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