Consumer Behavior 101: Digital Consumer Behavior - Online Ratings Reviews Valence Volume Fake Reviews

What It Is

Online ratings and reviews are a form of social proof that influence consumer purchasing decisions. A canonical example is Yelp, a review platform where users can rate and comment on local businesses. Yelp's algorithm uses a combination of valence (positive or negative sentiment), volume (number of reviews), and authenticity (filtering out fake reviews) to provide users with a trustworthy assessment of a business. Understanding online ratings and reviews matters for marketers, as it can significantly impact a brand's reputation, customer acquisition, and retention.

Key Terms & Concepts

• Valence: The positive or negative sentiment of online reviews, influencing consumer perception of a product or service. (Example: A study by Chevalier & Mayzlin (2006) found that a 1-star increase in valence on Yelp led to a 5-9% increase in sales.)
• Volume: The number of online reviews, which can indicate a product's popularity and credibility. (Example: A study by Liu (2006) found that products with more reviews were perceived as more trustworthy.)
• Fake Reviews: Online reviews that are intentionally misleading or fabricated, which can harm a brand's reputation. (Example: A study by Ghose & Ipeirotis (2013) found that 20% of online reviews on Amazon were fake.)
• Sentiment Analysis: The process of analyzing online reviews to determine their emotional tone and content. (Example: A study by Pak & Paroubek (2010) developed a sentiment analysis algorithm that achieved 85% accuracy.)
• Social Proof: The influence of online reviews on consumer purchasing decisions, based on the idea that people are more likely to trust the opinions of others. (Example: A study by Cialdini (2009) found that social proof was a key factor in consumer decision-making.)
• Online Reputation Management: The process of monitoring and managing a brand's online reputation, including responding to reviews and addressing customer complaints. (Example: A study by Hennig-Thurau et al. (2004) found that companies that actively managed their online reputation saw significant improvements in customer satisfaction and loyalty.)
• Review Filtering: The process of identifying and removing fake or low-quality reviews from online platforms. (Example: A study by Ghose & Ipeirotis (2013) developed a review filtering algorithm that removed 30% of fake reviews on Amazon.)
• Star Rating: A numerical rating system used to evaluate the quality of a product or service, often used in online reviews. (Example: A study by Chevalier & Mayzlin (2006) found that a 1-star increase in star rating on Yelp led to a 5-9% increase in sales.)
• Word-of-Mouth: The influence of online reviews on consumer purchasing decisions, based on the idea that people are more likely to trust the opinions of others. (Example: A study by Cialdini (2009) found that word-of-mouth was a key factor in consumer decision-making.)
• Customer Satisfaction: The level of satisfaction a customer experiences with a product or service, often measured through online reviews. (Example: A study by Hennig-Thurau et al. (2004) found that companies that prioritized customer satisfaction saw significant improvements in customer loyalty and retention.)
• Brand Reputation: The overall perception of a brand's quality, reliability, and trustworthiness, often influenced by online reviews. (Example: A study by Fombrun & Shanley (1990) found that brand reputation was a key factor in consumer decision-making.)
• Marketing Mix: The combination of product, price, promotion, and place that a company uses to market its products or services, often influenced by online reviews. (Example: A study by Kotler & Keller (2016) found that companies that prioritized the marketing mix saw significant improvements in sales and customer satisfaction.)

Common Misunderstandings

• Misunderstanding: Online reviews are a reliable indicator of a product's quality.
• Correction: Online reviews can be influenced by various factors, including fake reviews, biased opinions, and social proof. A study by Ghose & Ipeirotis (2013) found that 20% of online reviews on Amazon were fake.
• Misunderstanding: Online reviews are only relevant for consumer goods.
• Correction: Online reviews can be relevant for a wide range of products and services, including B2B, services, and experiences. A study by Hennig-Thurau et al. (2004) found that companies that actively managed their online reputation saw significant improvements in customer satisfaction and loyalty.
• Misunderstanding: Online reviews are only a reflection of customer satisfaction.
• Correction: Online reviews can reflect a range of factors, including customer satisfaction, quality, and brand reputation. A study by Fombrun & Shanley (1990) found that brand reputation was a key factor in consumer decision-making.

Quick Application / Identification

Scenario: A consumer is considering purchasing a new smartphone based on online reviews. The reviews are overwhelmingly positive, with an average rating of 4.5 stars. However, upon closer inspection, the consumer notices that many of the reviews are from the same IP address. What concept is at play here?

Answer: Fake Reviews. Explanation: The consumer is experiencing social proof, but the fake reviews are influencing their perception of the product's quality.

Scenario: A company is trying to improve its online reputation by responding to customer complaints on social media. However, the company's responses are often delayed or unhelpful. What concept is at play here?

Answer: Online Reputation Management. Explanation: The company is failing to manage its online reputation effectively, which can lead to a negative perception of the brand.

Scenario: A consumer is considering purchasing a new restaurant based on online reviews. The reviews are overwhelmingly positive, but the consumer is unsure if they are trustworthy. What concept is at play here?

Answer: Sentiment Analysis. Explanation: The consumer is trying to analyze the emotional tone and content of the reviews to determine their credibility.

Last-Minute Revision

• Valence: The positive or negative sentiment of online reviews.
• Volume: The number of online reviews.
• Fake Reviews: Intentionally misleading or fabricated online reviews.
• Sentiment Analysis: The process of analyzing online reviews to determine their emotional tone and content.
• Social Proof: The influence of online reviews on consumer purchasing decisions.
• Online Reputation Management: The process of monitoring and managing a brand's online reputation.
• Review Filtering: The process of identifying and removing fake or low-quality reviews from online platforms.
• Star Rating: A numerical rating system used to evaluate the quality of a product or service.
• Word-of-Mouth: The influence of online reviews on consumer purchasing decisions.
• Customer Satisfaction: The level of satisfaction a customer experiences with a product or service.
• Brand Reputation: The overall perception of a brand's quality, reliability, and trustworthiness.
• Marketing Mix: The combination of product, price, promotion, and place that a company uses to market its products or services.
• Fake Reviews can be identified by looking for suspicious patterns, such as multiple reviews from the same IP address.
• Sentiment Analysis can be influenced by biases and cultural differences.
• Online Reputation Management requires a proactive and responsive approach to customer complaints and feedback.
• Review Filtering can be challenging due to the complexity of online reviews and the need for human judgment.
• Star Rating can be influenced by various factors, including fake reviews and biased opinions.
• Word-of-Mouth can be influenced by social proof and online reviews.
• Customer Satisfaction can be influenced by a range of factors, including product quality, price, and customer service.
• Brand Reputation can be influenced by a range of factors, including online reviews, customer satisfaction, and marketing mix.
• Marketing Mix can be influenced by a range of factors, including product, price, promotion, and place.