9 min read
Apr 29, 2024
To succeed in the competitive ecommerce field, sellers must always have their finger on the pulse of their customers’ opinions. As an ecommerce retailer, it works to your advantage when you can track how customers and potential buyers feel about your brand and store.
This is why sentiment analysis is one of the most valuable tools in an ecommerce business kit.
In this article, we will look at the central role sentiment analysis plays in ecommerce communication, discover its uses, benefits, and challenges, and look at ways you can incorporate this useful technology into your business plan.
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What is sentiment analysis in ecommerce?
Sentiment analysis is the process of extracting data about the emotional content in a body of text. Another term for sentiment analysis is opinion mining, which captures the essence of the exercise. While being able to gauge the opinions and emotional context from text comes naturally to most human beings, it used to be a challenge for software to do the same.
Advancements in artificial intelligence (AI) and machine learning (ML) have bridged that gap. Natural language processing (NLP), a branch of AI and ML, has made it possible for computer programs to scan and process text to deduce emotional meaning—something close to human understanding. Thanks to NLP, it’s possible to analyze massive sets of text data, categorize documents according to sentiments, and extract valuable information from every instance of customer communication.
Most ecommerce customers are looking for a personalized experience. As a seller, you must pay attention to the power of sentiment analysis in creating a unique customer experience for shoppers. It is the best way to map out your audience’s likes and dislikes, identify your customer’s pain points, and track how your target audience views your brand.
Where to Look for emotional insights with sentiment analysis
The internet is overflowing with ecommerce text data. Every day, online shoppers upload reviews, make social media posts, and write to customer support accounts with their complaints and requests. It’s too much for a single employee or customer service team to handle on their own, which is why AI-powered NLP programs are such an asset. They can process large volumes of text quickly, making them the ideal tool for tracking critical text data generated through ecommerce activity.
To gather text data for an opinion mining exercise, you should look in the following places:
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Reviews
Product reviews are the first place to check when analyzing customer sentiment toward your store. On the surface, a review is simply a summary of a buyer’s experience. But if you look a little closer, you will see that even the most perfunctory review contains several valuable data points you can use.
Performing sentiment analysis on reviews lets you know which products are popular with customers, what kind of items appeal to which segment of your audience, and why people choose to shop at your ecommerce store.
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Social media
Social media platforms have taken the place of diaries and personal journals in 2024. It’s where internet users go to share their opinions and thoughts, including what they think about various products and brands. Tracking your mentions on social media ensures you will always know how the market perceives your brand at large.
Other data points you can extract by analyzing social media posts include audience demographics, product use cases, and comparisons to competitors. Consumers on social media are largely prolific and outspoken, making these platforms ideal for robust opinion mining.
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Customer communications
An area where sentiment analysis takes on even greater importance is customer communications. Whether through email, feedback forms, or chat conversations, your customer service team must intuitively respond to each customer’s emotional state.
Every interaction between a customer and your brand affects their emotions, and sentiment analysis will help you manage them effectively. Customers exhibiting negative sentiments might require closer attention, while conversations with satisfied customers can be safely closed.
Benefits of using sentiment analysis in ecommerce communication
As the amount of text data available online continues to grow, businesses are realizing the true value of sentiment analysis. A report found that 52% of large companies had adopted sentiment analysis software in 2022. With the sentiment analysis market expected to achieve a compound annual growth rate of 13.6% until 2031, the share of companies using sentiment analysis software has further increased.
There are many reasons why businesses are adopting sentiment analysis software at this fast rate. Let’s look at the most compelling reasons you should integrate sentiment analysis into your ecommerce business plan.
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Predict customer behavior
The advantage of sentiment analysis is that it demystifies customer motivations. It is meant to uncover why people prefer certain products or features.
You can better predict future demand patterns when you can pinpoint why customers buy or avoid one of your offerings. You can track customer sentiment toward your inventory in real time and adjust your sales strategy based on your findings.
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Build a positive brand reputation
By monitoring customer sentiment, you can deliver more meaningful solutions to customers when they really need them. Sentiment analysis helps companies be attentive and responsive to their customers’ needs, giving your brand a reputation for being caring, helpful, and committed to their satisfaction.
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Gather important business data
You can also use sentiment analysis to uncover useful information about your current customers, which can then be used to convert potential buyers. When you know why your existing customers flock to your shop, you can emphasize those features and services when marketing to newer leads and markets.
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Improve overall customer experience
The modern ecommerce consumer is looking for a personalized shopping experience tailored to their preferences. Sentiment analysis is extremely valuable in curating customers’ online experiences.
