The internet has revolutionized the way we make a purchase, especially in the retail eCommerce world like Amazon where experiencing products is usually not feasible. Therefore, customers largely rely on product reviews to make up their minds for better decision-making on purchases. 

Precisely, on an average each user provides 5 reviews and the most reviewed user have given 430 reviews for different products.  

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Here, Amazon Review Analysis comes as an invaluable advantage that AI and Machine Learning have given to businesses. A person can easily publish his own book through Amazon and not go through the anxiety of convincing a publisher to do so. Amazon review data analytics can tell companies a lot about their products, even elements that they might not have thought of. 

How is Sentiment Analysis Using Amazon Product Review Data Done?  

Precisely, sentiment analysis API uses Natural Language Processing (NLP) tasks to not only identify aspects of the product from the Amazon review but also allow brands to look beyond the star ratings. Amazon review data analysis provides insightful customer information that can be put to use for product betterment.   

Let’s find out how! 

#Step 1: Create Project and Insert URL

To start with, create a project. Afterwards, collect the data from the URL from Amazon reviews and upload it on the sentiment analysis platform. 

#Step 2: Sentiment Analysis 

Right, after the API fetches the data, it is analyzed to see if the Machine Learning based sentiment analysis gave the same inputs as the reviews the customer leaves on the website.   

For example, The analysis presents a 77% positive sentiment in tune with the product review received on Amazon. This proves to be compelling because the star rating of the product showed a higher positive sentiment as more than 80% of the reviews were positive. This is the kind of data you’ll require for genuine products and consumer insights. 

#Step 3:  Amazon Review Analysis 

Further, the reviews are scrutinized to analyze the qualitative sentiments beyond the numerical star ratings. For instance, Amazon data analysis showed that the comment mentioned under a 4-star review had several negative mentions of the product. This is where sentiment analysis processes those comments for their actual worth in the context of sentiment, i.e going beyond numerical data. 

#Step 4: Visualize The Data 

All the sentiment analysis insights gathered are presented on the customer experience dashboard with comprehensive details. You can easily observe the different aspects that the API extracted along with their related emotions. 

Also, the platform allows you to see the actual comments under the positive and negative color-coded bars. These insights and more can be used by brands to better understand their audience. Even more interesting is that they can even do a historical analysis of the data and observe the market and seasonal trends related to the specific product. 

Why Is Amazon Data Analysis Important? 

Amazon review data analysis is beyond any price for brands because the reviews are written by real product users, i.e the first-hand experience. This data proves to be a treasure trove for brands because people can be quite vocal about their experience and provide crucial information about the positives and negatives of a product, consisting of its delivery and the customer service they received. 

This also, unfortunately, led to fake reviews spearheaded by firms. Often, this is an unethical practice that shortchanges the public.  

Another unskippable reason is that even though a product received 4 stars, it doesn’t necessarily mean that the product was excellent. Diving a little deeper into the comments shows that there are several negative feedback that the review mentions despite the fact that people have given a higher star rating and vice versa. 

To address this issue, AI-powered review data analysis and social media listening are crucial for understanding and discovering customer and product insights. A sentiment analysis API used NLP tasks not only to find different aspects of the products from the Amazon review but also to enable brands to go beyond star ratings. 

Following this approach allows Amazon review analysis to help brands reach out to customers in a more engaging and effective manner while giving rich product insights. 

What Kind Of Insights Can Be Derived From Amazon Review Data Analysis?

Brands can easily identify custom topics, discover market trends, proactively manage brand reputation, look for product issues and analyze where they stand in comparison to competitors, and more through Amazon review analysis. 

Let’s look at them in detail: 

• Find Custom Topics 

Using Amazon review analysis can help brands identify topics, substopics, and aspects for which there can be not just negative and positive feelings but also an amalgam of emotions expressed. 

• Automated Topic Grouping 

A blend of NLP and sentiment analysis works together to clutch topics while conducting Amazon review data analysis. These then automatically group topics into custom categories like sturdiness, ease-of-use, convenience, etc. Even, if the format of the reviews is video-based, the feature of video content analysis treats it in the same manner as a text-based comment and analyzes it for topic classification. 

• Sentiment Analysis 

Mining the emotions from reviews is what marks Amazon review analysis a holistic approach than just numerical analysis. Qualitative data provides richer insights, because it tells you why something occurred or can motivate you into questioning why something is taking place. 

Notably, it allows you to get to the bottom of an issue highlighted by people, it’s easier to sort out the root of the problem. 

• Improvised Customer Support 

At the time, when you can determine what’s irking people about your product and the service they received, it becomes a lot easier to get to the root of those issues and set up a team to investigate the problem. 

You may even require to set up a better customer care management system to make sure customers don’t face inconvenience. Indeed, you can offer replacement services better, or also refund quicker. 

• Uncover New Trends 

With Amazon review analysis it’s easy for brands to discover numerous trends that would otherwise be hidden. The trend portrays the dips and rises in the sales helping to scrutinize the right strategy at the right time. 

• Find Concerns 

Whether that’s a need for better packaging, or a wider range of sizes for your product, your website needs an overhaul to cater to customer emails better- there are several things you can address to make sure that your product keeps bringing customers in. 

• Competitors Intelligence

You have access to analyze thousands of comments on Amazon regarding your product. But, you also have access to do the same for your competitors to see what they are saying about your product. 

You can take the data and compare it with what consumers feel about your product or services. Is there any way out to retain customers? Do you want your product to be available in different sizes for different demographics? 

All these insights can be at your fingertips with Amazon review analysis. 

• Manage Brand Reputation 

Undeniably, it’s a lot easier for companies to keep track of their online brand reputation with Amazon review data analysis. Though some brands choose unethical ways to address this issue through fake Amazon reviews, this is not a long term solution and in reality a chaos for the companies following this practice. Eventually, they lose customer support and confidence and are black-listed by customers. 

But, by following sentiment analysis of reviews, you can get backed by information about your brand’s perception in the market. This not just enhances your brand image but also allures customer loyalty. 

• Product Enhancement 

With all the gathered information and insights about the product on Amazon, you can easily see a trend showing what product enhancements your customers are demandingly looking for. You can explore product off-shoots that are complementary to your current products, or you can decide to tie up with another company to make sure your product is not overlooked because of some inconvenience. 

To be precise, this is what IKEA actually did, and it’s much bigger and better than ever before, irrespective of the bottleneck competition. 

• Review Scale 

Amazon review data analysis allows you to review your metrics and evaluate them based on periods. Besides, you can choose to measure the metrics for a specific time and compare them with a time span after a product enhancement has been integrated. And, just because these metrics are both qualitative and numerical, you get in-depth information about what you did right, and what can be improved. 

Wrapping Up:

Despite the fact where you are in the world, you will certainly have customers writing about you on Amazon. So, all you need is a reputed sentiment analysis tool that can easily distinguish between positive and negative reviews. 

Moreover, make sure you have a trusted sentiment analysis tool that can analyze several languages natively and does not use translations to read comments and reviews and have search inside video features.