---
id: c2a4f6d1-9b7e-4c2b-8d3f-1e6a7b9d5c21
name: Recommend optimal pricing adjustments
---

Detect pricing optimization opportunities by analyzing demand patterns, price sensitivity, and external factors to maximize revenue and margin.

## Known Tables & Columns

### `curated.pos_transaction_items`
- `ITEM` (varchar) - menu item identifier
- `ITEM_NAME` (varchar) - menu item name
- `QTY` (int) - quantity sold
- `PRICE` (decimal) - selling price
- `NET_SALES` (decimal) - total revenue from item
- `BUSINESS_DATE` (date) - transaction date
- `HOUR` (int) - hour of sale
- `REVN_CENTER` (varchar) - revenue center (Dining Room, Bar, Delivery)

### `curated.pos_hourly_sales`
- `ITEM` (varchar) - menu item identifier
- `DATE` (date) - transaction date
- `HOUR` (int) - hour of sale
- `QTY_SOLD` (int) - quantity sold
- `SALES_AMOUNT` (decimal) - total sales
- `REVN_CENTER` (varchar) - revenue center

### `curated.pos_transactions`
- `DATE` (date) - transaction date
- `REVN_CENTER` (varchar) - revenue center
- `TOTAL_REVENUE` (decimal) - total revenue
- `TOTAL_ORDERS` (int) - number of orders

## Analysis Approach

1. Analyze item-level sales for the last **90 days** to determine demand levels and revenue contribution for each menu item.

2. Identify **demand drivers**:
   - high-demand and low-demand items based on sales volume
   - demand patterns by **hour of day**
   - demand patterns by **day of week**
   - differences between **weekdays and weekends**
   - demand shifts across **revenue centers**.

3. Establish a **baseline demand** for each item using normal (non-holiday, typical weather) periods.

4. Detect deviations from baseline caused by:
   - **holidays and special periods** (e.g., Christmas, Easter, New Year, long weekends)
   - **weather conditions** (rain, temperature, wind impact on dine-in vs delivery).

5. Estimate **price sensitivity (elasticity proxy)**:
   - compare changes in price vs changes in quantity sold over time
   - identify items with **low sensitivity** (stable demand despite price variation)
   - identify items with **high sensitivity** (demand strongly affected by price).

6. Identify pricing opportunities:
   - **peak-hour pricing** for high-demand, low-sensitivity items
   - **off-peak discounts** for low-demand periods
   - **weather-based pricing adjustments** (e.g., delivery during rain.  Use get_weather_forecast tool to check weather for Orlando. Get current weather conditions and forecast.)
   - **holiday/event-based pricing strategies**
   - **revenue center-specific pricing differences**.

7. Validate that each item has sufficient data (minimum **30 units sold**) to ensure reliable recommendations.

8. Avoid overly aggressive pricing changes unless supported by strong and consistent demand patterns.

9. Balance **price increase vs expected volume drop**, prioritizing overall revenue and margin improvement.

10. Generate concise pricing recommendations including:
   - item name
   - context (time, day, weather, event, revenue center)
   - demand insight
   - recommended price adjustment (percentage, discount, bundle, dynamic pricing)
   - expected impact (revenue, volume, margin)
   - short reasoning.

11. Minimize database usage:
   - prefer aggregated summaries
   - do not request more than **3 queries** unless necessary
   - reuse previously retrieved data when possible.

## Severity Guidance

- **Low**: Small pricing optimization opportunity with limited impact.
- **Medium**: Clear pricing inefficiency across specific time periods or segments.
- **High**: Strong, consistent demand imbalance or pricing inefficiency with significant revenue upside.