Positive Review Analysis:
Review Collection:
This week, I am looking at positive reviews for Nike using google reviews. Using Chat GPT, I will analyze the reviews to find what customers are most satisfied about, key words in the reviews, and what is suggested in the reviews.
Analyzing Positive Reviews:
First Analysis:
To find information on how customers are feeling towards Nike, I gave Chat GPT a prompt along with 4 and 5 star reviews.
Prompt: What are the customers most satisfied with based on the reviews?
Chat GPT gave this response:
“Based on the reviews you provided, customers are most satisfied with a few clear themes. Here’s a breakdown with patterns and examples:”
The main theme includes:
-Customer service
-Price and deals
-Product selection
-Fast and easy shopping experience
-Store environment
Second Analysis:
To find common words and associations in the reviews, I used the following the prompt.
Prompt: Extract positive keywords from those reviews, and analyze the frequency and associations of these keywords.
Chat GPT gave this response:
“Here’s a structured positive keyword extraction + frequency & association analysis based on the reviews you provided.”
Common words included:
-Friendly
-Helpful
-Affordable
-Fast
-Organized
I asked Chat GPT to create marketing recommendations for Nike.
Prompt: What would be suggestions from the findings?
Chat GPT gave this response:
“Based on the keyword frequency and associations, the store is already strong in customer service, pricing, and selection—so the best recommendations focus on leveraging strengths and fixing minor pain points (like crowding and wait times).”
Results included:
-Double down on customer service
-Promote deals more strategically
-Enhance product discovery experience
-Reduce wait times
-Maintain store cleanliness
Analyzing Negative Reviews:
Review Collection:
Next I will look at negative reviews for Nike using google reviews. Using Chat GPT, I will analyze the reviews to find what customers are least satisfied about, key words in the reviews, and what is suggested in the reviews.
First Analysis:
To find information on how customers are feeling towards Nike, I gave Chat GPT a prompt along with 1 and 2 star reviews.
Prompt: What are the customers least satisfied with based on the reviews?
Chat GPT gave this response:
“Here’s a clear breakdown of what customers are least satisfied with based on these negative reviews, including patterns and insights.”
The main themes included:
-Poor customer service
-Inaccurate store times
-Pricing and value disappointments
-Poor return and refund policies
-Lack of basic store operations
Second Analysis:
To find common words and associations in the reviews, I used the following the prompt.
Prompt: Extract negative keywords from those reviews, and analyze the frequency and associations of these keywords.
Chat GPT gave this response:
“Here’s a structured negative keyword extraction + frequency and association analysis based on the reviews you provided.”
Common words included:
-Unprofessional
-Unexpected closures
-Overpriced
-Strict policies
-Poor selection
Recommendation:
I asked Chat GPT to create marketing recommendations for Nike.
Prompt: What would be suggestions from the findings?
Chat GPT gave this response:
“Based on the negative keyword analysis, it’s clear the main pain points are poor customer service, inconsistent store hours, operational inconveniences, and rigid policies. The suggestions should focus on reducing these negative experiences while maintaining strengths. Here’s a structured plan:”
Results included:
-Improve customer service consistency
-Ensure accurate store hours
-Simplify return policies
-Improve operational convenience
-Address inventory concerns
Personal Overview:
I thought it was interesting how Chat GPT was able to pull this information from Google reviews. I think it is interesting to note that Nike did not have very many negative reviews. Chat GPT did a great job detecting keywords, and making each response not too overwhelming. Because of these simplified responses, it made it easier to understand the consumer behavior. This can be helpful in my future career in marketing research to be able to see where a company can do better, and what they are doing good at, at that moment.