By using sentiment analysis to deliver product recommendations and exclusive offers, you can offer each customer an elevated experience that appeals exactly to their taste.
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Implementation strategies for sentiment analysis in ecommerce
If used correctly, sentiment analysis can be a major asset for your ecommerce brand. Ecommerce sellers have several ways to integrate sentiment analysis into their business model, each of which adds value in its own unique way.
Let’s look at the various ways to do so:
Sentiment-based product recommendations
Basing product recommendations on customer’s past likes and dislikes is the most obvious application of sentiment analysis.
For example, if you run an ecommerce clothing store, you can track how customers feel about different designs. If someone leaves a positive review for a Hawaiian shirt in your collection, you can recommend more colorful shirts in the future. It’s the classic “If you liked X, then you will like Y” proposition that everyone who shops online is familiar with.
Emotionally intelligent chatbots
Chatbots are a valuable tool for customer communications, especially for large businesses with audiences in the thousands. Even so, over 70% of customers are left unsatisfied with their chatbot experience. This can be chalked up to the emotional dissonance that arises when computers attempt to communicate with humans.
Sentiment analysis and NLP help train chatbots to respond more empathetically to customer conversations. By gauging the tone and emotion behind each response, these virtual customer agents become better at resolving queries, providing recommendations, or escalating issues to the human support team.
Social media listening
As mentioned earlier, tracking social media mentions give you a window into what people feel about your brand. You can use these insights to improve your services and offerings.
For example, customers may talk about how your products are good quality but quite expensive. If you are aware of this sentiment because of social media listening, you can announce a special sale or promotion code in response, which will help increase sales. Monitoring social media conversations lets you fine-tune your marketing strategies and customer outreach initiatives.
Tailoring recommendations
Sentiment analysis is instrumental in one-on-one interactions with customers but can also improve the experience of the entire audience. Tracking customer opinions refines your own recommendation engine by identifying products that evoke positive sentiments among customers.
Consider this example: someone buys a portable speaker from your store and leaves a review saying they are impressed with the battery life. When another customer is looking specifically for a speaker with a long battery life, the earlier review will be valuable in recommending products.
Sentiment analysis provides key data points that can enhance the effectiveness of collaborative filtering used by recommendation engines.
Building emotional profiles
Over time, sentiment analysis helps ecommerce brands know their customers better. Rather than simply learning what they buy, sentiment analysis also sheds light on why they purchase particular items. This captures their emotional preferences, which can be used to deliver an even more personalized and enjoyable shopping experience.
For example, if a customer has only bought hardcover books from your store, they display a clear emotional preference. Thanks to sentiment analysis, your recommendation system will prioritize hardcovers over paperbacks whenever they look up books in your online store.
Maintaining such emotional profiles for each customer helps you keep every shopper happy and coming back for more.
Successful implementation of sentiment-driven recommendations
Sentiment analysis can be usede in real time or to look back to measure its performance retrospectively.
Thousands of companies rely on sentiment analysis behind the scenes. Let’s look at a prominent example of how it played a significant role in a successful campaign.
Gillette: The best a man can be
In 2019, men’s shaving brand Gillette launched an advertisement challenging toxic masculinity, which received a harsh response from certain sections of the media. While the criticism of the campaign was extremely vocal, its effect on Gillette’s brand perception did not match up when sentiment analysis was applied.
Studying the Twitter responses to the campaign yielded data showing that 48.20% of responses were positive and 32.50% were negative. Positive responses received more engagement on Twitter than negative ones.
This proved to be an endorsement of Gillette’s decision to launch a value-based marketing campaign despite the backlash from a subset of its male-dominated audience.
Opportunities for ecommerce innovation
In the future, sentiment analysis is going to play an even more prominent role in shaping consumers’ online experiences. AI-enhanced and NLP-capable recommendation systems will be able to direct customers to products they will enjoy not just on a functional level but emotionally.
Of course, certain challenges need to be overcome. Current obstacles to sentiment analysis’ effectiveness include:
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Overcoming ambiguity in text data.
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Designing multilingual sentiment analysis software.
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Collating unstructured data with AI algorithms.
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Optimize your sales process. Leverage customer experience for revenue growth.
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Sentiment analysis in ecommerce communication: The new frontier
As sentiment analysis grows more advanced, ecommerce communication will become more streamlined and productive for buyers and sellers alike. Incorporate this valuable technology into your business plan to take your operations to another level and stay ahead of the competition.
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