Sentiment Analysis To Connect With Customers

Customers are constantly giving feedback on products or services they have received. But how can retailers use this to create a better service? It can take time to track customer satisfaction rates and reviews. But there are benefits to analyzing customer sentiment. This blog will look at what sentiment analysis is, and why retailers need it to connect with their customers.

What is Customer Sentiment Analysis?

Customer sentiment is how customers feel about products and services. You can find it in reviews, surveys, or even on social media pages— anywhere there is communication with customers. It can be measured using a scale as simple as positive to negative.

Alternatively, emotions such as happiness, excitement, frustration, and anger can be analyzed to give companies an idea of how their product has been received.

For example, in a survey ranking CRM call center software on how easy it is to use, customers may be asked to rate it out of 10. There may then be a section where customers can leave comments.

When analyzing this data, the rating could be used to rank the product against previous software or similar products created by competitors. Keywords and phrases could be picked out of the comments section to create a picture of how the product makes customers feel.

Sentiment Analysis Tools

To use sentiment analysis effectively, you need to keep up to date with customer sentiment. Customer opinions can change quickly, and it can be difficult to analyze a never-ending stream of data. Online sentiment analysis tools are the most effective way to collect and analyze data. They can track keywords on certain platforms and analyze data in real-time to give you an accurate picture of customer satisfaction rates.

AI-Based Sentiment Analysis

As artificial intelligence (AI) is getting smarter, we are finding more ways to use it to give customers a better experience. With AI optimization, you can track and prioritize issues, as well as using it to find out more about customer journeys. AI-based sentiment analysis can help you gain greater meaning from your customer sentiment data. As it learns, it can pick up tones of voice and hidden meanings in language. Usually, only humans would be able to do this.

Why Do Retailers Need Customer Sentiment Analysis?

Track Customer Satisfaction

High customer satisfaction rates are what keep them returning. Sentiment analysis will keep track of satisfaction rates so you can see how well your product has been received. It will also give you insight into exactly what products or services are making the customers happy and how. This is valuable information to keep satisfaction rates high.

Improve Customer Service

Customer service needs to be adaptive to customers’ needs. It’s not just about keeping them happy. You need to know what to do when they are unhappy. You can use data from complaints, returns, and comments from unsatisfied customers to train chatbots and colleagues to deal with unsatisfied customers better.

Product Development

Sentiment analysis can help gauge customers’ reactions to new products before they are released. It allows you to make those minor tweaks before your product goes up for sale.

Crisis Management

It is best to deal with problems before they get out of hand. Sentiment analysis can allow you to monitor live chats to determine if issues are being dealt with properly. Also, you can track negative keywords online to find damaging content before it gains momentum.

Improve Brand Awareness 

Data is a crucial part of any marketing strategy because a company can use it to find out what strategies worked in the past and which didn’t. Customer sentiment analysis is ideal for detecting changes in customer opinion as well as finding trends online. This helps improve brand awareness as you know what your potential customers are looking for. This means you can create better marketing strategies and more relatable advertising.

Reduce Customer Churn

It may only take one bad experience for a customer to leave a subscription service or stop using a product. For example, suppose customers are suddenly looking for alternatives to transcription as a company. In that case, you should check if there have been damaging reviews or if customers have found any problems that need resolving.

Where to Find Customer Sentiment

  • Reviews: Review websites are one of the best places to look for customer sentiment. But look out for fake and bot-generated reviews.
  • Surveys: You can create these to get customer feedback on new products or a service that you provide.
  • Social media: This can give you an overall idea of what your customers think of your products and brand. 
  • News: Analyzing news websites and blogs can give you a better idea of how your brand is perceived. You can also use a sentiment analysis tool to track these websites and alert you if certain negative words are used when talking about your company.
  • Live chat: Chats can be analyzed in real-time to help you prioritize important customer problems. You could also include a short survey at the end of live chats to give customer service feedback.
  • Customer relationship software (CRM): CRM is a crucial sales tool that holds the information on your customers’ journeys.

3 Steps of Customer Sentiment Analysis

1. Choose Your Model
  • A rule-based model: This is the easiest way to do sentiment analysis. But this does mean that you can miss more complicated sentiments. This model involved data labeling— creating a list of positive and negative words and phrases to track. This can be done manually or using a sentiment analysis tool. But, as you can see, this doesn’t leave much room for interpretation. In this example, it could wrongly interpret a positive review as negative.
Image made by author. Click to view larger
  • An AI-based model: This model takes longer to set up, but will be more useful in the long run. The AI needs to be trained to analyze customer sentiment. But the more text it sees, the more efficient it becomes. Once it is up and running, it will be fully automated and will gather and analyze your data.
2. Gather Your Data

Once you have chosen your model, you need to gather your data. You can use web scraping to extract content from websites or automate your data gathering. Whatever your method, you need to be thorough. For example, your social media sentiment analysis should cover all platforms your company and your customer use to get a realistic view of your brand.

3. Analyze and Evaluate

You should constantly evaluate your customer sentiment analysis tools to fix any inconsistencies. AIs are smart but may need retraining to deal with changes in how people communicate online. Rule-based models will also need updating with new words and phrases. Also, look out for fake reviews that could ruin your data. These could come from bots or competitors who are intentionally trying to mislead.

Use Sentiment Analysis to Connect with Your Customers

There is so much customer sentiment data out there. You can leverage it to connect with customers and optimize their journeys. Shoppers just want companies to listen to their opinions and respond to their feedback. From social media to review websites, retailers can tap into customer sentiment to improve their businesses.

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Grace Lau

Grace Lau

Grace Lau is the Director of Growth Content at Dialpad, an AI-powered cloud communication platform for better and easier team collaboration. She has over 10 years of experience in content writing and strategy. Currently, she is responsible for leading branded and editorial content strategies, partnering with SEO and Ops teams to build and nurture content. Grace Lau also published articles for domains such as UpCity and Soundstripe.

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