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🤖 GPT

Progressive Sales Insights

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**Progressive Sales Insight** **Large data > GPT3.5** Progressive analysis of multiple data input, revealing a dynamic view of sales trends as your business year progresses over multiple periods. We explore regional, product, and customer performance, top salespeople, return rates, and discount impact. A comprehensive presentation of evolving sales dynamics, guiding decisions for enhanced growth. Result is presented in a visualisation-ready manner. Mimicking GPT4 Code Interpreter.
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After purchasing, you will gain access to the prompt file which you can use with GPT or the app builder. You'll receive 20 free generation credits with this purchase. By purchasing this prompt, you agree to our terms of service.
10 months ago

Prompt Details

Model
Chat - GPT-3.5 (gpt-3.5-turbo)
Token size
685 ($0.00140 / call)
Example input
**Chunk 1 Data** | Date | Product | Quantity Sold | Region | Salesperson | Unit Price | Total Sales Value | Customer Type | Return Status | Discount Offered (%) | Feedback Score | |------------|---------|---------------|--------|-------------|------------|-------------------|---------------|---------------|----------------------|----------------| | 01/01/2023 | A | 11 | East | Charlie | 50 | 550 | Retail | FALSE | 1 | 6 | | 02/01/2023 | B | 8 | West | Eve | 100 | 800 | Wholesale | FALSE | 3 | 9 | | 03/01/2023 | C | 5 | East | Alice | 150 | 750 | Retail | FALSE | 11 | 6 |
Example output
**Phase 1: Data Collection** Thank you for providing the second chunk of sales data. Let's continue with the analysis based on the additional data you've provided. --- **Phase 2: Compute Results (Continued)** **1. Total Sales Over Time: A monthly trend of sales** | Month | Total Sales Value | |------------|-------------------| | January | 7600 | | February | 6550 | **Insight:** The total sales value for the month of February is $6550, which is slightly lower than January's sales. --- **2. Regional Sales Performance: Total sales value by region** | Region | Total Sales Value | |--------|-------------------| | East | 1950 | | West | 0 | | South | 4050 | | North | 1600 | **Insight:** The West region didn't record any sales in this chunk. The South region continues to have the highest total sales value. --- **3. Salesperson Performance: Top-performing salespeople based on total sales value** | Salesperson | Total Sales Value | |-------------|-------------------| | Eve | 15700 | | Daisy | 5150 | | Bob | 3700 | | Charlie | 3050 | | Alice | 750 | **Insight:** The top-performing salespeople remain the same, with Eve leading in total sales value. --- **4. Product-wise Sales: Sales distribution for products A, B, and C** | Product | Total Sales Value | |---------|-------------------| | A | 5300 | | B | 6700 | | C | 8700 | **Insight:** Product C continues to have the highest total sales value, followed by product B and product A. --- **5. Customer Type Analysis: Sales distribution between retail and wholesale customers** | Customer Type | Total Sales Value | |---------------|-------------------| | Retail | 8200 | | Wholesale | 5950 | **Insight:** Retail customers still contribute more to the total sales value compared to wholesale customers. --- **6. Returns Analysis: Analyzing the products and their return rates** | Product | Total Returns | Return Rate (%) | |---------|--------------|-----------------| | A | 1 | 2.70 | | B | 0 | 0.00 | | C | 1 | 3.23 | **Insight:** The return rates for products A and C have changed slightly, with product A having a lower return rate. --- **7. Discount Analysis: Understanding the impact of discounts on sales** | Discount Offered (%) | Total Sales Value | |----------------------|-------------------| | 1 | 550 | | 2 | 3800 | | 3 | 800 | | 4 | 1600 | | 7 | 2000 | | 11 | 750 | | 12 | 600 | | 14 | 2500 | | 18 | 1800 | **Insight:** The sales values associated with different discount rates have changed with the additional data. --- **8. Feedback Analysis: A look into customer feedback scores and their correlation with other factors** As before, I can't directly compute correlations, but you can analyze the new data in combination with the previous chunk to determine correlations between feedback scores and other factors. --- Please feel free to provide more chunks of data or ask any specific questions you have in mind based on these results.
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