Data Science Consulting:
Sales Forecasting and Dynamic Pricing
for your Omni-Channel Retail Business

Project Portfolio

Darwin Pricing is your Swiss-quality expert partner for Dynamic Pricing software development. Our consulting and SaaS solutions are trusted worldwide by over 700 clients and handle over $30 billion of annual revenue.

About Darwin Pricing LLC

  • Dynamic Pricing software and Data Science consulting since 2013
  • Founded in Aarau (Switzerland)
  • Exhibitor at the Web Summit in Lisbon (Portugal)
  • Investment by the Swiss Startup Factory in Zurich (Switzerland)
  • Geo-Pricing solution used by 700+ online retailers, with a focus on the US market
  • Development of the Dynamic Pricing solution of (€2.7bn yearly turnover)
  • Development of the Demand Forecasting solution of H&M (€22bn yearly turnover)

Goals of Dynamic Pricing

Automatic price adjustments during the season. Business goals:

  • Maximizing overall revenue and profit
  • Seasonal articles: Selling until the end of the season with the highest possible profit margin
  • Permanent articles: Avoiding clearance before the next stock receipt
  • Early detection of overstock and bottlenecks and proactive action
  • Reducing remaining stocks with effective sales, avoiding liquidation costs


Core of the Dynamic Pricing system: Sales Forecasting

  • How do prices affect purchasing behavior?
  • Weekly forecast for each article, depending on price list, assortment and seasonality
  • Extrapolation until the end of the season, considering stocks and seasonality
  • Determination of the optimal price list
  • Corrections as needed during the season


Quality and size of the data base are crucial for sales forecasting.
Better data ⇨ More precise sales forecast ⇨ Best results and stable prices

  • Relevant product details: category, brand, size, color, material, weight, quantity...
  • Current and historical sales data: Assortment, prices, stocks and sales on a daily basis
  • Current and historical competition prices, when available
  • Purchase prices, recommended retail prices
  • Date of the seasonal sales and delivery dates for inventory optimization
  • Liquidation costs for remaining stocks

Business Rules

Additional pricing restrictions:

  • Minimum price: e.g. purchase price plus VAT
  • Maximum price: e.g. 50% above RRP
  • Uniform prices: e.g. all article sizes for the same price
  • Price steps: e.g. price changes in $10 increments for items in the range ± $100
  • Update frequency: e.g. no more than one price change per week for each article
  • Price lock: e.g. no price changes at product launch or for certain brands

Technical implementation

  • Secure web application in the Amazon cloud (USA or EU)
  • 24/7 operational security with the cloud platform Red Hat OpenShift Online Pro
  • Daily synchronization of product, sales and warehouse databases
  • Sales forecast with artificial neural networks
  • Inventory optimization by extrapolation based on the seasonality curve
  • Daily selection of the optimal price list based on simulations
  • Prices as stable as possible, price changes only as often as needed
  • Automatic price changes through an API, possibly after manual release

Achieve the same business goals more efficiently and reliably

Dynamic Pricing:

  • Systematic, extensive
  • Controlled, data-based
  • Forward-looking, predictable
  • Considers many factors: Demand, seasonality, stocks, delivery dates, sales, gross margin...

Manual pricing:

  • Case-by-case, seldom
  • Gut feeling, personal experience
  • Reactive, hectic
  • Addresses goals one at a time: Profit margin, purchase frequency, customer acquisition, sales, stock clearance ...

Interested? We're looking forward to advise you!

Arrange an introductory call at a convenient date and time:

Schedule a Call