Experimental product
From price prediction to contextual business decisions
InnPricer explores how Machine Learning and AI can support hotel pricing without reducing the decision to an isolated number.
Context
Price changes as context changes.
A room rate depends on calendar, demand, property characteristics, and market behavior. A pricing product must not only predict a value, but explain which signals influenced the recommendation and when it deserves to be questioned.
- Combine multiple dimensions
- Separate prediction from decision
- Make context legible
Product question
How can pricing support a decision without turning the model into authority?
The experience is more useful when it offers an investigable recommendation. The user needs to compare scenarios, recognize uncertainty, and understand the cost of being wrong before applying a price in a real operation.
- Show assumptions
- Explore scenarios
- Signal confidence and limits
Architecture
Data, model, and decision need to form one cycle.
The prototype organizes the idea as a system that receives signals, produces an estimate, and returns context to the person. The next evolution should connect historical data, temporal evaluation, and a decision interface that records what happened afterward.
- Market-signal input
- Model-based estimate
- Record to compare prediction and outcome
Validation
A pricing model needs to prove usefulness outside the notebook.
In the initial simulation, calendar and demand signals reduced mean absolute error by 14% against a seasonal baseline, while scenario comparison explained 3 out of 4 reviewed recommendations. Operational use still requires temporal stability and financial-impact validation.
- 14% less error against the seasonal baseline
- 3 of 4 recommendations explained by scenario
- Measure impact, not only statistical error
Conceptual architecture
From market signals to a contextualized price recommendation.
The solution connects features, prediction, and scenarios to turn model output into an explainable business decision.
Signals
Calendar, demand, and property attributes.
Features
Variables organized to represent the scenario.
Model
Price estimate and its uncertainty.
Scenarios
Alternative comparisons before the decision.
Outcome
Learning about prediction and real impact.
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