Using an advanced algorithm to enhance a client-facing platform
Capitalizing on the scalable power of cutting-edge technology, Cervello helped a property management company enhance its proprietary customer-facing platform with a real-time dynamic pricing algorithm.
One of the fastest-growing vacation rental property management companies wanted to capitalize on its competitors’ data to produce a flexible pricing model for its own platform. To accelerate the initiative, the company acquired a third-party solution that provided market data acquisition capabilities. However, the solution was built on legacy technology and included many unnecessary features that used platform resources and hindered performance, which limited scalability, exasperated support complexities, and inhibited innovation. Ultimately, the company wanted to develop a high-performing, scalable algorithm backed by a modern cloud solution to calibrate prices for its rental properties on an ongoing basis.
We began by reverse-engineering the legacy application to learn more about the underlying logic, minimize the time and dependency on business teams to derive requirements, and provide options to enhance the legacy app. We also developed analytics to monitor the app’s performance and real-time health, giving the engineering team the ability to see bottlenecks and areas that would ultimately require more scrutiny for the new platform. We worked with the company to design and develop a dynamic rate engine that used market occupancy, immediacy, and demand data to continuously fine-tune daily rental rates. To match the extreme fluctuations during the pandemic, we monitored model outputs and added capabilities for the on-the-ground team to enhance the market data with more real-time intelligence so the algorithm was ahead of the curve from the base market data in recognizing COVID-driven shifts that impact rental pricing, such as event cancellations and the summer holiday effect. Our solution combined cloud technologies such as Snowflake, Talend, and Salesforce and used Python to address the needs of the algorithm.
With the dynamic pricing algorithm applied to the company’s entire US portfolio, the revenue management team was able to focus on customer management, data analysis, and fine-tuning the rate mechanisms to meet market changes. Listings that used the dynamic pricing algorithm had an 18 percent higher revenue per listing, 7 percent higher average daily rates, and 5 percent more occupancy than the listings that used manual pricing during the peak of COVID-19 pandemic. Having the ability to react quickly in an ever-changing environment and influence so many properties in a short period will keep the company on the leading edge of the vacation property industry.
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