Using Sentiment Analysis to Improve Customer Experience in E-Commerce

Last Updated August 17, 2020

Get to know your customer better than ever before—with multilingual sentiment analysis.

Whoever said all publicity is good publicity obviously lived in a time before social media. The fact that people are talking about your brand means nothing unless you understand the tone of that conversation.

Knowing what people think and feel about your products and services allows you to act quickly to tailor your offering to better align with the needs and wishes of your customers.

But how is it possible to get inside their minds to discover what they’re really thinking?

Sentiment Analysis: Turning Online Chatter into a Business Advantage

Millions of online shoppers regularly share their views on social media and review sites. Scrutinizing this publicly available customer data allows patterns to be detected and a picture to be compiled of your customer’s mindset. This process is referred to as sentiment analysis.

This involves the use of artificial intelligence (AI) to collate text data from a variety of sources, identify opinions, and classify the findings into a positive, neutral, or negative response to a product, service, or brand.

While customer surveys have always existed in retail, the growth of e-commerce has accelerated the science to a new level of sophistication, involving precision-targeted processes designed to dig deeper into customer attitudes.

These include:

  • Fine-grained: Fine-grained sentiment analysis is the detection of positive or negative sentiment at the phrase or sentence level. It begins with a simple binary sentiment—good or bad—that can be divided into more detailed sentiments—very good, good, neutral, bad, or very bad—depending on the application.
  • Emotion detection: Emotion detection is used to detect emotional states within a piece of text, including happiness, sadness, and frustration. Typically, this is achieved with lists of relevant words and machine-learning techniques.
  • Aspect-based: Aspect-based sentiment analysis recognizes public opinion regarding a specific aspect of a product or service—such as the battery life of a smartphone—to identify what customers consider its pros and cons.
  • Intent: This takes sentiment analysis a step further by identifying what intention is being expressed in the text. For example, is the customer sharing a query, an opinion, or a complaint?

The Benefits of Sentiment Analysis for E-commerce

By putting these various methods to use, online retailers can acquire the information needed to:

1. Improve customer experience

Using sentiment analysis to reveal customer attitudes allows you to deal with resistance to your brand, products, and services head-on.

This improves your customer’s purchasing journey and their overall impression of your company, which means they’ll want to visit again. They’ll also want to share their positive experience with others.

2. Gain a competitive advantage

Sentiment analysis provides continual feedback on where your company stands in relation to your competitors. More specifically, it identifies precise areas where you outperform rivals, and where you fall short.

Take customer support, for example. People who have grown up with 24/7 internet availability and handheld smartphone convenience do not expect to be kept waiting days for a response to a query. So, if your competitors are responding to customer queries faster than you, sentiment analysis lets you know promptly, so you can take quick action to deal with it.

3. Predict the future

By evaluating the popularity of products and features and the tone of language used when commenting on them, sentiment analysis makes it possible to identify not only what’s hot and what’s not, but also what’s only just beginning to heat up.

The ability to make sales and campaign adjustments according to real-time data ensures you’ll be ready for the latest trends before they happen.

4. Open new markets

As well as identifying emerging trends, sentiment analysis also helps you detect developing new markets. Research what people are talking about highlights their needs, their frustrations, and their passion points, all of which opens up the possibility of targeting previously untapped customers.

5. Build a better brand

Sentiment analysis helps you hone your products, services, and personality. Over time, this builds a reputation of a company that is ahead of the game, responsive to customer needs, and in tune with the mood of the moment.

A strong and attractive brand like this gets noticed, draws in more customers, and increases positive chatter across social media and beyond.

6. Support shopper research

A large part of the online shopping experience involves pre-purchase research by the customer. Analyzing customer feelings and frustrations at this early stage allows you to address the needs of potential shoppers throughout this crucial decision-making process.

For example, this could be accomplished by providing convenient product summaries tailored to meet the needs of prospective customers.

7. Engage with customers

Capturing the tone and subject matter of conversations in your customers’ online communities allows you to connect with your audience through several online channels. Online engagement brings a whole range of benefits, including the ability to drive consumers to your website.

Overcoming the Challenges of Sentiment Analysis for E-commerce

Information that isn’t available in a traditional row/column database—such as email messages, telephone transcripts, tweets, blog posts, and Facebook messages—is known as unstructured data.

According to projections made by IDC, it is estimated that by 2025, 80 percent of the world’s data will be unstructured. The nebulous nature of unstructured data makes it harder to collate than the more accessible structured data found in traditional databases.

Sentiment analysis, however, can be paired with artificial intelligence (AI) algorithms that transform unstructured data into accessible insights. These algorithms ‘tag’ each piece of data with a classification label that makes it recognizable to sentiment analysis. The findings can then be evaluated just as with standard structured data.

The multinational nature of e-commerce demands sentiment analysis that operates multilingually. While sentiment analysis in the English language has improved dramatically over recent years, the same cannot be said for non-English languages.

However, a 2014 study showed that by combining sentiment analysis tools with machine translation, processes can produce “sentiment analysis results with significant precision.”

Getting Started with Sentiment Analysis

As a company with expertise in artificial intelligence, machine translation, and sentiment analysis, Summa Linguae Technologies is ideally placed to overcome the potential challenges of analyzing and acting on customer attitudes, whatever their native language.

Summa Linguae Technologies CEO, Krzysztof Zdanowski, describes a typical project:

“SLT were asked to analyze and tag sentiments for multiple datasets with up to 400,000 messages, each in 9 different languages. Following a comprehensive solution involving nearly 200 resources assigned directly to the project, the client was able to build a unified platform in various languages to listen, engage, and measure customer attitudes across 25 social and 10 messaging channels.”

Contact Summa Linguae Technologies now to discover how data-savvy e-commerce companies can deploy sentiment analysis to gain essential insights and optimize the customer experience across their entire shopping journey.

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