E-reputation and semantic analysis of customer reviews
Posted: Sat Dec 28, 2024 6:11 am
As you know, your brand's e-reputation is a bit like a digital showcase. Prospects scan the information they find on the web and depending on what they read, they move or not to your establishments.
Customer reviews are a big part of your online reputation . They build trust with your consumers and have a direct impact on your bottom line. As evidence, they impact 67.7% of purchasing decisions 1 .
Additionally, 88% of consumers look for online reviews before choosing a local service 2 .
To manage your e-reputation , you must therefore jamaica mobile phone number list manage your customer reviews. But how? Today, artificial intelligence allows for extremely precise studies, in particular thanks to the semantic analysis of customer reviews.
Let’s discover this new technology together!
1. Improve your online reputation by analyzing customer reviews by category and location
2. Going even further with semantic analysis of intentions
Conclusion
Improve e-reputation by analyzing customer reviews
1. Improve your online reputation by analyzing customer reviews by category and location
Analysis of customer reviews by category
Before recent technological advances, customer reviews were classified according to the simple negative/positive duality. At the limit, we could have a detail per point of sale. Not very precise therefore.
Now, the semantic analysis of customer reviews makes it possible to detect, group and sort comments according to targeted themes such as reception, schedules, delivery or advice. In the example below we can see that AI detects different topics within the same customer review.
Example of customer reviews analyzed with semantic analysis
Example of customer reviews analyzed with semantic analysis
In addition to the occurrence, the tool obviously performs a classification of the themes according to the positive or negative aspects. This cross-referencing of the data makes it possible to highlight the main subjects of the opinions and their tone.
In the example below we notice that customer reviews mainly refer to the staff, the atmosphere and the welcome.
ranking category semantic analysis customer reviews
Excerpt from the quarterly report of the Digitaleo semantic analysis
You can therefore identify at a glance the strengths of your network and areas for improvement ! Practical for adapting your action plans.
Analysis of customer reviews by location
Semantic analysis of customer reviews also allows you to rank by proximity, with details by point of sale.
In the example below we notice that some points of sale have a score of 100% positive reviews (well done!) while others exceed 50% negative reviews. The territorial differences are therefore highlighted.
ranking category establishment customer reviews semantic analysis
Excerpt from the ranking of network establishments by semantic analysis
This proximity analysis provides you with several advantages to optimize your customer satisfaction . First of all, by identifying the "good students" you can identify their strengths. Which you then duplicate across your entire network! Then, the analysis highlights your areas for improvement . You can see where it's not working, find out why and implement corrective actions.
Let's imagine that your point of sale located in Rennes obtains a high score in Reception (100% positive opinions). At the same time, your establishment in Paris reaches a score of 67% negative opinions on the same subject. There is therefore a problem. Perhaps you should consider training on customer reception for your Paris teams? Without the details by theme and by location, you would not have been aware of this difference and would not have been able to act.
The semantic analysis of customer opinions therefore allows you to improve the experience of your customers. By acting upstream before the consumer rates you, you improve your e-reputation !
semantic analysis customer reviews intentions
2. Going even further with semantic analysis of intentions
Deep semantic analysis processes the content of customer reviews by adding the examination of intentions. This technology makes it possible to differentiate within the same message on the customer's feelings. It understands the nuances and allows for a fair and global vision.
Let's take the example of a customer who writes: " Defective products, broken after only one week of use! Fortunately, the after-sales service quickly proceeded with an exchange ". By adding the intention to the occurrence, the semantic analysis of customer reviews will classify the comment as negative for the "product" theme but also as positive for that of "after-sales service". This allows for an extremely detailed study and a precise vision of the customer journey in your points of sale!
As with the classification by theme and by location, the study of intentions deepens your strengths and areas for improvement . This allows you to prepare a detailed action plan for each point of sale!
Don't forget: consumers regularly share their experiences on the web, having a quality customer journey means benefiting from a good online reputation !
Conclusion
Semantic analysis of customer reviews is an effective way to improve your e-reputation . By studying the themes, tone, location and intention, this technology provides you with a precise vision of your customer journey. You thus identify your strengths , to be duplicated across your entire network, and your areas for improvement .
To quickly analyze all of your customer reviews, you can rely on a customer review management platform . Thanks to the artificial intelligence developed by Digitaleo, you benefit from a classification of your customer reviews into 16 themes specially adapted to brand networks (Accessibility, Reception, Traffic, Ambiance, Choice, Advice, Team, Environment, Loyalty, Schedule, Delivery, Price, Cleanliness, Appointments, Responsiveness, After-sales service). This allows you to optimize the customer experience and therefore your online reputation !
