Did you know that online product reviews influence 69% of consumers? Compared to the high percentage of customers utilizing this information to their advantage, the amount of companies using the information is very small.Many organizations use first-generation reviews & ratings tools to aggregate reviews and glean high-level insights, but they don’t realize that simple keyword breakdowns and average star ratings only make up the tip of the iceberg. Basic analysis of review and rating data will only give you a limited understanding of what your customers are thinking.To get the most value from this data, deep learning techniques like natural language processing (NLP) contextual analysis is the best solution.In this article, we will show you how deep learning can help you extract more insights from review sentiment analysis. We will also compare it to the answers you are currently getting from basic analysis methods.
Natural Language Processing (NLP) is a field of computer science and linguistics that deals with the interaction between computers and human (natural) languages, such as written text. In its simplest form, we can use NLP to convert one natural language to another automatically. However, more sophisticated applications of NLP include:
NLP algorithms are typically based on statistical models that learn from a corpus of training data. We can then use these models to analyze new pieces of text, taking insights from the near-human level contextual analysis of each word and sentence. In this way, NLP can be used for a variety of tasks, such as:
Review sentiment analysis is the process of extracting opinions and emotions from customer reviews. This can be useful for businesses that want to understand how customers feel about their products or services on a granular level.Review sentiment analysis can be performed using manual or automated methods. Manual methods involve reading through customer reviews and coding them according to sentiment (positive, negative, or neutral).Automated methods use natural language processing algorithms to identify sentiment in reviews. Some businesses use review sentiment analysis to track customer satisfaction over time or to compare different products or services. Others use it to understand how customers feel about specific aspects of their business, such as customer service.
Sentiment Analysis determines whether a piece of writing is positive, negative, or neutral.It's used in a variety of fields, from marketing to customer service to politics. And it's often used to analyze reviews, such as those on Amazon and large retailers such as Target and Walmart.Traditionally, Sentiment Analysis has been a time-consuming and manual task requiring hours of data analysis, but with the advancement of Natural Language Processing (NLP), it's now possible to automate the process.And when it comes to Sentiment Analysis, we can use NLP to contextually understand the emotions expressed in a piece of text. We can use this understanding to automatically classify the text as:
NLP-based Sentiment Analysis is more accurate than traditional methods, and it's much faster too.
There are two main types of data analysis: basic analysis and deep analysis. Basic analysis is the process of looking at data in order to find high-level trends and summarize information.On the other hand, deep analysis involves an in-depth examination of data to pinpoint specific and actionable insights that can be used to inform decision-making.Basic analysis is typically quicker and easier to do, but it can be less accurate than deep analysis. Deep analysis is often more time-consuming and complex, but it can provide insights that would be missed with a basic analysis.
To stay successful or gain a competitive advantage, organizations are always looking for ways to improve products and messaging. One way to do this is to stay up-to-date on customer feedback by conducting regular analyses of online reviews.With the current tools available to help organizations do it all from aggregation, responding to reviews, syndication, and analysis, there is a base level of information available related to customer sentiment.Typically, these tools show review volume, as well as what keywords are showing up the most. This information can be very insightful, helping you to identify which products are top performers and which might need some improvement.With this information, organizations can answer questions such as:
One of the main problems with basic review analysis is that it can be difficult to get an accurate picture of customer sentiment. This is because star ratings and keywords don’t paint a complete picture of customers' thoughts and emotions about specific product attributes and themes.This means that the review might not accurately reflect the product itself.Additionally, this level of analysis leaves out competitor data which can be advantageous for coming up with messaging claims, understanding what customers want, and identifying white space in the market or areas for competitive advancement.Finally, review analysis can be biased by the person conducting the analysis.
Deep R&R analysis using NLP tools like Yogi can help you answer key questions about your business, such as how to update your PDPs to narrow the gap between you and your competitors. This analysis can also help you identify which specific features your product team should focus on to address customer feedback and improve sentiment.Additionally, deep R&R analysis can help you determine why your product's sales dipped at a specific time and which retailer was underperforming the most.Finally, this type of analysis can also help you identify what topic of conversation is trending on an individual SKU compared to its direct competitors within a set period of time. You can gain a competitive advantage in your industry by understanding the answers to these questions.Questions you can answer with online product Reviews & Ratings analysis using NLP:
Deep R&R analysis addresses the problems of basic review analysis by providing a complete and comprehensive picture of customer sentiment across a variety of themes and competitors. This is because deep R&R analysis uses NLP to analyze reviews and ratings, generating contextual insights on a sentence level.Deep R&R analysis can is conducted on a market-wide scale. This means that it can compare customer sentiment across brands and products and identify trends over time.Finally, deep R&R analysis is not biased by the person conducting the analysis. The NLP algorithms used for deep R&R analysis are unbiased and objective.
Yogi’s K-means-based AI platform analyses Reviews & Ratings at the deepest level possible across all competitors and retailers. Save time searching for insights so you can move the needle for your brand today.