The Democratization of Sophistication in Hotel Technology

By Tammy Farley Co-Founder and President, The Rainmaker Group | April 17, 2016

Accurate, timely data is crucial to making decisions that drive revenue. Yet while technological advances make it possible to mine more data than ever before, the prohibitive cost of such solutions have made them feasible only for the largest and most prosperous organizations, leaving some of the most critical information out of reach of the majority of hoteliers. Now, new technologies are disrupting established markets, offering affordable, more flexible analytical capabilities to operators for whom they were previously either out of reach or could only be accessed through their parent brands. This is an important trend in our industry – we call it “the democratization of sophistication” -- and it’s enabling revenue managers to revisit conventional revenue management ideas.

From Revenue Management to Business Intelligence

Over the past two decades, the hospitality industry has increasingly adopted the use of revenue management systems. However, the ability to predict trends, as revenue management processes do, is no longer enough. To prevail in today’s competitive market, hoteliers must understand what’s behind those trends. And that requirement has led to the rise of new, revenue-focused business intelligence platforms.

Business intelligence tools automate the complicated and time-intensive process of retrieving and analyzing hotel performance data from multiple sources, and deliver those insights to decision-makers in the form of intuitive dashboards and daily reports. Drill-down capabilities enable users to “slice and dice” data at whatever level they choose, enabling them to quickly and accurately pinpoint the issues affecting performance numbers and make timely, strategic decisions that drive revenue.

When a system gathers data at the most granular possible level (such as rate code), users are able to identify the root causes of the trends that they see in their revenue management forecasts. As the following examples show, these underlying causes – once uncovered – are frequently surprising:

  • Identifying Producing and Non-producing Accounts - Performed at the
    individual rate-code level, a complete analysis of historical performance
    can determine, for instance, that an account is producing high volumes of
    room nights or revenue but only on sold-out nights, potentially displacing
    more profitable business.
  • Evaluating and Modifying Promotions - Promotions can be an effective tool to
    stimulate bookings, but too often they cannibalize demand instead. If, for
    example, a hotel’s 14-day advance purchase product is being booked exactly
    14 days prior to arrival, it may be a sign that existing bookers are trading
    down to the lower rate. Moving the term to 21 days could solve the problem.
  • Analyzing Source Markets - This enables revenue managers to compare
    year-over-year pick-up by source market – a significant benefit to
    independent hotels that have to invest marketing dollars directly. If the
    data shows a drop in production in one particular market, the hotel might
    change its marketing strategy in that market; if the drop is caused by
    economic or other factors outside its control, it might redeploy those
    marketing dollars elsewhere.
  • Analyzing Channel Profitability. The proliferation and growing complexity of
    distribution channels makes this a tough area for hotels to manage. When
    users can aggregate up to the channel level, comparing marketing investment
    against production they can determine which channels are most profitable for
    their hotel.
  • More Granular Pacing Analysis - Pacing analysis, or how bookings are picking
    up for a particular future arrival date, can be performed at a far more
    granular level with business intelligence, providing hoteliers with insights
    that are actionable to a surgical level of precision.
  • Identifying Anomalies - Revenue managers can use business intelligence tools
    to pinpoint irregularities and their sources. For example, they can identify
    not only when a rate code’s ADR drops but drill down into the individual
    rate code to ascertain if the drop was the result of human error – an easy
    fix – or another cause.
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