Customer reviews are a big part of your online reputation . They build trust with your consumers and have a direct impact on your bottom line. As evidence, they impact 67.7% of purchasing decisions 1 .
Additionally, 88% of consumers look for online reviews before choosing a local service 2 .
To manage your e-reputation , you must therefore jamaica mobile phone number list manage your customer reviews. But how? Today, artificial intelligence allows for extremely precise studies, in particular thanks to the semantic analysis of customer reviews.
Let’s discover this new technology together!
1. Improve your online reputation by analyzing customer reviews by category and location
2. Going even further with semantic analysis of intentions
Conclusion
Improve e-reputation by analyzing customer reviews
1. Improve your online reputation by analyzing customer reviews by category and location
Analysis of customer reviews by category
Before recent technological advances, customer reviews were classified according to the simple negative/positive duality. At the limit, we could have a detail per point of sale. Not very precise therefore.
Now, the semantic analysis of customer reviews makes it possible to detect, group and sort comments according to targeted themes such as reception, schedules, delivery or advice. In the example below we can see that AI detects different topics within the same customer review.
Example of customer reviews analyzed with semantic analysis
Example of customer reviews analyzed with semantic analysis
In addition to the occurrence, the tool obviously performs a classification of the themes according to the positive or negative aspects. This cross-referencing of the data makes it possible to highlight the main subjects of the opinions and their tone.
In the example below we notice that customer reviews mainly refer to the staff, the atmosphere and the welcome.
ranking category semantic analysis customer reviews
Excerpt from the quarterly report of the Digitaleo semantic analysis
You can therefore identify at a glance the strengths of your network and areas for improvement ! Practical for adapting your action plans.
Analysis of customer reviews by location
Semantic analysis of customer reviews also allows you to rank by proximity, with details by point of sale.
In the example below we notice that some points of sale have a score of 100% positive reviews (well done!) while others exceed 50% negative reviews. The territorial differences are therefore highlighted.
ranking category establishment customer reviews semantic analysis
Excerpt from the ranking of network establishments by semantic analysis
This proximity analysis provides you with several advantages to optimize your customer satisfaction . First of all, by identifying the "good students" you can identify their strengths. Which you then duplicate across your entire network! Then, the analysis highlights your areas for improvement . You can see where it's not working, find out why and implement corrective actions.
Let's imagine that your point of sale located in Rennes obtains a high score in Reception (100% positive opinions). At the same time, your establishment in Paris reaches a score of 67% negative opinions on the same subject. There is therefore a problem. Perhaps you should consider training on customer reception for your Paris teams? Without the details by theme and by location, you would not have been aware of this difference and would not have been able to act.
The semantic analysis of customer opinions therefore allows you to improve the experience of your customers. By acting upstream before the consumer rates you, you improve your e-reputation !
semantic analysis customer reviews intentions
2. Going even further with semantic analysis of intentions
Deep semantic analysis processes the content of customer reviews by adding the examination of intentions. This technology makes it possible to differentiate within the same message on the customer's feelings. It understands the nuances and allows for a fair and global vision.
Let's take the example of a customer who writes: " Defective products, broken after only one week of use! Fortunately, the after-sales service quickly proceeded with an exchange ". By adding the intention to the occurrence, the semantic analysis of customer reviews will classify the comment as negative for the "product" theme but also as positive for that of "after-sales service". This allows for an extremely detailed study and a precise vision of the customer journey in your points of sale!
As with the classification by theme and by location, the study of intentions deepens your strengths and areas for improvement . This allows you to prepare a detailed action plan for each point of sale!
Don't forget: consumers regularly share their experiences on the web, having a quality customer journey means benefiting from a good online reputation !
Conclusion
Semantic analysis of customer reviews is an effective way to improve your e-reputation . By studying the themes, tone, location and intention, this technology provides you with a precise vision of your customer journey. You thus identify your strengths , to be duplicated across your entire network, and your areas for improvement .
To quickly analyze all of your customer reviews, you can rely on a customer review management platform . Thanks to the artificial intelligence developed by Digitaleo, you benefit from a classification of your customer reviews into 16 themes specially adapted to brand networks (Accessibility, Reception, Traffic, Ambiance, Choice, Advice, Team, Environment, Loyalty, Schedule, Delivery, Price, Cleanliness, Appointments, Responsiveness, After-sales service). This allows you to optimize the customer experience and therefore your online reputation !