Customer Review Analysis Powered By Sentiment Analysis
Customer review analysis helps you understand customer and product experience feedback from multiple channels to uncover intelligent business insights. To this end, using multiple customer review sources to discover customer and product insights gives you more accurate, granular, and high-precision results. For this, you need to power customer review analysis with sentiment analysis. Machine learning automates emotion mining from multiple sources seamlessly, and thus you get strategic, action-oriented insights for your business.
What Is The Importance Of Customer Reviews Analysis?
Customer review analysis is invaluable for a company’s growth strategy, product roadmap, customer acquisition and retention strategy, and marketing campaigns. Let’s examine its importance in detail.
1. Product innovation
Customer review analysis can tell you important details as to what direction your product roadmap should be headed. Customer feedback can also have hidden gems that can give you product innovation and improvement ideas that you can share with your product development team. These insights can also lead to better product differentiation, which by far is the best way to stand out from competitors. Coupled with a great brand experience campaign, this can be a winning strategy for greater business growth.
2. Effective marketing
Customer review analysis can give you insights based on customer demographics that you can use to develop more targeted marketing strategies that are efficient and effective. With the help of sentiment analysis, you can ensure that each bracket of your target audience feels heard and spoken to, thus experiencing a more genuine connection with your brand.
3. Track customer motivations
It is important to know how different types of customers engage with your brand and what their motivations to complete a purchase are. When you use multiple sources of customer review data to analyze the sentiment prevalent in the market about you, you can find any hidden criteria that you can leverage to ensure more sales conversions and growth.
4. Improved customer service
Customer review sentiment analysis can show you concrete steps in which you can better your customer experience by improving your customer service. Whether it’s by phone, email, or chatbot, you can make sure that your customers always feel that you are approachable and care about them even after they have made a purchase or haven’t frequented your store or website in recent times.
5. Wholesome brand experience
Emotion mining customer reviews for brand insights can provide you with key information you can use to revamp your brand experience strategies. Since a brand experience can boost a company’s perceived brand loyalty and better your chances of customer retention, it is a worthwhile attempt to use machine learning in order to get accurate and insightful business intelligence from a comprehensive customer review analysis.
6. Product-fit & market gap analysis
Customer feedback can help you figure out how well your product is fulfilling market needs and also if you are the right fit for that market. Investing in a product that is already common and one that you cannot find a credible product differentiation with can be detrimental. With customer review analysis done from multiple sources like Reddit, Amazon, or Google, you can get a balanced product fit and market gap analysis.
7. Competitor analysis
Customer and brand reviews can tell you not only about your own brand but also about your competitors. Use customer experience analysis to predict market sentiment on similar products and services and fortify your business strategy with this intelligence.
8. Discover market trends
Voice of the customer data can give you a head’s up regarding emerging trends in your industry and business vertical. You can know what new facets customers prefer and how you can satisfy them. This makes you resilient to sudden market dips and prepares you for the future.
9. Measure business stability
Customer review analysis can tell you a lot about the longevity of not only your business but also that of your potential clients or business partners. If customers are happy with you, they will not be easily swayed by competitors.
10. Build customer loyalty
Customer review sentiment analysis is very important in building customer loyalty programs that help with customer retention and challenge attrition. Analyzing sentiment in customer feedback can tell you what concrete steps you can take to ensure repeat customers and thus save on costly new customer acquisition measures.
What Are The Major Sources Used For Customer Review Analysis?
Major sources for customer review analysis include Google, Trustpilot, Reddit, Glassdoor, Reddit, and OpenTable. Let’s look at each of them individually.
Google is the most popular and one of the most important sources of insights from customer review sentiment analysis. You can post reviews through the GoogleMaps link and be as descriptive as you like. Customers can also do it anonymously or publish their names. More importantly, since Google is so ubiquitous and one of the top-rated platforms for user-generated content, you can leverage Google review analysis to understand your brand reputation, customer satisfaction, and product and market gap fit.
Trustpilot is an independent website that customers can use to publish their reviews on a variety of businesses. The website has 6.9 monthly impressions on its Trust widget and has more than 120 million customer reviews. This makes it a formidable data source for customer review sentiment analysis. Trustpilot is a perfect example of how customer reviews can help customers make better purchase decisions, as well as help companies better their products and services. Headquartered in Denmark, the company connects customers from over 47 countries.
Glassdoor is by far the most-used platform by current employees and ex-employees to post reviews about their companies. To build an employer-brand and to ensure that your customers see you in a positive light, you need to ensure that your employees are happy as well. Disgruntled employees leave bad reviews and this can affect your overall brand experience, which can, in turn, adversely affect your brand worth in the eyes of your customers, no matter how popular you may think you are.
Customer review analysis insights from Reddit can be priceless for a company because the platform hosts genuine conversations among users. These conversations, usually in subreddit posts, can be threads that can diversify into other related topics. These topics can range from help with computational algorithms to suggestions for good vacation spots. And because the comments are crowdsourced and can be upvoted or downvoted, there are better chances of you getting insights that are not manipulated through fake reviews as often is the case with other platforms.
