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MARCH 17-20, 2024 MANDALAY BAY, LAS VEGAS

MARCH 17-20, 2024 MANDALAY BAY, LAS VEGAS

AI Pricing Models for e-commerce

Balance between new prices and the one working well

Fast-reinforcement learning that finds the optimal price and the customer's willingness to pay

Competitive edge

Faster exploration
and longer optimal play

Leverage data from orders, inventory, customer behavior and competition to train the AI models

Price Explorer

Out-of-the-box AI pricing models that pull in demand analytics to optimize prices immediately!

  • Multivariate price testing
  • Price elasticity measures
  • Value-based pricing automation
  • Switchback & Markdown policy
     

Stock Optimizer

The model is constantly training on inventory and demand data to improve pricing policies. You can employ ‘Stock Optimizer’ as a category manager assistant to:

  • Streamline inventory turnover
  • Lower the storage expenses
  • Consider marketing costs 
  • Manage current stock availability

Adaptive Pricer

Adapt to dynamic markets and apply your pricing strategy within different contexts, like:

  • Page visits and conversion rates
  • Holidays and days of the week
  • Competitor’s market moves
  • Marketing segments and buyer behavior 

Markdown Runner

The point of markdown is to determine the timing and magnitude of clearing the inventory while maximizing revenue.

  • Utilize plug-n-play price ladders 
  • N-periods optimal price reductions 
  • Suitable for fast-fashion and FMCG
  • Manage exponential value drops

Demand-based multi-pricer

Optimize all retail prices simultaneously while blending revenue and profit.

  • Compute the revenue vs. profit tradeoff
  • Set targets for revenue and profit on a category level
  • Get fresh prices based on demand changes
  • Include business rules like margin guards, competition, and rounding

Manage and optimize your business retail cycle

Adapt to changing markets with pricing automation policies and models.

 

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