⚙️ What This Mega-Prompt Does:
- Constructs a detailed customer engagement data table for a company, including metrics like engagement channel, frequency of contact, and engagement score.
- Utilizes visual keys (emojis and colors) to represent engagement channels and contact frequency, enhancing data readability and interpretation.
- Provides analytical insights and actionable recommendations based on the calculated engagement scores and customer interaction patterns.
❓Tips:
- Utilize advanced data visualization tools to enhance the presentation of the customer engagement table, making it easier to identify trends and patterns at a glance.
- Develop a dynamic dashboard that allows stakeholders to filter and sort the engagement data by different parameters, such as engagement score or frequency of contact, to tailor insights to specific needs.
- Schedule regular review meetings with key stakeholders to discuss the insights from the customer engagement data, ensuring continuous improvement and alignment with business strategies.
❓ Customer Engagement Analyst ChatGPT Mega Prompt
#CONTEXT:
You are an expert in customer analytics tasked with generating a table showcasing key customer engagement data for a given company. Your goal is to analyze the data to surface insights and provide recommendations to improve customer engagement.
#ROLE:
You are a customer analytics expert with deep knowledge of engagement metrics across various marketing channels.
#RESPONSE GUIDELINES:
Generate a table with the following columns:
- Customer Name
- Engagement Channel (using emoji key)
- Frequency of Contact (using color key)
- Engagement Score (calculated based on provided methodology)
Include an Engagement Channel Key using emojis:
- ❓ Email
- ☎️ Phone
- ❓ Website
- ❓ Mobile App
- ❓ In-Store
Include a Frequency Key using colors:
❓ Daily
❓ Weekly
❓ Monthly
❓ Quarterly+
Calculate the Engagement Score on a scale of 1-100 based on a weighted average of the following factors:
- Recency of last interaction (30%)
- Total number of interactions in last 90 days (30%)
- Diversity of engagement channels used (20%)
- Depth of engagement (e.g. time spent, actions taken) (20%)
Provide three key takeaways and three recommendations based on your analysis of the customer engagement data.
#TASK CRITERIA:
- Focus on surfacing the most important insights from the customer engagement data
- Provide actionable recommendations to improve customer engagement
- Use the provided keys and methodology to ensure consistency in the data presentation
- Avoid including any personally identifiable information in the table
#INFORMATION ABOUT ME:
- Company Name: [COMPANY NAME]
#RESPONSE FORMAT:
Customer Engagement Metrics for [COMPANY NAME]
| Customer Name | Engagement Channel | Frequency of Contact | Engagement Score |
|---------------|--------------------|-----------------------|------------------|
| $name1 | $channel1 | $frequency1 | $score1 |
| $name2 | $channel2 | $frequency2 | $score2 |
| $name3 | $channel3 | $frequency3 | $score3 |
| $name4 | $channel4 | $frequency4 | $score4 |
Engagement Channel Key:
- ❓ Email
- ☎️ Phone
- ❓ Website
- ❓ Mobile App
- ❓ In-Store
Frequency Key:
❓ Daily
❓ Weekly
❓ Monthly
❓ Quarterly+
Engagement Scoring Methodology:
The Engagement Score is calculated on a scale of 1-100 based on a weighted average of the following factors:
- Recency of last interaction (30%)
- Total number of interactions in last 90 days (30%)
- Diversity of engagement channels used (20%)
- Depth of engagement (e.g. time spent, actions taken) (20%)
Key Takeaways:
- $insight1
- $insight2
- $insight3
Recommendations:
- $recommendation1
- $recommendation2
- $recommendation3❓How To Use The Prompt:
- Fill in the [COMPANY NAME] placeholder with the specific name of the company you are analyzing. For example, if you are analyzing customer engagement for "Acme Corp", replace [COMPANY NAME] with "Acme Corp".
- Example: In the table header and throughout the document, replace [COMPANY NAME] with "Acme Corp" to personalize the analysis report.
❓ Example Input:
#INFORMATION ABOUT ME:
- Company Name: God of Prompt, the biggest collection of easy-to-follow AI resources, AI Prompts & How-to Guides for busy Small Business Owners.
❓ Example Output:
❓Additional Tips:
- Ensure data accuracy and consistency by regularly updating and verifying the customer engagement metrics to reflect the most recent interactions and engagement scores.
- Incorporate customer feedback and sentiment analysis into the analysis to gain a deeper understanding of customer preferences and satisfaction levels, which can further enhance the recommendations provided.
- Leverage machine learning algorithms to predict future customer engagement trends based on historical data, enabling proactive strategies to improve engagement and retention rates.
- Collaborate with cross-functional teams, such as marketing, sales, and customer service, to align on the insights and recommendations derived from the customer engagement data, fostering a holistic approach to enhancing customer experiences.