Machine learning and human judgement - We help combine best of both worlds. We incorporate the impact of local events, holidays, back-to-school, school closing, local carnival and games, social signals and macro-economic variables.
~ Demand Forecasting
~ Store Ordering
~ Purchase Order Recommendations
~ Shelf Space Allocation & Optimization
~ Continuous Monitoring And Improvement
Built-In AI for pricing, predicting & ordering that’s smart, fast and affordable. It uses data , AI/ML to power a proprietary, self-learning platform that sets a new standard in pricing intelligence & planning automation.
Trevy AI
Trevy Pricing
Better price realization to improve Sales & Margin.
~ Determine price elasticity by product, location and day; hence determine the extent of mark-up or markdown
~ Differential Pricing Strategies
~ Avoid a race to the bottom
~ A/B testing to demonstrate effectiveness of recommendations
~ Continuous monitoring and improvement
~ Determine price elasticity by product, location and day; hence determine the extent of mark-up or markdown
~ Differential Pricing Strategies
~ Avoid a race to the bottom
~ A/B testing to demonstrate effectiveness of recommendations
~ Continuous monitoring and improvement
Trevy Price Discovery & Monitoring
~ Identify Substitutable / Similar Products With a “Similarity” Score
~ Analyze /slide & dice historical competitor pricing
~ Get Real-time competition pricing insight
~ Competitor price trend alerts
~ Discover new competitors
Trevy AI
The Intelligence we provide
Price Optimization
- How should we price for profit, as opposed to price for sales?
- Are we pricing right? What’s the price elasticity (ability to pay) by product, by location and day?
- How can we make changes, at speed? How do we not leave money on the table?
- Can we conduct “price experiments”, and get a quick read on results?
- What customer attributes are significant that drive price acceptance?
- What are frequently occurring baskets and who are the “key basket holders”? How do we minimize discounting?
- How should we adapt to competitor pricing, while still avoiding a “race to the bottom?”
Demand Forecasting
- How do we automate the demand forecasting process, and make it more intelligent and self-learning?
- How we do reduce manual involvement, and yet get the benefit of human judgement?
- How do we incorporate factors that impact demand?(e.g. local events, macro-economic factors, holidays and so on)
- How can we determine latent demand? Answer the question – What COULD we sell?
- How do I ensure the delicate balance between over-stock and out of stock?
- How should I distribute stock across my stores and on-line?
- How should I anticipate demand, and stock up, so as to not lose demand?