How To Draw Tangible Business Benefits From Review Analysis
Review analysis allows you to find important information about your brand perception, market trends, customer satisfaction, product strengths and weaknesses, new sales opportunities, and more.
When reviews are analyzed through text analytics and sentiment mining, they can give in-depth insights that can be business critical. To get the most accurate and holistic insights, it is essential that you choose suitable data sources, and that the review analysis platform itself is built and trained for your particular business and industry.
This guide provides you with all the information you need to ensure that you draw tangible business benefits from your customer feedback data.
What Is Review Analysis?
Review analysis is the breaking down of customer feedback into detailed aspects to understand customer sentiment about a product, service, or brand reputation.
When analyzing reviews, it is important to be objective and go deep into the causes behind the feedback rather than just an overall view of whether the input is positive or negative. This is vital because a review can also depend on the customer’s mood, the type of customer they are (regular, elite, or casual), the time they wrote the review, and even the platform they chose.
Machine learning (ML) tools that analyze sentiment fit this bill completely. They objectively break down reviews and comments into finer details such as aspects, topics, and themes, and extract real-time or time-bound business insights from them. Thus, with an ML tool at hand, you can analyze reviews on Reddit, Google, YouTube, Instagram, or any other source you want, at speed and without human bias.
Another important factor is that the more sources you analyze, the more accurate and detailed your understanding of customer wants and needs will be. This is primarily because customers may choose various avenues and platforms to voice their opinions based on their ages, geographic location, online habits, and such.
Thus, having a larger cross-section of review platforms to collect data from, increases your chances of getting to know your customer base and their behavior patterns better.
Why Do Companies Need Review Analysis?
Apart from tracking customer satisfaction, there are some critical factors that review analysis helps with.
1. Improve customer experience
Machine learning tools enable review analysis in such a way that they show you emotional triggers in your customers. This allows you to improve customer experience more effectively by addressing the particular issues brought to your notice by the insights.
2. Gain real-time insights into emerging trends
Businesses can get real-time customer experience analysis insights through live streams or time-bound data pulls that a sentiment analysis tool can do. They can also get these insights from historical data, all of which can show emerging trends in the market.
3. Identify product & service gaps
Review analysis allow you to improve your business operations including product and service gaps, logistical issues, customer-care challenges, and many other functions.
4. Increase sales conversions & upselling
Customer insights can tell a business what is the best way to reach out to a customer, what deals they like, what payment options they prefer, and many other such intricate details that can help you increase your sales as well as upselling opportunities.
5. Build brand loyalty & reduce churn
Having a great brand experience is the best way to retain customers and attract new ones. Knowing what customers want and what makes them happy, and using that information to develop loyalty programs to boost brand loyalty are some major advantages that review analysis insights provide.
6. Training chatbots & customer care professionals
Insights gained from analyzing reviews help businesses train tools that ease a customer through the customer journey. This includes both automated systems like chatbots as well as human resource-based customer service staff.
7. Manage brand reputation
You gain important alerts and notifications about spikes in your brand mentions, negative comments, low star ratings, and more, so that you can immediately take action to preserve and manage your brand reputation.
8. Monitor sentiment over time
A comments analysis tool helps you monitor customer and market sentiment over time. It can be related to a new product launch, new store location, change in staff, or just an overall change in industry trends. You can set benchmarks for yourself and also for your competitors based on certain metrics and parameters.
9. Develop engaging marketing content
Social media sentiment analysis can give you the right insights about what kind of marketing content can give you the most sales conversions. This is important because simply an increase in social media engagement (likes, shares, etc) does not necessarily mean an increase in sales.
10. Competitor analysis
Just as you can get insights about your own products and services, you can also get insights about competing brands and customer motivations, preferences, and purchase habits.
Sentiment mining from review analysis gives you these and many other advantages. You can further increase the depth and accuracy of the insights by having a bigger and more diverse customer review database.
