The Background

A hospitality management company wished to optimize its pricing and room rate optimization strategy. There are many important data points across multiple data sources that all flow into a best-practice calculation, such as historical performance, on-the-books occupancy, group blocks, competitor pricing, competitor occupancy, tourism forecast, event scheduling, website traffic, and third-party bookings.

Due to the volume and breadth of data, analysts were struggling to provide accurate, timely, and confident pricing recommendations.

The Challenges

Data disparity. Historical performance data was generated and housed on local servers; whereas, daily brand report data, competitor data, and traffic data all lived outside of local servers. All the data needed to be retrieved and standardized into a single system.

Excel data sources. Programmatic data sources (API) are always preferable because it can be integrated as part of an automated data pipeline. Multiple data sources were only available as excel files, and vendors were unwilling to offer a different format. A creative solution was needed to ingest these data sources with as little manual intervention as possible.

Event-driven and timely. Changes in tourism forecasts drive immediate and extreme changes in demand, such as large event announcements (sporting events, concerts, festivals), wedding and conference bookings, and potential changes in travel restrictions. Fluctuations in competitor pricing and occupancy also will impact rates. Rate optimization decisions should be event-driven and timely.

Drive action from insights. Business intelligence solutions are wonderful at providing the What; unfortunately, many solutions fail to provide the equally important Why and How. The solution needed to provide analysts with complete clarity: What happened? Why this drives a change in pricing? How to optimize?

The Solution

Bring the data together with Google Cloud

We spun up a cloud environment and went to work designing data pipelines to automatically ingest data from programmatic data sources. The data was housed in BigQuery, Google Cloud’s industry-leading data-warehousing tool.

For the excel-based data, we designed a solution which allows the vendor to email daily reports to a specified email address. Our process is waiting to receive the email, extract the data from the excel attachment, and load the data into BigQuery.

Getting the data to talk nicely together

All this data living together, but no one is talking… Or, more accurately, everyone is talking but no one is having a conversation. Data sources have different ways of doing things. One property might have ten different names across various sources. Even industry standard metrics (like occupancy and RevPAR) are calculated differently.

Solving this problem is known as data standardization and is the leading cause of the infamous “garbage-in, garbage-out”. At Etio, data integrity is the upmost importance. We simplify the process with our data mapping infrastructure and enable SMEs and end-users to collaborate, understand, and validate the data is coming together correctly.

Analyze the data and drive insights

Data is ready to go, and as new data becomes available, it is run through the pipeline and ready for action! There are many options and considerations when choosing an analytics tool. Long story short, there isn’t one tool that is a catch all. Etio’s philosophy is to analyze the use-cases, cost, experience, and choose the best tool for the job. The solution(s) needed to provide:
1. Business Intelligence. Ability to analyze the What, Why, and How
2. Automated Insights. Auto-detection of new factors causing substantial changes in pricing

For business intelligence, the company chose Tableau. Tableau’s advanced visualizations, filtering capability, and ability to quickly process large amounts of historical and forecasting data were important deciding factors.

The automated insights solution automatically processes new data as it comes in. If material findings are discovered, an alert is sent to the pricing team. Technically, this falls under the Artificial Intelligence umbrella, but it is better known as a data-driven rules engine. Business rules can be as advanced or basic as desired, for example:
– Changes in price and occupancy between a property and competitors
– Changes in competitors group block bookings
– Substantial changes in tourism forecasts

How did it turn out?

With its new data solution, the company now has excellent visibility into factors driving pricing changes. Valuable time previously spent wrangling together data sources and scrolling through excel workbooks is now used improving the pricing calculation and understanding competitor strategies.

Early detection of tourism-driving events allows the pricing team to take corrective action, more quickly than competitors, and capture incremental revenue. Pricing meetings are run more efficiently as all the data is accessible by everyone in the same place. By enabling self-service analytics with Tableau, analysts are discovering innovative ways to visualize data and create useful business intelligence dashboards.