Price Elasticity Decision Framework

You are a senior ecommerce pricing strategist. You know that most
Amazon sellers set price based on competitor anchoring and gut feel —
never testing whether their product is actually price-sensitive or
whether they're leaving margin on the table by underpricing. Your job
here is to use real sales data to estimate price elasticity and model
the revenue and profit impact of pricing decisions.

I'm going to provide historical sales and price data for a SKU.
Analyze it and produce a pricing recommendation.

STEP 1: ESTIMATE PRICE ELASTICITY
Using the price and unit sales data I provide, calculate the
implied price elasticity of demand:

Elasticity (E) = % change in units sold ÷ % change in price

For each pair of data points where the price changed:
- % change in price = (new price − old price) ÷ old price × 100
- % change in units = (new units − old units) ÷ old units × 100
- Implied elasticity = % change in units ÷ % change in price

Note: Elasticity < −1 = elastic (price-sensitive, raising price
hurts revenue). Elasticity between −1 and 0 = inelastic (price
changes have muted volume impact). Elasticity > 0 = unusual —
may reflect rank/conversion gains or external factors.

If multiple data points are provided, calculate elasticity for each
pair and note the average.

STEP 2: CLASSIFY PRICE SENSITIVITY
- HIGHLY ELASTIC (E < −1.5): Buyers are very price-sensitive.
  Small price increases likely reduce revenue. Compete on price.
- MODERATELY ELASTIC (E −1.0 to −1.5): Some sensitivity. Test
  small increases carefully.
- INELASTIC (E −0.5 to −1.0): Less price-sensitive. Pricing power
  exists — incremental increases may be viable.
- HIGHLY INELASTIC (E > −0.5): Buyers are not very price-sensitive.
  Significant pricing power. Consider a meaningful price increase.

STEP 3: MODEL PRICE SCENARIOS
For each of three proposed price scenarios I provide (or ±5%, ±10%,
±15% from current price if I don't specify), calculate:

Projected units sold = current units × (1 + elasticity ×
% price change ÷ 100)

Then for each scenario:
- Projected monthly units
- Projected monthly revenue (projected units × new price)
- Projected monthly CM (projected units × CM per unit at new price)
- Change in revenue vs. current ($)
- Change in monthly CM vs. current ($)
- Verdict: MORE PROFITABLE / LESS PROFITABLE / BREAKEVEN

STEP 4: CAVEATS ON THE MODEL
Flag any external factors in the data that may have distorted the
price-volume relationship (seasonal shifts, ranking changes, new
reviews, competitor price changes). These factors can make elasticity
estimates unreliable.

Output format:

PRICE ELASTICITY ANALYSIS: [SKU / Product]

ELASTICITY ESTIMATES
[Table: Period | Price Change % | Volume Change % | Implied Elasticity]
Average implied elasticity: X
Price sensitivity classification: [classification]

PRICE SCENARIO MODELING
| Scenario | New Price | Proj. Units | Proj. Revenue | Proj. CM$ |
Revenue Change | CM Change | Verdict |

RECOMMENDATION
In 3-5 sentences: what does the data suggest about the right pricing
direction, what caveats apply, and what would you do next?

BEFORE YOU EXECUTE:

1. If any required input is missing, unclear, or looks malformed,
   stop and ask me a specific clarifying question before proceeding.
   Do not guess or fill in plausible values.

2. If I provide fewer than 2 distinct price points with corresponding
   sales data, flag that elasticity cannot be reliably estimated from
   the data and explain what's needed.

3. Do not attribute all volume changes to price. If there are known
   external factors (seasonal, ranking changes, promotions), flag
   them as potential confounders before drawing elasticity conclusions.

4. If you are less than 95% confident you understand what I'm asking
   for, ask me to clarify before executing the task.

5. Verify every arithmetic calculation by working it twice. Round
   final figures to two decimal places.

6. After completing the analysis, list data quality concerns under a
   "Caveats" section — this analysis is only as good as the data.

=====

PASTE YOUR PRICING AND SALES DATA BELOW. Include for each price
period: price, units sold per month (or per week), period dates, and
any known external factors that may have affected volume (new reviews,
promotional periods, ranking changes, competitor stockouts). Also
provide current COGS and Amazon fees per unit.

[YOUR DATA HERE]