Actor Proposal

Actor Name: SmartPriceOptimizer

Actor Description: SmartPriceOptimizer is an advanced actor that helps e-commerce businesses optimize their pricing strategy through a machine-learning algorithm. The actor gathers data from various sources, analyze the market trends, and competitor pricing to recommend price alterations that maximize revenue generation for the online retailer.

List of Inputs:

• store_url (URL of the online store to analyze) Description: The URL of the e-commerce store for which the pricing optimization will be performed. Purpose: To determine the target store that needs price optimization. Placeholder Example: "https://www.example-store.com"

• competitor_urls (Array of competitor store URLs) Description: A list of URLs of competitor stores for comparison of products and pricing. Purpose: To analyze competitor pricing strategies. Placeholder Example:

  [ "https://www.competitor1.com", "https://www.competitor2.com" ]

• data_sources (List of data sources for market trend analysis) Description: A list of external data sources for gathering market trends and pricing data. Purpose: To analyze market trends and provide optimal price recommendations. Placeholder Example:

  [ "https://www.source1.com/endpoint", "https://www.source2.com/endpoint" ]

• api_key (API access key) Description: The API access key for authenticating with the data sources. Purpose: To enable secure access to the specified data sources. Placeholder Example: "1a2b3c4d5e_example"

List of Outputs:

• optimized_prices (Optimized prices for the product catalog) Description: A list of recommended prices for each product available in the online store catalog. Values: An array containing product IDs and their corresponding optimized prices.

• price_comparison_report (A detailed price comparison report) Description: A comprehensive report outlining the differences between existing prices, competitor prices, and the recommended optimized prices. Values: An array containing product IDs, current prices, competitor prices, and the recommended optimized prices.

List of Services to Be Used:

• Apify (Web scraping and automation platform) Name: Apify URL: https://apify.com/ Why: To automate data extraction from e-commerce stores and competitors' websites.

• RapidAPI (API Marketplace) Name: RapidAPI URL: https://rapidapi.com/ Why: To obtain API access to various data sources for market trend analysis.

Earning Potential:

By implementing the SmartPriceOptimizer, businesses can make more money through:

• Improved pricing strategy that increases store revenue and profits.

• Better pricing competitiveness in the market, attracting more customers.

• Dynamic price adjustments based on current market trends and competitor actions.

Needed Capital and Why:

The needed capital will be used for:

• Research and development of the machine-learning algorithm.

• Integration with various data sources and APIs.

• Maintenance of the actor infrastructure and support for the customers.

Estimated capital required: $200,000

Entry Barriers:

The entry barriers include:

• The expertise required in building and maintaining a machine-learning model for price optimization.

• The difficulty in obtaining access to reliable and accurate data sources for market trends and competitor pricing.

• Continuously updating and adapting the

actor to account for the rapidly changing e-commerce landscape.

Expected Outcome and Pricing Model:

Costing: Fixed monthly subscription fee for using the SmartPriceOptimizer.

Pricing tiers based on the size and needs of the e-commerce store (e.g., Basic: $49/month, Pro: $149/month, Enterprise: Custom pricing)

Earning: Commission on incremental revenue generated from the optimized pricing strategy.

Percentage based on the increased revenue for the e-commerce store (e.g., 5% of additional revenue generated)

ROI Calculation:

Assuming a 20% increase in revenue after implementing the SmartPriceOptimizer, and with an initial customer base of 50 e-commerce stores:

• Monthly subscriptions (Basic: 30 stores, Pro: 15 stores, Enterprise: 5 stores)

• Subscription revenue: (30 x $49) + (15 x $149) + (5 x Custom) = $3,735

• Earnings from increased revenue (average monthly revenue per store: $5,000)

• Avg. increased revenue per store: $5,000 x 20% = $1,000

• Total increased revenue: $1,000 x 50 = $50,000

• Commission (5% of increased revenue): $50,000 x 5% = $2,500

Total Monthly Earnings: $3,735 (Subscription) + $2,500 (Commission) = $6,235

ROI = (Monthly Earnings * 12 months) / Initial Investment = ($6,235 * 12) / $200,000 = 37.41% This would pay for the investment in approximately 32 months.