Machine Learning Based Solution for a Leading Hotel Owner
The client approached us to build a machine learning-based software that dynamically determines optimal room rental prices across multiple hotel booking platforms. The solution leverages competitor listings and market trends using a K-Nearest Neighbors (KNN) model to forecast daily pricing. We also delivered interactive dashboards for real-time decision-making and improved revenue accuracy.
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Industry
Hospitality -
Country
India
Technologies
Python
Pandas
Jupyter
KNN Algorithm
Scikit Learn
Years In Business
Projects Delivered
Happy Clients
Countries Served
Business Goals
Our client discussed several business objectives at the beginning of the project execution. So, here are detailed goals and vision of the client for their project.
- The client wanted to define optimal rent for hotel rooms after evaluating multiple hotel booking platforms.
- They were also expecting the digital platform to figure out competitor pricing and ongoing market trends using categories like number of rooms, beds, bedrooms, washrooms, etc.
- Besides, their expectation was to set prices competitive to attract customers and stay ahead of competitors.
- Lastly, they wanted to automate the process of defining prices by deploying data-driven pricing without manual work.
Business Challenges
Price Sensitivity/Finding the Right Price
The client wasn’t sure about the price point, yet they were to retain profitability. They wanted pricing to be suitable, competitive, and affordable for customers.
Risk of Under-or Over-Pricing
If price is too low, chances of revenue will be lower. And if its too high, there might not be too many customers. Ultimately, they might lose revenue and customers.
Market Dynamism & Variability
Hotel industry includes several variables, such as number of beds, bedrooms, bathrooms, guest capacity, which all affects pricing.
Decision Making without Sufficient Insight
The client was lacking strong visibility in making pricing decisions, which was a major challenge for them to sustain in the market.
Technical Solution
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Machine Learning Model Using K-Nearest Neighbors
We built ML model that foresee rental pricing on similar listing by using their prices averages as reference.
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Determining Supporting Variables
Severa variables like bedrooms, beds, guest capacity are included as feature of this model.
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Graphical Visualization & Dashboards
Our solution includes graphical representation based on different criteria like accommodation, bedrooms, beds, bathrooms, etc.
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Automated Pricing Recommendation Workflows
Process flow: find similar listings → average their prices → set listing’s price to that average. It starts automating the entire manual process.
Project Glimpse
Key Features
Comparable Listings with Pricing
Graphical Dashboard and Visualizations
Automated Price Prediction
Modifiable Feature Parameters
Transparent rental price-setting
ML-based Pricing
Result
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01.
Increased attractiveness to renters
Since pricing is competitive, the property becomes more appealing, which is likely increasing occupancy.
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02.
Better Profitability and Efficiency
Our client was struggling with setting optimal pricing per room, which is why they were incurring losses.
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03.
Better Competitor Awareness and Trend Visibility
Out client was having insider information of what their competitors are charging, what it includes, which has helped building strategies.
Let’s get in touch!
India
SPEC House, Parth Complex, Near Swastik Cross Roads, Navarangpura, Ahmedabad 380009, INDIA.
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+91-79-26404031, 32-33-34 -
[email protected]
