Dynamic Pricing Innovation: The Evolution of Promenade at Bukit Vista

img Furqon Bukitvista | August 21, 2023
Greetings! I am Furqon, a dedicated and results-driven PR specialist with a passion for crafting compelling narratives and building strong relationships. With a keen understanding of the technological implementation in the hospitality industry, I am excited to share our latest update about our property pricing recommender.

In our relentless pursuit of innovation, we’ve embarked on a groundbreaking journey that fuses cutting-edge technology with hospitality expertise. Introducing Promenade, a remarkable tool that transcends conventional revenue management. At Bukit Vista, we’re not just redefining the hospitality landscape – we’re revolutionizing it, one data-driven decision at a time.

Promenade: Where Innovation Meets Intelligence

Promenade is a system to collect data in the form of available blocks from rental properties in Bukit Vista, it then suggest a price change and provide the revenue management team insights to approve or reject the suggestion and update the price to OTAs (Ex. Airbnb)

Promenade goes beyond mere suggestion – it’s an orchestration of data-driven intelligence. By calculating a multitude of factors, from seasonality to booking count, runway to current rates, it crafts the Suggested Price Event Decision (SPED).
SPED is a proposed new price setting and reflected by percentage.


Example: The current price (prev rate avg) for Asri Village is at IDR 610,000, with +3.7 SPED, Promenade suggested that the price should be increased by 3.7% → IDR 632,570.

But here’s the twist – this suggestion often dances to its own rhythm, distinct from the Pricing Event Decision crafted by our adept Business Intelligence team.

Continuing from the previous example: Our Business Intelligence team decided to lower the rate by 15% – reflected as -15 in Price Event Decision (PED) row → resulting decrease to IDR 520,000.

Promenade Impact

As a result, Promenade helps our revenue management team to price each date of the calendar by getting and collecting data like booking window distribution, pricing distribution, runway, etc. Afterwards, the revenue management team price the dates in the form of change in percentage which we call PED (Pricing Event Decisions). Which we keep on doing until we reach enough data to train an machine learning model to predicts it for us.

However, this sparks a big question, how to detect which prediction was right?

Felix’s Trailblazing Vision: Promenade Model Explainer

Cue Felix, the luminary of our Data Engineer team. Sensing the harmonious potential between technology and human insight, Felix embarked on a visionary project: the Promenade Model Explainer visualization. This marvel of innovation peels back the layers of decision-making, showcasing the intricate factors that coalesce to shape the Pricing Event Decision.
Meet Felix, our agile Data Engineer who build the model

Pain points of previous version of promenade

Before, Promenade lacks vision as it was outputting random and wrong PED. We would like to know what cause this, as Machine learning model learns from data, we would like to see how each instance is being predicted and why the model predicts a certain PED. The model becomes something like a black box without any explanation on why.

On August 15, a new epoch was ushered in – the launch of the Promenade Model Explainer. With this unveiling, our Business Intelligence team have newfound insights into the factors at play on the outputs of PED. The realization dawned: decisions could be based not on assumptions, but on the foundation of intricate data.

From the picture, we can see the contributing factors for set PED (-7.25%). One thing that was significantly noticed by Business Intelligence team in this specific case that they over-weighted the current price factor (represented as prev_rate_avg) too much. It dragged the potential revenue numbers that we could achieve.

The inner workings of the explainer


We use LIME (Local Interpretable Model-agnostic Explanations) which uses local prediction to explain the model. It generates perturbations of data and inputs those data to the model and sees the output. As we increase and decrease the input data of each feature, we will be able to see what has high and low impact on the predictions.

With this, we are able to detect some features like previous rate average (average price for a given available blocks) doesn’t make any sense because it doesn’t align with what the revenue management analysis to take a certain decision. With that in mind, we’re planning to modify and  engineer the feature to make Promenade model better at predictions.

Charting New Horizons: The Call for Data Visionaries

As we stand at this juncture, the echoes of innovation reverberate across Bukit Vista. But this is not just a tale – it’s a call to action. The Promenade journey has just begun, and we’re inviting the brightest minds in data science to join us. As Felix’s vision becomes a beacon, we’re seeking those who dare to reshape industries through the fusion of technology and insight.

Excited? Be part of a revolution that’s rewriting the playbook of data-driven hospitality. Apply to Bukit Vista and discover the future of revenue management, and chart your own path with us.

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