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Feature Requests

Add Dashboards and Charts to Compare Performance Across Repricing Strategies
I’d like to request the addition of visual dashboards and performance comparison charts that allow sellers to evaluate the effectiveness of different repricing strategies over time. Currently, while Aura provides individual SKU performance and basic reporting, there’s no consolidated view that helps sellers understand which strategies are truly driving better sales velocity, Buy Box retention, or profitability. As a result, optimizing pricing strategies often involves guesswork or manual data exports, which is inefficient and limits strategic decision-making. Current Limitation: 1) There’s no built-in way to compare performance metrics side by side across different repricing strategies. 2) Sellers must rely on manual SKU-level analysis or external spreadsheets to detect trends. 3) It’s difficult to identify which strategy performs better under specific market conditions, product categories, or price ranges. Proposed Functionality: 1) Introduce a “Strategy Performance Dashboard” where sellers can view and compare key metrics for each active strategy over a selectable time range (e.g., 7, 14, 30, 90 days). 2) Include charts and tables that display aggregated performance metrics, such as: 3) Total sales and revenue generated per strategy 4) Average profit margin and net profit per strategy 5) Buy Box win rate and average price positioning 6) Number of SKUs assigned to each strategy and their distribution 7) Conversion rate and sales velocity over time 8) Allow filtering by marketplace, category, supplier, or custom tags for deeper analysis. 9) Support side-by-side line or bar charts to visualize performance trends and detect patterns. Benefits: 1) Data-driven strategy optimization: Quickly identify which strategies are delivering the best results and which need adjustment. 2) Faster decision-making: No more manual data crunching to figure out what’s working. 3) Improved experimentation: Sellers can A/B test strategies and measure impact using real performance data. 4) Better scalability: Makes managing multiple strategies across large catalogs far more efficient.
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Use Historical Data to Identify the “Sweet Spot” Landing Price for Each Listing
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.
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Add Conditional Pricing Rules Within Strategies (Dynamic Adjustments Based on Sales Velocity)
I would like to request the addition of conditional pricing rules inside Aura’s repricing strategies, allowing sellers to automatically increase or decrease prices based on real-time sales activity. At the moment, strategies focus primarily on Buy Box position and competitor prices, but they don’t allow for dynamic adjustments when sales volume is unusually high or low within a specific time frame. Adding this functionality would give sellers much more granular control over pricing and inventory management. Current Limitation: 1) There’s no way to automatically react to sales velocity trends (e.g., slow sales indicating overpricing, or a sudden spike indicating the price might be set too low). 2) Sellers must manually monitor performance and adjust prices, which is inefficient and prone to delays. 3) High-volume sellers or those managing large catalogs can’t practically do this manually for each SKU. Proposed Functionality: 1) Within a repricing strategy, add a section for Conditional Pricing Rules, where sellers can define triggers based on recent sales activity. Example triggers could include: “If fewer than X units sold in the last Y hours, decrease price by Z%.” “If more than X units sold in the last Y hours, increase price by Z%.” “If no sales in the last Y hours, lower price to min price or reduce by a fixed amount.” “If sales exceed X units in Y hours, increase price up to max price to protect margins and inventory.” Allow these rules to stack or prioritize, with clear logic for how they interact with existing Buy Box–based strategies. Benefits: Better inventory control: Prevent running out of stock too quickly when demand spikes. Profit optimization: Capture higher margins when sales velocity indicates strong demand. Automated clearance: React faster to slow-moving items by adjusting prices downward. Reduced manual work: Minimize the need to constantly monitor and tweak prices manually. Optional Enhancements: 1) Support both percentage-based and fixed dollar adjustments.
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Add a Separate Field for Sales Tax Cost in Pricing Calculations
Currently, Aura’s pricing and profit calculations only include marketplace referral fees and do not provide a dedicated field to account for sales tax costs. As a result, sellers like myself have to manually combine sales tax with profit margins, which makes pricing calculations less transparent and harder to manage at scale. This is a significant limitation because not all sellers have or qualify for a reseller certificate, meaning sales tax must be paid upfront when sourcing products. For those sellers, the tax represents a real cost that directly impacts profitability, and it should be accounted for separately from profit. Problems with the Current Setup: 1) Inaccurate profit reporting: Sales tax is currently bundled into profit, which inflates profit margins and makes performance metrics unreliable. 2) Manual calculations required: Sellers must constantly adjust profit percentages to “bake in” tax, increasing the risk of errors. 3) Limited flexibility: Different jurisdictions have different sales tax rates, and some suppliers charge tax while others do not. There’s no easy way to reflect this variability in Aura. Proposed Functionality: 1) Add a dedicated “Sales Tax” field in the pricing settings for each product or globally at the account level. 2) Allow the tax amount to be entered either as a percentage (e.g., 8.25%) or a fixed amount per product. 3) Include the sales tax in the cost calculation formula so that profit margins and repricing rules are based on the true landed cost of the item. 4) Show sales tax as a separate line in the profit breakdown view, alongside referral fees and cost of goods. Benefits: 1) Accurate profit tracking: Sellers can clearly see profit margins after taxes and fees. 2) Simplified workflows: No need to manually adjust profit targets or embed tax costs in profit fields. 3) Better support for diverse seller profiles: This change accommodates both tax-exempt sellers and those who are not exempt, improving Aura’s usability across the board.
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Enable Web-Linked Supplier Price Triggers for Dynamic Repricing
I would like to request the addition of a feature that allows Aura users to link a product’s cost to a supplier’s advertised price on the web, so that repricing rules can automatically react to supplier price changes in real time. Tools like AutoDS already support this functionality by letting users associate a listing with a supplier product URL. A web scraper monitors that URL for price changes. When a change occurs, the system automatically updates the product cost inside the platform, which then triggers the repricing logic to adjust the min/max/target prices accordingly. This capability ensures: 1) Real-time cost accuracy: No need to manually update product costs when suppliers adjust their prices. 2) Reduced underpricing risk: Avoid selling at a loss due to delayed manual updates. 3) Improved scalability: Makes it easier to manage large catalogs with multiple suppliers. 4) Competitive advantage: Keeps pricing strategies aligned with actual market movements. Proposed Functionality: 1) Add a field in the product settings where sellers can input a supplier product URL. 2) Aura periodically checks (e.g., via web scraping or API where available) the supplier page for price updates. 3) When a price change is detected, Aura updates the Cost Price of the product automatically. 4) This update can then trigger the existing repricing rules to adjust the offer accordingly. Optional Enhancements: 1) Support for popular supplier platforms (e.g., Walmart, Amazon, AliExpress, Home Depot, etc.) as pre-configured templates.
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