Positive review analysis
Review collection
As a follow up to the previous blog post regarding the use of AI to assist in digital marketing, we will look at using AI to analyze customer reviews for adidas. To analyze customer reviews, I used ChatGPT by inserting specified prompts as well as copying and pasting Google Maps customer reviews starting with the positive reviews.
Analyzing positive review
First analysis
To begin this process, I went to Google Maps and searched for adidas retail store locations. I then navigated to the reviews and set the sort filter to the highest ratings. I selected a portion of the highest positive reviews since ChatGPT cannot analyze all reviews at once. For the first analysis of adidas reviews, I inserted the following specified prompt into ChatGPT followed by pasting the copied Google Maps reviews.
Prompt used:
What are the customers most satisfied with based on the reviews?
Result:
Based on customer reviews, adidas customers are particularly satisfied with the following aspects.
Product Style and Design:
Customers frequently mention that adidas products are stylish and trendy. Many reviewers appreciate the modern designs that can be worn both for workouts and everyday activities.
Comfort and Fit:
Customers highlight the comfort of adidas shoes and apparel. Many reviews mention cushioning, support, and overall wearability throughout the day.
Brand Reputation:
Customers trust adidas as a well-known global brand. Many reviewers mention brand loyalty and repeat purchases due to consistent quality.
Versatility of Products:
Customers appreciate that adidas products can be used for both athletic performance and casual wear, making them more valuable.
Second analysis (further analysis – word association)
The second analysis involves breaking down the positive reviews and examining the words associated with them. To do this, I entered another prompt into ChatGPT.
Prompt used:
Extract positive keywords from those reviews, and analyze the frequency and associations of these keywords.
Result:
Here is an analysis of positive keywords extracted from adidas customer reviews:
- Comfort”
- Frequency: Very high
- Associations: soft, supportive, all-day wear
- Style
- Frequency: High
- Associations: trendy, fashionable, modern
- Quality
- Frequency: High
- Associations: durable, reliable, well-made
- Fit
- Frequency: Moderate
- Associations: true to size, perfect fit
- Versatile
- Frequency: Moderate
- Associations: gym, casual, everyday wear
Recommendation
Now that we have identified positive trends, I entered another prompt into ChatGPT to generate recommendations.
Prompt used:
What would be suggestions from the findings?
Result:
Based on the review analysis, here are suggestions for adidas:
- Emphasize Style and Comfort in Marketing
adidas should continue highlighting the combination of fashion and comfort in advertising. - Promote Versatility
Showcase how products can be used for both workouts and everyday wear. - Use Customer Testimonials
Highlight real customer experiences to build trust and credibility. - Strengthen Brand Loyalty Programs
Encourage repeat purchases through rewards and exclusive offers. - Leverage Social Media Trends
Continue using influencers and digital campaigns to promote lifestyle appeal.
Negative review analysis
Review collection
To analyze negative reviews, I followed the same process. I went to Google Maps, searched for adidas store locations, and filtered the reviews to display the lowest ratings. I copied several one-star and two-star reviews and pasted them into ChatGPT.
Analyzing negative review
First analysis
For the first analysis, I entered the following prompt.
Prompt used:
What are the customers least satisfied with based on the reviews?
Result:
Based on the reviews, here are areas where adidas customers have reported dissatisfaction.
High Prices:
Some customers feel adidas products are expensive compared to other brands.
Sizing Issues:
Customers mention inconsistent sizing, especially when ordering online.
Durability Concerns:
A few customers report that certain products do not last as long as expected.
Online Order Issues:
Customers mention delayed shipping and difficulties with returns or exchanges.
Second analysis (further analysis – word association)
To further analyze the negative reviews, I entered another prompt.
Prompt used:
Extract negative keywords from those reviews, and analyze the frequency and associations of these keywords.
Result:
- Expensive
- Frequency: High
- Associations: overpriced, high cost
- Sizing
- Frequency: Moderate
- Associations: inconsistent, too small, too big
- Durability
- Frequency: Moderate
- Associations: wear out, not long-lasting
- Shipping
- Frequency: Moderate
- Associations: slow delivery, late orders
Recommendation
After identifying negative trends, I entered another prompt.
Prompt used:
What would be suggestions from the findings?
Result:
- Improve Pricing Strategy
Offer more promotions and discounts to stay competitive. - Standardize Sizing
Improve consistency in sizing across products. - Enhance Product Durability
Focus on quality control for long-term product use. - Optimize Online Shopping Experience
Improve shipping speed and return processes.