Positive Review Analysis
Review Collection
Before going further into the post I would like to make it clear I used AI. Specifically ChatGPT as a digital tool. This blog showcases my ability to use prompts and find key words through reviews. Throughout this post I will show you the in depth process I used to analyze reviews for Black Hills Federal Credit Union(BHFCU).
Analyzing Positive Review
First Analysis
To start off this Analysis, I headed to Google Maps. I found Black Hills Federal Credit Union and clicked on their reviews. Then I clicked on the sort tab and picked the "highest rating" selection. After that I copied and pasted the reviews with the highest ratings into the text box. In ChatGPT I also asked: What are the customers most satisfied with based on the reviews? Below are the findings.
1. Friendly & Helpful Staff
This is mentioned over and over in your reviews:
- “so friendly and helpful”
- “working with Jess was a breeze”
- “patient with our endless questions”
- “truly cares about people”
2. Personalized, Human Experience
People don’t just say staff are nice—they say employees:
- Take time to explain things
- Help with complex issues
- Go above and beyond
3. Easy-to-Use Online & Mobile Banking
Multiple reviews highlight:
- “online banking is so easy to use”
- “app is secure and easy”
4. Trust & Reliability
Customers mention:
- Being with the bank for many years
- “always reliable for me”
- “28 years of the best banking”
5. Better Than Big Banks
Several reviews directly compare:
- “I won’t ever use a big bank again”
Second Analysis
To further this Analysis I wanted to know the keywords and categorize the reviews. In the chat box I prompted: Extract positive keywords from those reviews, and analyze the frequency and associations of these keywords. ChatGPT generated these findings below.
1. High Frequency
These appear repeatedly across multiple reviews:
- Friendly → Most dominant
- Helpful
- Easy (app / process)
- Great service
2. Medium Frequency
- Patient
- Reliable
- Knowledgeable
- Caring
3. Lower Frequency
- Efficient / Quick
- Welcoming
- Informative
- Accommodating
- Secure
Keyword Associations:
1. “Friendly” + “Helpful”
2. “Easy” + “App/Online Banking”
3. “Patient” + “Explained/Questions”
4. “Quick/Efficient” + “Helpful”
5. “Caring” + “Trust/Loyalty”
6. “Better Than Big Banks” + “Service”
Recommendation
To take this information from above and turn it into useable data. I put the following into the text box: What would be suggestions from the findings? Below are the results.
Negative Review Analysis
Review Collection
In this Analysis I will be using ChatGPT again. What is different is the emotions behind the reviews. This is a negative review analysis . This is important because it will show what can be improved rather than what to continuing building on. I will show the in depth process I used and the results I acquired.
Analyzing Negative Review
First Analysis
This time I sorted the Google Maps reviews under the "lowest rating" section. Unlike before where I asked what customers were most satisfied with. For the negative review analysis I asked in the text box: What were customers least satisfied with based on the reviews. Below is the information received.
1. Poor Customer Service
This is the most frequent complaint by far.
Customers mention:
- “extremely rude” staff
- “indifferent” attitudes
- “frustrating and miserable” experiences
- “terrible customer service”
- “rude and insensitive” responses
2. Difficulty Reaching Support
Many complaints focus on communication issues:
- Long hold times (20+ minutes)
- Calls going to voicemail
- “Does anyone answer the phone?”
- Not being able to reach account support
3. Inconsistent Branch Experience
Customers specifically mention:
- Some branches being worse than others
- Certain locations having rude or unhelpful staff
- Preference for one branch over another
4. Process & Policy Frustrations
Customers were unhappy with:
- Excessive ID requirements (even for simple tasks)
- Confusing or strict policies
- Lack of flexibility in basic transactions
5. Technology & Access Issues
Some reviews highlight:
- App failures
- ATM outages
- Delays accessing deposited money
- Limited control over transactions (e.g., bill denominations)
6. Fees & Financial Handling Issues
Serious complaints include:
- Unexpected fees (inactive account fees)
- Force-placed insurance issues
- Delinquent payments caused by system/process errors
7. Long Delays & Inefficiency
Examples:
- Waiting years for loan decisions
- Delays in resolving issues
- Slow processes overall
8. Lack of Transparency / Trust Concerns
Some customers mention:
- Arbitration concerns
- Policy changes without clear communication
- Feeling misled or uninformed
Second Analysis
Next in the text box I prompted: Extract negative keywords from those reviews, and analyze the frequency and associations of these keywords. This will show the trend people commonly feel and the information generated is below.
High Frequency
- Rude / Poor customer service → most dominant
- Frustrating / Miserable experience
- Unreachable / On hold / No response
Medium Frequency
- Issues / Problems
- Slow / Delays
- Fees / Charges
- Confusing / Complicated processes
Lower Frequency
- Incompetent
- Denied (loans/services)
- Failing (app/ATM issues)
- Delinquent (payment issues)
Keyword Associations:
- “Rude” + “Customer Service”
- “On Hold” + “Unreachable” + “Voicemail”
- “Confusing” + “Complicated” + “Policies”
- “Fees” + “Charges” + “Delinquent”
- “Failing” + “App” + “Issues”
- “Slow” + “Delayed” + “Denied”
- “Rude” + “Branch” (Location-Based Complaints)
Recommendation
After the information above was generated I asked ChatGPT: What would be suggestions from the findings? Below is the information to improve the brands identity.
1. Improve Customer Service Training (Top Priority)
Finding: “Rude,” “indifferent,” and “terrible service” are the most frequent complaints.
Suggestion:
- Provide additional training focused on:
- Empathy and tone
- Handling sensitive situations (fraud, disputes)
- Active listening
- Set clear service expectations across all branches
2. Fix Communication & Response Times
Finding: Customers struggle to reach support (on hold, voicemail, no answers).
Suggestion:
- Increase staffing for phone support
- Add callback options instead of long hold times
- Expand support channels (chat, text, email)
- Set response time standards
3. Standardize Service Across All Locations
Finding: Experiences vary significantly by branch.
Suggestion:
- Create consistent service guidelines
- Monitor branch-level performance
- Use customer feedback to identify weak locations
- Provide additional training where needed
4. Simplify Processes & Policies
Finding: Customers feel processes are confusing and overly complicated.
Suggestion:
- Review and simplify:
- ID requirements
- Transaction processes
- Account procedures
- Clearly explain why certain steps are required
5. Increase Transparency Around Fees
Finding: Complaints about unexpected charges and penalties.
Suggestion:
- Clearly communicate:
- Fees
- Policies (e.g., inactive accounts, insurance requirements)
- Send reminders or alerts before fees are applied
- Provide simple explanations in plain language
6. Improve Technology Reliability
Finding: Issues with app, ATMs, and accessing money.
Suggestion:
- Invest in system stability and updates
- Quickly communicate outages or issues
- Provide backup options when systems fail
7. Speed Up Service & Decision-Making
Finding: Complaints about delays (loans, support, transactions).
Suggestion:
- Streamline approval processes
- Set clear timelines for services (especially loans)
- Keep customers updated during delays
8. Improve Transparency & Trust
Finding: Concerns about policies (e.g., arbitration, account handling).
Suggestion:
- Communicate policy changes clearly and early
- Use simple, easy-to-understand language
- Be proactive rather than reactive