Data Blog by Lizeo

How to analyze and extract insights from consumer reviews?

According to a recent study conducted by Ipsos in 2023, 92% of French people admit to scrutinizing online customer reviews before finalizing a purchase. This trend clearly indicates that consumers place more trust in the firsthand experiences of their peers than in traditional advertising.

In this context, it becomes imperative for brands to delve deeper into the analysis of these comments in order to better understand how customers perceive their products.

This includes understanding the reasons for their satisfaction, as well as the reasons for their dissatisfaction. This valuable information can serve as a compass to guide your future product developments and improvements.

By scrutinizing these reviews, it is also possible to identify opportunities for innovation. Indeed, the flaws or shortcomings mentioned by consumers can serve as points of reflection to refine your offerings and more effectively meet market expectations.

In summary, the analysis of online customer reviews represents an essential strategic tool for staying competitive and meeting your customers’ needs in an informed manner.

Challenges

Monitoring one’s online reputation by quickly identifying issues
Identifying areas for improvement in products and services
Identifying consumer needs, expectations, and preferences to inform marketing and product development strategies
Identifying the strengths and weaknesses of competitors
Measuring customer satisfaction over time and assessing the impact of actions on customer satisfaction.

Lizeo methodology

Diagnostic: Identifying the main digital touchpoints/website + Understanding the primary search criteria
Data Collection: Capturing reviews and ratings related to the universe that we want to analyze (country, product category, product references, brands, services, points of sale...)
Pre-processing and cleaning of collected data
Data processing by AI: Extracting the main consumer concerns expressed in reviews by grouping them by Topic (or subjects), analyzing the sentiment of each topic mentioned in the reviews, and qualifying each review by identifying the topic/sentiment pair.
Data Intelligence: Counting reviews and tracking ratings over time, cross-referencing reviews and ratings to detect weak signals from consumer feedback on your product, and understanding your online reputation compared to the competition.

Analysis of reviews and ratings for oil-free fryers: What do we learn from collected data?

The collected data allows brands to analyze consumer feedback on their own products as well as to compare themselves to the competition. In the insights identified below, our analysis focuses on the small household appliances market, particularly oil-free fryers.

The topics addressed

Examples of insights:

The collected data allows for the identification of popular themes and characteristics mentioned in customer comments. Among them:

Ease of use
Durability
Value for money
Finish/design
Performance
etc.

Consumer sentiments by topics

Examples of insights:

Analyzing consumer sentiment regarding a theme allows for the identification of pain points and a deeper understanding of whether the theme reveals a positive, negative, or neutral aspect of the product or service.

Comparing the percentage of positive reviews by brand and theme.

Examples of insights:

This comparison with the competition allows for the extraction of powerful insights on several fronts:

Marketing/Advertising Positioning: Identifying key points to enrich marketing and sales arguments.
Product Evolution: Identifying recurring issues to improve future product versions.
Purchase Decision: Identifying themes that will reassure the consumer or have no influence on the decision-making process.

How to analyze and extract insights from consumer reviews?

Challenges

Lizeo methodology

Diagnostic: Identifying the main digital touchpoints/website + Understanding the primary search criteria

Data Collection: Capturing reviews and ratings related to the universe that we want to analyze (country, product category, product references, brands, services, points of sale...)

Pre-processing and cleaning of collected data

Data processing by AI: Extracting the main consumer concerns expressed in reviews by grouping them by Topic (or subjects), analyzing the sentiment of each topic mentioned in the reviews, and qualifying each review by identifying the topic/sentiment pair.

Data Intelligence: Counting reviews and tracking ratings over time, cross-referencing reviews and ratings to detect weak signals from consumer feedback on your product, and understanding your online reputation compared to the competition.

Analysis of reviews and ratings for oil-free fryers: What do we learn from collected data?

The topics addressed

Consumer sentiments by topics

Comparing the percentage of positive reviews by brand and theme.

Do you want to understand what your customers think of your products?

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