The value of Amazon review & feedback analysis
Optimize product quality
From Amazon review & feedback analysis, we can easily extract the reasons why consumers are dissatisfied with our product, such as problems, complaints, and even reason of returns and exchanges. These are collectively referred to as product quality standards. The quality standards obtained from Amazon review & feedback analysis are communicated to the product department and quality department as the task of product iteration optimization.This closed-loop process is called from Amazon review & feedback analysis to quality problem solving, that is, VOC-CTQ process.
Improve brand experience
In Amazon review & feedback analysis, we should not only look at negative product reviews, but also at consumers’ positive review on our brands, markets, marketing and services. For example, customers review on our promotional campaign and the discount rule, and their public praise of our customer service. The information obtained from Amazon review & feedback analysis, on the one hand, can help us continuously improve our operation ability; on the other hand, it can enlarge the positive consumer word-of-mouth and convert it into brand.
Expand competitive advantage
In terms of product competitiveness or selling point (MUS), we should get more more advantages or exclusive functions. Through Amazon review & feedback analysis, we can clearly interpret consumers’ purchase motivation and experience praise. For example, “the power storage capacity of this product is the best among all the products I have used, and the quality is very good without heating”.From this review, we can see that customer care about the power storage capacity and safety of products. Some information obtained from Amazon review & feedback analysis is a point that needs us to enlarge infinitely in the creation of product selling points.
Improve sales conversion
Amazon review & feedback analysis has two levels of effect on sales transformation. One is mentioned above, obtaining the competitive advantage of products through Amazon review & feedback analysis, and then enlarging infinitely. The second is to find keywords that conform to consumer context and cognition. For scientific and technological products, we often describe our products in professional terms. However, from the review analysis of Amazon, we can see that the consumers are not familiar with professional term. For example, when we mention turbo, but consumers always said Power. We use words that are easy to search, read, and understand to describe products, so as to improve the sales conversion rate.
Mining Opportunities for new products
Mining explosive products is a deep-seated role in Amazon review & feedback analysis. We usually judge opportunities by sales, that is, the market supply. What Amazon review & feedback analysis can tell us is the consumer demand scenario and usage scenario, that is, the market demand situation. Only when the market volume and growth rate are large enough and meet the needs of consumers at the same time,Only when they can pay for it can they truly grasp the supply-demand relationship of the market and create hot money with high sales.
Dimensions and methods of basic Amazon review & feedback analysis
Monitor competitors through basic dimensions
The basic information and product parameters of product analysis include: product name, product price, listing picture, size and weight, product warranty, release date, packaging accessories, product selling point/function level parameters, product technology, product exclusive selling point/design.
Amazon review & feedback analysis and star Rating distribution include: Total number of review, total number of Purchase Review, total number of Rating, product Star Rating, star Rating distribution proportion, PR number and proportion, NR number and proportion.
Through the monitoring of the above product basic information and product review information, we can see the quality changes of competitors. Through the monitoring of review and star level in Amazon review & feedback analysis, we can see the changes of product sales volume and word-of-mouth from the side.
Explore product problems through positive and negative review
Top NR/PR statistical analysis includes: TOP 5 Positive Review and Top 5 Negative Review by product. These key point that we may be able to quickly change of products from Amazon review & feedback analysis. It is worth noting that positive and negative reviews do not refer to 1-star or 5-star reviews; Instead, they disassemble all consumer descriptions and extract the complaints and praises from them through Amazon review & feedback analysis.The specific execution method is as follows:
The first step is to select 10-20 competitors, such as BSR 20;
The second step is to browse all the reviews in the past two years and aggregate the contents with similar tagging;
The third step is to count the top ten NR and PR with the highest extraction probability;
The fourth step is to compare the high-frequency NR/PR with its own products and produce conclusions reports.
By digging out the quality problems in Amazon review & feedback analysis, we will continuously improve our products and improve their quality.
Dimensions and methods of advanced Amazon review & feedback analysis
Use basic tags to determine consumption scenarios
Cross-Industry basic tags, usually demographic tags and e-commerce quality tags. In Amazon review & feedback analysis, we can use these tags to clarify user portraits and basic word-of-mouth of categories.
For example, in Amazon review & feedback analysis, we can see who buys for whom, for apartments or villas, commercial or public, seasonal or holiday. The order accumulation of these information can help us to constantly identify the target user groups. For example, “I bought the latest smart sweeper as a Christmas present for my wife,This will help her clean up our public areas on the first and second floors “. Wife, Christmas gift, two floors, public area is the user portrait we obtained through Amazon review & feedback analysis.
E-Commerce quality tags, or product-level specific, can be divided into design, function, packaging, quality, promotion, technology, service, market, and brand. These nine first-level tags also constitute the nine basic factors for categories or brands in Amazon review & feedback analysis. For each factor, score 1-5 from the dimension of consumer review,It also ranks all products for the first time from the perspective of customers.
