Pricing AI for Retail (Markdown) Product Description
Core Modules
Ensure large markdown retail campaigns achieve business goals, applying unique AI discounting and pricing for every SKU in every location. Each of the features below is at a SKU x location level. The following core modules make up the current out-of-the-box capabilities.
Markdown
Features: Global products page to view portfolio-level weekly KPIs and detailed per product status. Campaign creation flow to build end-of-season campaign plans and select products. Campaign optimization and export journey, including choosing optimization strategy and reviewing forecasted impact.
Price Sensitivity
Features: Calculate the impact of a price change on a given product, in terms of future demand (price elasticity). For example, if a product is calculated to be elastic, a price decrease is highly correlated with a forecasted increase in sales of the product. This is done at a product x location level.
This is combined with the demand forecast and specific business guardrails (e.g., maximum acceptable markdown) to provide the optimized campaign product prices.
Demand Forecast
Features: Gets a prediction on the future volume of products to be discounted. This prediction is based on the historical sales data of each product line.
This is combined with the price sensitivity and specific business guardrails (e.g., maximum acceptable markdown) to provide the optimized campaign product prices.
Limitations
- Limit of 20,000 product x location combinations load tested
- Limit of 100 concurrent users
- Supports existing products only (not the initial price of new products)
- Does not include cross-elasticity out of the box; this is a customization
Additional Modules
Optional extras are the additional capabilities that can be added to the Subscription for an additional cost. The Customer may elect to receive the following Optional Extras. In the event the Customer elects to receive any Optional Extras, the parties shall discuss and negotiate relevant terms in good faith.
Promotions
Optimized in-season retail campaigns that focus on producing discounted prices that maximize profit at a product x location level
Features: Configure and optimize an in-season retail campaign, with pareto curve allowing the user to find the sweet spot between average selling price and revenue (instead of sell-through vs gross margin in the Markdown web app).
Limitations
Additional limitations of this module:
- See core modules
Snowflake Acceleration
Host both the application data at rest and the machine learning compute in a customer's Snowflake data cloud.
Features: Move the majority of compute optionally to the customer's Snowflake infrastructure, including the elasticity workflow, demand forecast, and DBT pipeline. This provides increased data and compute sovereignty, and increases the performance of the machine learning models.
Limitations
Additional limitations of this module:
- The Peak product and optimiser are still hosted in the Peak infrastructure currently