You can use the platform for advertising but more importantly to connect with your customers in a meaningful way. Reddit sentiment analysis can show you how you can use the platform for brilliant customer and market insights.
OpenTable is a smart SAAS platform that allows you to book a restaurant reservation online as well as post reviews about your dining experiences. You can use it to find fine-dining restaurants, boutique dining, takeaway options, and interesting local fare that you may not have found otherwise. Customer review analysis of OpenTable comments and reviews can tell you how to make customer experience better for your patrons - whether in terms of wait times, parking convenience, locations, improved menu or wine list, or other ways.
How Is Customer Review Sentiment Analysis Done?
Machine learning (ML) can automate customer review sentiment analysis. An ML platform uses several ML tasks for this purpose so it can give you contextualized customer experience and product insights. Repustate’s sentiment analysis solution for customer reviews analyzes feedback from multiple sources in the following way.
Step 1. Data Collection
Step 1 consists of gathering data from various sources that are relevant to your business. You can upload customer reviews directly through the source link such as from Google, or Reddit. Or you can download the data in an excel file and upload it onto the platform.
Step 2. Data Processing
The ML algorithms now process all the data for customer review analysis using text analytics, natural language processing (NLP), topic classifiers, named entity recognition (NER), etc. The platform identifies and extracts aspects, topics, and themes like “price”, “food”, or “drinks” from the data and readies it for the next step.
Step 3. Customer Review Sentiment Analysis
The sentiment analysis API now scans each aspect and entity isolated from the customer review analysis and calculates the sentiment for each. It will score sentiment in a range from -1 for negative, 0 for neutral, and 1 for positive emotions. Eventually, it will give you an overall sentiment of your product, and individual, aspect-based sentiment scores.
Step 4. Visualizing the CX Insights
All the customer review analysis insights can be viewed on a customer experience dashboard of the Repustate IQ. You can also see the insights on other visualization tools like Power BI or Tableau that can be integrated into the API. These visualizations, in the form of charts and graphs, can also be shared with your extended marketing teams for deeper discussions.
Discover more about Review sentiment analysis.
Why Do We Need To Analyze Multiple Sources Of Customer Reviews?
High-quality, diverse data can give you an advantage over prevailing market dynamics, which includes your competitors. This is why you need to analyze multiple sources of customer reviews for intelligent business insights.
Below are some important reasons why you should always consider using more than one source for customer review sentiment analysis.
1. Business intelligence
Customer review analysis using ML-enabled sentiment analysis helps you extract business intelligence from various sources seamlessly. This is important because you can gather granular product insights, market differentiators, build social media strategies, and more when you have data from various types of customer demographics.
2. Capture new markets
The more high-quality data you have, the more accurate the insights are. When you think of capturing a bigger market and expanding into new territories, evaluating customer feedback from different sources can be invaluable for your market research.
3. Market gap analysis
The more diverse your data, the more precise the customer review sentiment analysis results are. This diverse data will help you measure your product-market fit, and also your product-gap analysis.
4. More sales conversions
Accurate insights give you a steady foundation on which you can build your sales and marketing strategies on. This in turn helps you with better sales conversions because data-driven strategies always have positive outcomes.
5. Track competitors
You can leverage customer review analysis for measuring and tracking competitor performance and customer leanings towards them. The more varied sources of customer review data you use, the more holistic your insights will be.
6. Better employee satisfaction
Indeed, Linkedin, and Glassdoor, are all platforms that you can harness for employee satisfaction scores to ensure that not only do you have a positive work environment but that your overall brand reputation is not affected by disgruntled employees.
7. Increased brand worth and visibility
Using multiple customer review data sources like TikTok, Instagram, Reddit, Google reviews, etc. for social media sentiment analysis gives you fresh ideas as to how to market your brand and how to relate to different types of audiences.
8. Effective marketing propositions
When you are able to categorize your customers based on their purchase decisions, buying behavior, motivators, and other factors, it makes it easy to develop strategic marketing propositions that are more effective.
9. Product differentiation
Figuring out market trends through customer review sentiment analysis can help you with product differentiation so that you can better your brand impression.
10. Enriched customer loyalty
Sentiment insights from customer review analysis provide you with crucial data that you can use to develop customer loyalty programs and improve customer experience and engagement.
Emotion mining customer experience reviews using machine learning is the sure-shot way to ensure intelligent insights for business growth. Repustate’s all-in-one customer experience analytics platform, Repustate IQ, provides you with the capability to analyze numerous customer review data sources including TikTok insights and YouTube comments analysis, in 23 languages.
Once trained, the machine model is set to analyze all your customer data and gets more and more intelligent over time with use. Choosing the aspect model that suits your industry-type is important because otherwise, an automated platform will not extract the right insights from the data. With Repustate you have this advantage. Choose a model, whatever your industry - banking, automotive, retail, or any other.
The solution is available as an API or as a full-fledged platform with a comprehensive customer experience dashboard. You can install it on Cloud or if you wish, choose an on-prem version.