ChatGPT Prompt to Analyze Open-Ended Survey Responses

ROLE: You are a data analyst with access to a MongoDB database through an MCP connection.

CONTEXT:
- Database: [DATABASE_NAME]
- Collection: [COLLECTION_NAME]
- Relevant fields: [FIELD_NAMES]
- Time range or filter (if applicable): [DATE_RANGE_OR_CONDITION]

TASK:
1. Query the specified collection through the MCP connection to answer this question: [QUESTION_IN_PLAIN_LANGUAGE].
2. Show the exact query or aggregation pipeline used.
3. Report the total number of documents the query matched.
4. Summarize the result in 2-3 plain-language sentences a non-technical stakeholder could understand.
5. Flag anything unusual in the data, such as missing fields, unexpected nulls, or a result that seems inconsistent with the time range given.

CONSTRAINTS:
- Only report numbers that came from the actual query result, not an estimate.
- If the MCP connection cannot reach the collection, say so instead of guessing an answer.

OUTPUT FORMAT:
- Query used: [pipeline or filter]
- Documents matched: [count]
- Summary: [plain-language explanation]
- Notes: [anything unusual, or "none"]

ROLE: You are a customer analytics specialist helping me segment customers using the RFM (Recency, Frequency, Monetary) framework.

CONTEXT:
- Data: [DESCRIBE YOUR DATA - e.g. "a CSV with columns: customer_id, order_date, order_total"]
- Analysis period: [TIME RANGE, e.g. "last 12 months"]
- Business type: [E.G. ECOMMERCE, SUBSCRIPTION, RETAIL]
- Number of segments wanted: [NUMBER, e.g. "5-7 named segments"]

TASK:
1. First, review the data I provide and flag any quality issues (missing values, duplicate orders, refunds, or inconsistent date formats) before scoring anything.
2. Calculate Recency, Frequency, and Monetary values per customer based on the period above.
3. Score each metric from 1 (worst) to 5 (best) relative to the full customer base.
4. Combine the three scores into a single RFM code and assign each customer to a named segment (e.g. Champions, Loyal Customers, At Risk, Lost).
5. Summarize how many customers fall into each segment and one recommended action per segment.

CONSTRAINTS:
- State the scoring thresholds you used so the method can be repeated on future data
- Do not invent data points that are not in the file I provide
- If a required column is missing, ask me for it instead of assuming

OUTPUT FORMAT:
- A table: customer_id | recency_score | frequency_score | monetary_score | RFM_segment
- A short summary section listing segment names, customer counts, and one action per segment

DATA:
[PASTE OR DESCRIBE YOUR CUSTOMER PURCHASE DATA HERE]

ROLE
You are a data analyst specializing in customer lifetime value (LTV) and cohort analysis.

CONTEXT
I am analyzing transaction data for [BUSINESS TYPE / PRODUCT] to understand customer lifetime value by acquisition cohort.
Data provided: [DESCRIBE COLUMNS, E.G. CUSTOMER_ID, TRANSACTION_DATE, REVENUE, CHANNEL]
Time range: [START DATE] to [END DATE]
Cohort definition: [E.G. SIGNUP MONTH, ACQUISITION CHANNEL, CAMPAIGN NAME]

TASK
1. Group customers into cohorts based on [COHORT DEFINITION].
2. For each cohort, calculate cumulative revenue per customer at [TIME INTERVALS, E.G. MONTH 1, 3, 6, 12].
3. Identify which cohorts show the fastest payback relative to an acquisition cost of [CAC VALUE, IF KNOWN].
4. Flag any data quality issues (duplicate IDs, missing dates, negative revenue from refunds) that could distort the results.
5. Summarize which cohorts are most and least valuable, and why.

CONSTRAINTS
- Do not blend one-time revenue (shipping, tax, fees) into the LTV calculation unless explicitly told to.
- State any assumptions made about missing or ambiguous data.
- Keep the cohort definition consistent across the entire analysis.

OUTPUT FORMAT
- A short summary of findings (3-5 sentences)
- A table of cohorts with cumulative LTV at each time interval
- A ranked list of cohorts from most to least valuable
- A list of data quality flags, if any