The Digital Deep Dive With Aaron Conant

Strategically Leveraging Ratings and Reviews To Drive Growth

Thu Jun 01 2023

reviewsratingsanalysiscustomer feedbackdata-driven decisionsAI integrationproduct descriptionsbrand strategiesonline shoppingretail trends

Description

The episode covers the importance of reviews and ratings analysis, utilizing reviews and ratings for business success, optimizing product changes and AI integration, and enhancing product descriptions and brand strategies. It explores the strategy behind reviews analysis, the impact of customer feedback, and the use of data-driven claims. The episode also delves into the role of AI in analyzing text sentiment and generating summaries. Additionally, it discusses the challenges faced by smaller brands and trends in product design and retail strategies.

Insights

Reviews and ratings analysis is crucial for understanding performance and competition

Analyzing reviews and ratings provides insights into consumer sentiment, helps benchmark against competitors, and informs product improvements.

Data-driven claims based on reviews and ratings lead to more effective business actions

Using evidence from reviews and ratings allows organizations to make informed decisions and drive impactful changes.

AI plays a significant role in analyzing text sentiment and optimizing product descriptions

Neural networks like GPT can accurately determine sentiment and generate concise summaries. AI can also be used for generative purposes, such as optimizing product descriptions.

Smaller brands can leverage AI to maintain multiple product detail pages efficiently

AI tools like GPT can help smaller brands manage and edit numerous product detail pages more effectively, enabling them to pivot quickly.

Designing products for online shopping and considering online ratings are emerging trends

Brands are shifting their focus towards digital shelf optimization, leveraging online ratings and reviews, and exploring new retail strategies like drop shipping.

Chapters

The Importance of Reviews and Ratings Analysis
Utilizing Reviews and Ratings for Business Success
Optimizing Product Changes and AI Integration
Enhancing Product Descriptions and Brand Strategies

Summary

Transcript

The Importance of Reviews and Ratings Analysis

00:04 - 13:26

Ratings and reviews are often taken for granted, but there is a whole next level of strategy behind them.
Reviews analysis is important to understand what people are talking about in the reviews.
When you start getting around 10-20 new reviews per week, it's important to start analyzing them.
Once you reach thousands of reviews or want to benchmark against competitors, a tool for analyzing reviews becomes necessary.
Comparing your star rating to the market average is crucial for understanding your performance.
Best-in-class organizations have visibility into every retailer they sell on and compare themselves to specific competitors.
Analyzing competition and doing sentiment analysis beyond star ratings is key.
Having organized data is important for comparing to competitors and specific brands on different retailers.
Analyzing reviews and ratings metrics goes beyond star rating and volume, it involves understanding trends, market-wide impact, and retailer-specific performance.
Customer feedback from reviews and ratings can affect various aspects of the organization, so it's crucial to get that information to the right people and drive action based on it.
Making data-driven claims with evidence from reviews and ratings can lead to more effective business actions.
Looking at your own product's reviews is not enough; you need to also consider the competition's performance.
Identifying strategic products of competitors in each marketplace helps determine where you stand in relation to them.
Using data to craft a different Product Detail Page (PDP) based on consumer expectations and meeting their needs is a critical use case.
Reframing messaging on PDPs can help meet consumer expectations, improve average star rating, and reduce negative feedback.
Adjusting what is important based on consumer feedback helps improve product offerings.

Utilizing Reviews and Ratings for Business Success

13:19 - 27:53

Adjusting product emphasis based on reviews and ratings
Switching bullet point order based on customer feedback
Automated pipeline to target competitors with drop in sentiment or rating
Sorting reviews by most recent is important for shoppers
Using ratings and reviews instead of focus groups for accurate data
Verified reviews provide accurate audience insights
Reviews and ratings analysis answers sentiment-related questions
Using Yogi data to inform survey questions and hypotheses
Gathering reviews and market analysis is crucial when entering a new product category
Understanding competitor performance and customer preferences helps differentiate products
Using data from real customers in product development leads to better outcomes
Promotional reviews can boost initial ratings, but organic reviews reflect true product performance
Monitoring and addressing issues early in a new product launch is essential for success
Prioritizing changes based on feedback can be done by considering the timeline of impact

Optimizing Product Changes and AI Integration

27:33 - 41:35

Low hanging fruit for making changes include PDP and advertising strategy changes.
Understanding the 'why' behind product issues is important, as some may require long-term changes while others can be addressed quickly.
DDP changes and targeting underperforming competitors are examples of relatively quick enhancements that can be made.
Medium changes involve market positioning and emphasizing certain claims.
Longer-term changes may involve packaging, product, or innovation.
Even small enhancements that improve conversion rates by just 1% can have a significant impact on ROI.
There is often internal pushback against making changes due to resistance to change and personal attachment to products.
Aggregating data to build a case for internal changes is crucial, especially when significant updates are needed.
Making a business argument with data-backed evidence is more likely to result in action being taken.
AI is another topic worth discussing.
AI is a subset of artificial intelligence called neural networks, which is used to analyze text and determine sentiment accurately.
Large language models like GPT are good at synthesizing qualitative information into concise points.
GPT can be used to generate summaries of product reviews and ratings, highlighting positive and negative aspects.
To make the business case, further analysis is needed to compare with competition, track trends over time, and compare across retailers.
AI can also be used for generative purposes, such as optimizing product descriptions or A+ content based on specific analysis.
The quality of results from AI improves when given a specific problem to solve rather than asking it to handle a broad range of feedback.
Writing effective prompts is crucial for obtaining high-quality content from AI models.
Currently, only a few people are using AI at an advanced level due to time constraints and lack of familiarity with the technology.
Smaller brands are more likely to embrace AI for tasks like generating product descriptions.

Enhancing Product Descriptions and Brand Strategies

41:10 - 52:43

Aaron is the smaller brands, right? The ones that have, they're the ones that are like, 'Hey, I have 150 different PDPs to maintain. Like I'm not gonna rewrite all these bullets. So I'm gonna send it to GPT.'
But they also don't have 50 years of brand equity, right? They don't have legal teams or compliance teams who are brand teams.
The smaller brands can shift and pivot a lot quicker.
Smaller brands sometimes get acquired by big corporate behemoths and then their original vision gets ruined.
GPT can help larger brands maintain and edit multiple PDPs more efficiently.
Brands want to update their PDPs more frequently but struggle due to the number of products and the effort required.
Analyzing reviews and using advanced tools can make organizations more agile and data-driven.
The goal is to make teams operate more efficiently by reducing processes and optimizing PDPs.
There will be exponential effects as various inputs from different systems come together for optimized PDPs.
Some interesting trends include designing packaging for online sales and considering online ratings in physical stores.
Designing products for online shopping and in-store purchases
Shifting towards digital shelf first before physical shelf
Customers vetting products online through ratings and reviews
Omni-channel tracking of offline to online attribution using QR codes
Retail media networks emerging as a way to compete with Amazon
Drop shipping as a strategy for offering products across multiple marketplaces
Brands still aiming to get into physical stores for high volume sales
Changing frame of reference around ratings and reviews beyond star ratings
Winning players harvesting free, raw, and authentic data from customers