The psychology behind ratings and reviews

Illustration by Marcia Diaz

Another week, another eyeshadow palette, another fatigued customer—or so customers say in their reviews online. They’re searching out products that are “really worth it” and asking why they should spend upwards of $40 on yet another palette to add to their collection. “Have we really reached peak makeup?” they ask. With brands experiencing drops in sales and consequently searching out new ways to connect with customers and retain their loyalty, brands are starting to listen to the dialogue that goes on online, especially in product reviews.

With 91 percent of people regularly or occasionally reading online reviews and 84 percent trusting them as much as a personal recommendation, there’s no doubt that online reviews generate brand buzz and affect consumers’ decision making. Online reviews also affect trust of the brand itself. Brands are finding that quality products and services combined with persuasive marketing isn’t enough. They now need to create empathy and a personal connection throughout the customer journey, where every touchpoint can now be mapped through online reviews, surveys such as NPS, customer care interactions and other forms of customer feedback.

91 percent of people regularly or occasionally read online reviews and 84 percent trust them as much as a personal recommendation.

Customer reviews, when understood as a vast and varied source of quantitative and qualitative customer feedback data, hold the answers to questions about customer behaviour and psychology that can point the way forward for your brand. Whether positive or negative, reviews generate word of mouth about your brand, products and services and create dialogue not only between brand and customer but between consumers themselves. Essentially, reviews tap into a human desire for transparency and belonging.

As a customer feedback solution, AI text analysis of reviews lets you dig deeper into feedback data, significantly the psychological reasoning behind reviews. When collected and analyzed through AI text analytics, reviews become a superior source of insight into customer needs and behaviours. Those insights can be used to inform CX and marketing strategies, product and service improvements, customer service and care programs and other areas of your business.

Why customers write reviews

It’s been said that the future of retail is in customer experience, including personalization of products and services and relationship building between brands and customers that reinforces loyalty. Essential to both is listening to customers and acting on what they have to say. Every customer who posts a review wants to be heard, by the brand they’re reviewing and by their peers. Many want someone to take action: either the brand to make a change or other customers to buy or avoid a product or service.

Customer reviews typically cover one or more areas:

  • personal experience with a product or service
  • characteristics and quality of a product or service
  • value for money
  • customer service and customer care
  • shipping times and quality

Look deeper and each of those reviews reflect a customer’s character, psychology and an opinion that blends the emotional and intuitive with facts and logic, showing that the two are not exclusive. That very human form of expression is what AI text analytics captures so well. And if you’re concerned about the trustworthiness of certain reviews, text analysis can flag inconsistencies in wording, location and date of reviews and other data.

The future of retail is in customer experience, including personalization of products and services and relationship building between brands and customers that reinforces loyalty.

How reviews affect customers

Reading through reviews has become an essential part the customer journey towards purchasing. Psychologically, we place value on the opinions and behaviours of others and often make choices based on the choices that other people have or haven’t made.

Customers see other customers’ reviews as personal testimony. When reading customer reviews, a dialogue opens between reader and reviewer and, less directly, between brand and reviewer. Readers see a small window into another person’s psychology as they offer a their story, however short, about a product or service.

Just as storytelling is powerful for creating effective customer experience, the stories that customers tell create a more personal connection between storyteller and reader. That human connection is persuasive in a different way than the persuasion of marketing and branding language. Reviews represent multiple perspectives of “real people” who are seen as more impartial than any information offered by a brand.

Unlike reviews and ratings on certain gig-economy apps, retail industry reviews and ratings reflect opinions that aren’t as closely tied to users’ reputations, thereby boosting their trustworthiness. An exception is with influencers or other reviewers who have received products or services at no cost. Sites like beauty retailer Sephora have added options for customers to reveal this fact, letting readers assess the review with that in mind. Seen as another column in a spreadsheet, promotional-related reviews can be flagged by AI text analysis.

The stories that customers tell create a more personal connection between storyteller and reader. That human connection is persuasive in a different way than the persuasion of marketing and branding language.

On brand and review sites, customers can also pick and choose which reviewers they relate to and, because some sites let reviewers categorize themselves by age, gender, location and other details, consumers can filter the reviews to only see the ones most relevant to them. Lastly, reviews let people imagine the experience of a product or service, a process of “affective forecasting” that embeds the idea deeper into people’s minds and affects decision-making.

What brands can learn from analyzing reviews

Driven by its founder’s own skin concerns and desire for a tailored solution, Proven Skin Care used AI to analyze the data of millions of consumer reviews and survey results alongside ingredients, products and scientific journal articles to create their groundbreaking Skin Genome Project database, the key to how the company tailors its product recommendations to customer’s unique needs. Data leads the brand and its marketing.

Branded as a holistic and scientific skin care company that looks at a customer’s lifestyle, environment and genetics to formulate a unique skin care regimen for them, Proven Skin Care puts customers both at the centre of its brand and at the centre of the data it uses. The resulting product reviews reflect customer satisfaction and loyalty on a personal level, where customers write detailed reviews of products while also sharing their own stories and expressing a strong emotional connection to the brand.

When used to customize products and services to a customer’s needs, with a goal of boosting sales and loyalty through personalized connection between brand and customer, AI analysis of data falls into a “cool” rather than “creepy” category. That is, a brand’s empathy for a customer’s needs is followed through with a personalized retail experience.

Analyzed through AI text analytics, customer review data reveals:

  • Who your customers are, what their primarily concerns are and what they expect from your brand.
  • Whether products or services work or fail and how, as well as what improvements could be made.
  • The perceptions and values that customers have around your brand and how they see your brand in relation to competitors.
  • Ideas for next steps for marketing campaigns, improving CX and closing gaps in the customer journey, as well as new features for products and services.

When used to customize products and services to a customer’s needs, with a goal of boosting sales and loyalty through personalized connection between brand and customer, AI analysis of data falls into a coolrather than “creepy” category.

AI text analysis provides the most efficient and insightful way to use reviews to your brand’s advantage. For example, you can see how many customers share the same concerns even if their reviews are worded differently. Fast-tracking data analysis leads to faster and more accurate responses to customer concerns. With these tools in your pocket, your CX strategy can include improved customer response.

When you gather and analyze reviews through AI text analytics tools like Keatext, which has no limits on how much structured and unstructured data it can ingest, you can take action that improves your CX and marketing strategies, increases sales potential and leads to making relevant changes to products and services. Looked at in tandem with NPS and other CX markers, AI insights based on online reviews offer a more complete picture of what customers think of your products and services as well as your overall brand. Those measurable insights can also lead to justifying the ROI value of those strategies to executives across your organization.

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