting analysis tools, businesses can gain profound insights into customer sentiment. This proactive approach not only mitigates the risk of customer churn but also fosters lasting customer loyalty. Embracing sentiment analysis empowers you to enhance the customer experience and ensure long-term success for your business.
If you’d like to find out how sentiment analysis can help your brand, book a demo with our experts and see how Determ can help you.