How Is Review Analysis Done?
The sentiment analysis model that you choose must be first trained to your business and industry so that aspects are collected with more precision and your results are as accurate as can be. The sentiment analysis process that determines the insights from customer reviews comprises many tasks, which can be broken down into four main steps.
Step 1. Data Collection
The first step is composed of data collection. You can gather the data either directly using the URL of the online source or manually upload the comments or survey data as an excel file.
Step 2. Data Processing
Now that you have collected all the data for review analysis, it will be cleaned and prepped for text analysis. The ML model will use natural language processing (NLP) algorithms and semantic analysis to analyze the text. Aspects, entities, and topics will be recognized and extracted.
Custom tags created for aspects such as “price”, “customer service”, “food”, “check-in” etc. will make sure that all aspects are grouped in the right categories. Multilingual data analysis further assists in ensuring that each review is analyzed in the language it is written in.
Step 3. Analyzing Reviews For Sentiment
Each aspect, entity, and topic extracted and isolated in step 2 is scored for sentiment between -1 to +1, with 0 being neutral. These scores are then holistically calculated to give you the overall brand perception as well as aspect-based customer satisfaction metrics. Thus you get not only individual aspect sentiment measures but also the overall customer sentiment score.
Step 4. Visualizing the CX Insights
All the sentiment insights thus extracted by the platform are available on an intuitive and interactive dashboard in the form of charts and graphs.
How Is Review Analysis Done Using Repustate IQ?
Repustate IQ offers review analysis through a number of aspect models, each meant specifically for your industry. Once chosen, the model is further trained for your particular business. This ensures the most precise insights. Detailed below are the steps in which you can process your review data for sentiment analysis.
Step 1 - Create Project
As a first step, you will need to register on Repustate IQ. Once you’ve created your login credentials, you must create a new project to begin analyzing the reviews from the source you want.
Step 2- Upload & Analyze Data
Now that you have created a new project that will save your insights, you need to select the source of your reviews. Since Repustate IQ can source data from numerous live social media APIs, this is a simple task.
You can paste the URL link of the source of your choice (Reddit, Amazon, etc.) in the space provided and hit enter. The model will immediately start pulling in the data.
Alternatively, you can download the comments and reviews, and upload the data in the form of an excel file. See the video below.
Step 3 - Visualizing Insights
Once the ML model has processed all the reviews through tasks such as named entity recognition (NER), neural networks, native natural language processing, text analysis, sentiment analysis, and others, it will showcase all the insights onto its visualization dashboard. You can see this data in the form of charts, bar graphs, word clouds, and other ways.
If you have chosen Repustate IQ as an API, you can still see all the insights comprehensively because Repustate IQ is compatible with many external dashboards such as PowerBI and Tableau.
Whatever option of Repustate IQ you choose, you are provided with the most accurate review analysis insights at speed and scale.
Step 4 - Creating Custom Entities Without Coding
If you want to create your own sentiment rules to get the variety of insights you want, you can do all that without having to code. Repustate IQ is built in such a way that you can create your own sentiment rules by creating custom entities based on your business requirements.
Creating Custom Sentiment Rules
Creating Custom Entities
Repustate IQ puts the control in your hands so that you get the right results even if you do not have data analysts on your team. You do not need any third-party integration of underlying software either.
Review analysis provides you with business intelligence that can make you resilient against changing market dynamics and a competitive environment. Having the right insights that you can use for your growth strategies can go a long way in helping you develop effective campaigns to build customer relationships, boost customer loyalty, increase brand perception, brand comparison for competitive intelligence and elevate your sales performance.
Repustate IQ is available in 23 languages, all of which are processed using our own speech taggers. This is one of the biggest reasons why Repusate’s review analysis insights from foreign language data are the most precise in its category. The platform is available on the cloud for your virtualized infrastructure, as well as an on-premise installation.
Leverage your customer review data and use it to your advantage as our numerous clients all over the world do.