Mining explosive demand through lean specific tags
If you need to find new products and create best seller product, basic tags are far from enough. We need to create exclusive user experience tags for a category or even a single product.
User experience tag library is a tag library designed for products from the user’s perspective. It contains the user’s basic data, use process, purchase process, experience and scenario.
Different from user behavior tag library and product attribute tag library, user experience tag library is a tag system that combines user attributes, user actions, product attributes and consumer emotions.
Created by whom
In general, the product manager of the company is responsible for the design of Amazon review & feedback analysis of user experience tag library for his own category.
The product manager designs the user experience tag framework and is responsible for the tag design of product attributes in the framework. Then, five organizations including quality, market, brand, sales/operation and service are invited to expand tags. Finally, the Amazon review & feedback analysis user experience tag library is formed.
The product manager will formulate tag standards, quality requirements, safety requirements, maintenance mechanisms, and manage tag library iterations.
How to Create
Step 1: Determine the basic direction:
First identify objects, identify people (consumers), things (products), and relationships (use, purchase, experience, and emotion). For example: My wife thinks the appearance of this product is very beautiful. In this sentence, we can get people (wife, woman), things (appearance, ID desgin), relationships (forward, design)
Step 2: establish the design idea of Amazon review & feedback analysis:
The design idea of tags, or design logic, refers to the five ways of core words, divergence, drill-down, dynamic, and abstraction to extend the first-level and second-level tags.
Core words: if the core words are brand preferences, then subordinate tags are established around brand phrases, such as brand country, brand grade and brand grade.
Relevant: for example, around physiological parameters, we distribute all relevant tags, such as height, weight, blood type and hair color.
Drill down: The parent-child structure. Drill down all attribute tags. For example, if Bluetooth is the parent, drill down to the Bluetooth version, link technology, etc.
Dynamic: tags that record the development process and behavior process, such as browsing, clicking, sliding, and jumping out.
Abstract: Unified abstract processing from the same things, such as music, movies, food, tourism
Step 3: develop Amazon review & feedback analysis tag classification (level 1 and Level 2 tags)
Around the data source, we can design a primary and secondary tag tree from seven dimensions: User attributes, platform attributes, product attributes, environment attributes, usage attributes, experience attributes, and emotion attributes.
Step 4: compile the underlying tags for Amazon review & feedback analysis
When the primary and secondary levels are clear, we begin to continuously add, delete, modify, and query the underlying tags. Among them, the update frequency of product attribute tags is the highest, and we become the neat update, which requires the product manager to constantly gain insight into new products and sub-categories in the market and optimize the update.
How to analyze
After marking, you need to use tools to Mark and analyze the content. From the content after marking, we use tools to conduct a comprehensive Amazon review & feedback analysis.
- Users
Based on the product usage scenario data and the usage scenario tag heat map, the distribution of product usage scenarios under different brands of different groups is analyzed.
View the emotional distribution of users and identify the satisfaction degree of product functions by analyzing the scenario data of product manipulation.
Look at the pain points, cool points and demand points of consumers through the data of user preference expectation.
Determine product quality requirements and standards through tolerance for user functional quality problems
- Look at the product: mainly from the overall product quality, scene experience, function module to identify the problem
- Look at selling points: a comparative analysis between product selling points and user sentiment
- Look at competing products: compare all the above contents with competitors’ advantages and disadvantages
Use AI technology to monitor and analyze Amazon review
Automated massive data processing to break through labor cost
Amazon has nearly 40,000 multi-level departments, and the BSR rating of all Amazon markets has accumulated more than 100 million. However, a professional Amazon review analyst can only read 300 reviews manually a day and finish tagging and analysis. If you want to complete the Amazon review & feedback analysis task of the whole category, you often need a complete team to work for more than one month.The ability of data intelligence, from data capture, multi-language and multi-format parsing, word meaning and semantic aggregation, and primary tag aggregation, usually takes only a few minutes. On the other hand, for Amazon review & feedback analysis, we cannot randomly select hundreds of reviews to draw conclusions.only more comprehensive market data can have data validity.
Accurate positioning of multi-language review to identify customer sentiment
Even manually, it is impossible to accurately identify all languages, formats, and semantics. Perhaps the product is durable and reliable, and there are thousands of expressions. The intelligence of AI Amazon review & feedback analysis is not only reflected in its ability to identify multiple languages, gather global dictionaries and hot topics, but also in its ability to truly identify consumers’ intentions.AI intelligent Amazon review & feedback analysis can accurately identify consumers’ complaints, praises and expectations without recruiting Native language colleagues from various countries.
Deep-level industry tags to explore future product scenarios
Even industry experts cannot traverse all tags. Many micro-scenarios and new scenarios may still be fermented in consumer demand. AI intelligent Amazon review & feedback analysis has certain predictability and can provide new consumer scenarios for future innovation. Through continuous machine learning, capture the latest hot searches,AI intelligent Amazon analytics can automatically capture and aggregate new tag words and open a new direction for categories.
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