Feedback Analysis

Why Everyone is Talking About Sentiment Analysis Using Product Review Data

Customer Sentiment Analysis

How can consumer brands effectively mine data from product reviews and ratings?

Over the last few years, brands have started using  “Voice of Customer” data to make better decisions and enhance consumer experiences. Sentiment analysis using product review data has emerged as a great way to find these Voice of Customer answers. This process helps brands find useful insights directly from customer feedback, ensuring their products and marketing match customer needs and expectations.

So why review sentiment analysis? 

People love to talk about the products they buy and use. When consumers feel strongly about something, they want to share their opinions and experiences, not only with their friends but with thousands of internet strangers. These reviews can tell you so much!

A decade ago, you may have called reviews free marketing. But today, customer reviews and ratings can serve an even bigger purpose: Strategic guidance for your brand.

Consumer needs and trends are changing quicker than ever, and brands need up-to-date data to keep up with them. The customer-generated review data at brands’ disposal offers the most valuable and readily available resource for continuous market research.

Keep reading to learn more about sentiment analysis and how it can make a huge impact on your metrics.

The Increasing Relevance of Product Reviews for Consumer Brands

Utilizing the full potential of online product reviews goes deeper than whether or not consumers like a product. When used correctly, the sum of consumer review data can provide 360-degree insight into what consumers think about your product, your competitors' products, and the market as a whole, down to granular detail.

Analyzing specific changes in sentiment allows brands to understand what elements of a product or experience are causing shifts. Uncovering specific drivers for positive or negative direction enables quicker, and more targeted action.

Beyond visibility into the factors that impact your brand, customer sentiment analytics provide in-depth insight into specific competitors as well as the competitive landscape as a whole. Understanding drivers of change across the market as well as tracking performance over time allows brands to prioritize initiatives and maintain a competitive edge.

Analyzing the “why” behind consumer preferences and changes over time gives brands the opportunity to act on trends, enhance messaging, and identify white space in the market. Dialing into consumer feedback enables brands to answer questions like:

  • How should you update your PDP on a specific retailer to maximize conversion rates?
  • Which specific features should your product team focus on first to address your customers' feedback and improve sentiment?
  • Why did your product's sales dip at a specific time and which retailer was underperforming the most?
  • What topic of conversation is trending on an individual SKU compared to its direct competitors within a set period of time?
  • What product categories should your upcoming brand launch focus on to gain a competitive advantage?

The Difference Between Aggregation and Insights

On their own, reviews and ratings don’t help companies decide what actions to take or lead to winning strategies. Just as compiling data with an aggregator doesn’t show insights, give depth of data, or impact KPIs. Data without organization, without a system, is chaos. 

Review aggregation software does a great job of syndicating reviews and collecting everything in one place. But these platforms aren’t built to answer questions or provide business insights beyond monitoring the quality and quantity of reviews.  

Review sentiment analysis offers so much more.

Powered by artificial intelligence, review sentiment analysis offers an effective tool to help companies understand their customer base, and it's changing the game for brands like Microsoft and Johnson & Johnson.

Using Natural Language Processing to Unlock Review Analysis 

Natural Language Processing (NLP) describes how computers learn and understand human languages. NLP functions as a specific type of machine learning that processes text-based data. Consumer brands with large sources of user-generated content need these processes to mine data effectively and turn large amounts of qualitative review data into quantitative data.

And that’s where Yogi comes in! 

Using NLP and AI to aggregate and analyze reviews from across the web, Yogi breaks down reviews and ratings into usable sentiment data, providing unmatched insight into how shoppers feel about products down to granular features and attributes, showing trends as they emerge. 

But what is sentiment analysis?

Learn more about the NLP method used by Yogi’s product review insights software, K means clustering.

Sentiment Analysis Using Product Review Data

Sentiment analysis refers to how analysts or analysis tools look for the feelings behind text. This information then helps businesses observe conversations about their brand, product, or service. Sentiment analysis relies on customer review data to understand how consumers socially engage.

Sentiment analysis uses NLP to break down reviews and classify them as positive, negative, or neutral. Sentiment analysis can be done for entire reviews or, a tool like Yogi can dive in for additional granularity, determining which reviews have sentiment around specific features, attributes, or concepts.

Sentiment analysis creates an understanding of how consumers feel at the base level about a company, product, or feature. When done correctly, shopper sentiment analysis provides an in-depth look into product and brand perception at any level required.

Yogi uses customer sentiment analytics to help Marketing, eCommerce, and Product teams improve their PDP, marketing campaigns, and product roadmaps to better align with their customers. On top of advanced analysis abilities, Yogi leverages in-app business visualizations and BI integrations to easily pinpoint market trends, and even benchmark your shopper sentiment against competitors.

Generating analysis of keywords, themes, NPS, and other customer patterns allows for highly specific insights.

The Limitations of Sentiment Analysis

Sentiment analysis can be tough. Some software can’t read between the lines when people use sarcasm or irony. To take a text at face value, one might interpret a comment as “good” if it uses typically positive phrasing. But perhaps the commenter actually wrote a negative statement using sarcasm. 

Advanced sentiment analysis tools like Yogi detect the nuance of what someone really means within its context. This is where NLP and AI used for sentiment analysis is able to create something really special. 

Yogi uses many tools to accurately analyze product reviews in context. These methods provide different insights that work together to paint an overall picture:

  • Sentiment Analysis 
  • Intent Analysis
  • Contextual Semantic Analysis

Human communication contains endless complexities. Written languages express thoughts and feelings in strange and unique ways. Yogi cuts through that, understanding the product reviews empathetically and turning that into accurate sentiment data. 

Want to learn more? See Contextual Sentiment Analytics in Action

Studies show how to apply sentiment analytics methods to product review data. This particular study relied on customer review data from Amazon.  

The researchers demonstrated their findings using two levels of categorization:

  • Sentence-level categories
  • Review-level categories

Use Product Review Analysis to Improve Your Business

At Yogi, we have seen firsthand that review sentiment analysis can provide brands with the most granular and intimate levels of visibility into the thoughts and feelings of their consumers.

Click here to see the strategic insights our customers use Yogi to discover every day.