I would like to request a feature that uses historical performance data to estimate the optimal (sweet spot) landing price for each SKU. The landing price should reflect the point at which a listing historically achieved the best balance between sales velocity and profit margin, rather than relying solely on static min/max ranges or manual experimentation.

Current Limitation:

1) Pricing today is mostly rule-based (e.g., undercutting competitors or matching Buy Box), but it doesn’t consider how the listing has actually performed at different price levels over time.

2) Sellers must manually review sales reports to guess the ideal price point, which is inefficient and often inaccurate.

3) There’s no built-in tool to correlate price vs. sales performance, price vs. Buy Box win rate, or price vs. profit over historical periods.

Proposed Functionality:

1) Aura could analyze historical sales, traffic, Buy Box wins, and profit data for each SKU and identify the price range that delivered the highest overall performance.

2) The system would calculate and display a “Sweet Spot Price” for each listing, derived from actual data trends such as:

3) Highest revenue achieved per day/week at specific price points

4) Best conversion rates or sales velocity at a given price

5) Correlation between price changes and Buy Box retention

6) Profitability curves across historical price variations

7) Sellers could then use this suggested sweet spot price to set their min, max, or target price more intelligently, or even allow the repricer to automatically anchor around that optimal point.

Benefits:

1) Data-driven pricing decisions: Remove guesswork by using actual performance data to guide pricing.

2) Higher profitability: Identify prices that historically produced the best profit margins, not just lowest prices.

3) Improved competitiveness: Maintain pricing close to proven performance levels while reacting to market conditions.

4) Reduced manual analysis: Automate what is currently a time-consuming task for sellers managing many SKUs.

Optional Enhancements:

1) Allow users to define the optimization goal (e.g., maximize profit, maximize revenue, maximize Buy Box win rate).

2) Visualize historical price vs. performance curves in charts.

3) Provide periodic “sweet spot refresh” reports, so sellers can adapt pricing as trends shift.

4) Combine this with AI (optional) to automatically update target prices based on evolving data patterns.