Mining Amazon Reviews: A Guide for eCommerce Managers
Amazon reviews data mining is the automatic method of extracting vital insights from customer comments on Amazon through machine learning-driven text analytics and sentiment analysis. In an environment where disruptive technologies, rising customer demands, and innovative business models dictate the success of an enterprise, eCommerce companies need to keep evolving and developing effective measures to remain profitable. Read how Amazon reviews data mining can help eCommerce companies achieve this.
Why Is Amazon Feedback Important For Business?
Amazon reviews data mining is a very important source to find out the actual market sentiment around your brand and products. This is because numerical metrics of likes, dislikes, or star ratings don’t really give the true nature of customer satisfaction and product expectations.
For example, when we analyzed the reviews of a protein powder on Amazon, we found that even though the customer satisfaction score showed 77% 5-stars and 12% 4-stars, the overall positive sentiment score of the product was still 77% and not 89% (77+12) as the star ratings would suggest.
Through Amazing review analysis, we discovered that even though customers had given 4 stars and above to the product, they had mentioned cons in their comments. The sentiment analysis API picked up on these sentiments and therefore when calculating the positive, negative, and neutral scores, took them into consideration.
Apart from this, the API also gives other nuanced details from Amazon reviews data mining about sentiment trends through timeline associations, aspect emotion co-occurrence, and other business intelligence.
How Is Amazon Review Data Mining Done?
If you’re curious to know how a sentiment analysis platform actually gives you these valuable insights, here’s a brief outline of the process in layman’s terms.
The sentiment analysis solution uses machine learning algorithms to process the reviews in order to extract the important information from them for product innovation, customer experience analysis, and brand experience insights. It does so in four major steps.
Step 1: Data gathering
You can enter the Amazon review URL directly into the sentiment analysis platform’s dashboard. As soon as you hit Enter, the solution will immediately begin downloading the comments for analysis.
Step 2. Data Cleaning and Preparation
The software cleans the data by only accepting text and removing punctuations, redundancies, and other irrelevant material from the data. It, however, does analyze emojis as they are used to denote sentiment. It uses natural language processing (NLP) to cluster related topics and prepare the text for semantic analysis.
Step 3. Data analysis
In this step of Amazon reviews data mining, the API analyzes the data through many sequences such as multilingual data text analysis to recognize and extract information from comments that were in non-English languages.
It categorizes the data and discovers topics and aspects, which it analyzes individually for the sentiment. Therefore, like in the example of the protein powder, you will get customer sentiment for different aspects such as solubility, taste, packaging, price, and others. This way, you know how the market perceives your product with regard to every minor detail.
It is important to note that the API is first trained on the dataset meant for your industry. As the model analyzes more data, it becomes more and more intelligent over time.
Step 4. Insights visualization
Now that the platform has conducted all the Amazon reviews data mining under the hood, it will show the insights on the dashboard in the form of charts and graphs. It will show insights through various permutations and combinations that will give you in-depth details of every aspect of your product.
Apart from Amazon reviews, which are text-based, the sentiment analysis solution also has the capability to extract data from video-based content. This is how it can give important market insights for your e-commerce business through TikTok social listening or Instagram sentiment analysis as well.
What Are The Benefits of Amazon Reviews Data Mining?
You can use product and customer intelligence from Amazon reviews data mining in numerous ways to enrich your e-commerce business. This could be for operational improvements, bridging product-market gaps, enhancing customer experience, offering better payment solutions, or any other factor. A look at the top ways you can use sentiment analysis to take advantage of Amazon reviews.
1. Fine-Tune Your Services
Amazon reviews are a great source of finding out what it is about you that customers like the best, and what it is that you can do to improve your business. Feedback about customer care, website user interface (UI), user experience (UX) issues, terms and conditions, third-party marketplace functionality, shipping and tracking facilities, and various other facets of your business, can be harnessed to improve your operations and fine-tune your service.
2. Explore Partnerships
E-commerce companies can explore partnerships based on Amazon reviews data mining results that show the affinity between your and complementary products or services. Whether you are an Alibaba, Etsy, e-Bay, or a small and medium business, the same principles apply. Sentiment analysis applications can help you in finding the right alliance, whether you want to acquire other e-commerce businesses to expand your capabilities, or whether you want to use the insights to improve your logistics and supply chain, or add payment methods like payment installment solution PayBright to cater to a wider range of customers.
3. Enrich Your Social Media Strategy
Based on what intelligence you gather from Amazon reviews, you can choose to highlight the positives of your business through a well-developed social media strategy. This can involve the use of Influencer marketing, TikTok campaigns, as well as other social customer engagement tactics.
4. Use Feedback As Idea Generators
Sales and marketing tactics need to be driven by data and not by emotions. That’s why negative feedback must be welcomed and dealt with, with great consideration as they can have hidden gems of product innovation ideas. H&M, Abercrombie & Fitch, Target, and many others have had their share of negative feedback and product backlashes but used it to understand market dynamics and improve their offerings. As an e-commerce company can use insights through Amazon reviews data mining to find out who your regular customers are, who your social media customers are, how you can offer product differentiation for different customer personas, and what motivates each group.
5. Eliminate Need For Focus Groups
When it comes to market research for improving your service or products, this point serves well for both eCommerce businesses and otherwise.
In a study, per the Kellogg Institute, researchers found that Amazon reviews data mining insights were the same as the ones received from focus groups on almost all topics. This showed that businesses could stop depending on focus groups for product and service insights, which was really the most time-consuming of market research.
However, if you still feel the need to enrich your market research through personalized customer experience data, survey sentiment analysis can be of great help. This is because surveys can help you reach your target audience and get vital information for your marketing needs, and the results can still be analyzed in an automated manner.
Customer feedback is crucial to business innovation. Being able to extract meaningful data from hundreds of comments through Amazon reviews data mining can make all the difference in ensuring your market relevance as times change and the competition becomes savvier.
Insights that you receive from Repustate’s text analytics and sentiment analysis solution, Repustate IQ, can provide you with these and numerous other benefits that can boost your marketing and business strategies. Available as an API as well as a platform with a comprehensive sentiment analysis dashboard, the solution can understand 23 languages in the native tongue without the need for translations.