How Do You Eat an Elephant? One Bite at a Time!
By Anand Medepalli Vice President of Industry Strategy, JDA Software | December 01, 2013
Big data is all the buzz these days, especially in the B2C marketplace, and it is playing a major role in the hospitality industry, as hospitality companies explore different approaches to conquer this data and gain customer insights. According to IBM, roughly 90 percent of all the data in the world has been generated over the last two years – Web searches and postings, online purchases, social media chatter to name a few are the culprits. Hospitality companies are teaming with Internet companies to gather, organize and analyze data to make smarter business decisions on customer engagement, distribution strategies, labor planning, pricing, merchandising, and operations. But ask any hotelier and it is very likely they will tell you that they don't yet quite know how to mine this plethora of data to gain intelligent insights in a sustainable manner to plan, forecast and optimally engage with their customers.
The main factors hindering the ability of a hotel to evolve into an analytic-orientated organization are a dated operating model made up of disparate, aging systems that are not synchronized, siloed business units that inhibit information sharing, ad hoc manual processes that deal with the here and now, and a workforce that relies more on experience than information. These internal challenges should make it clear that a big bang approach to make this transformation to analytics will not work. Like with any change, hoteliers will do well to start with their current strengths and capabilities and adopt a phased "crawl, walk, run, zen" approach to achieve data analytics nirvana. What could these phases look like for a forward-thinking hospitality company?
Crawl - This is where most hospitality companies find themselves today thanks to their investments in revenue management. Revenue management offers some formal processes and analytical capabilities that can form the basis for this transformation. At the heart of revenue management is the ability to crunch millions of historical records to produce occupancy forecasts at a granular level of detail (e.g.; daily forecast for the next 180 days at a property, rate segment, and length of stay level of detail). This ability is what allows some hoteliers to claim that they have been in the big data field for decades. Although this claim has merit, because these forecasts are produced with just one end in mind, namely the ability to identify the yieldable rate to offer a customer, they are an end in themselves; they serve no higher purpose and are therefore static in nature.
To move past this stage, organizations must begin using intelligent demand forecasts across their enterprise in an automated manner – even if the automation is through Excel or intranet – for more than just optimizing revenue. For instance, these forecasts, aggregated to the right level of consumption, can be shared with property managers in a timely manner so they can make better decisions on pricing and operations. Marketing can be alerted to identify low demand periods in the forecast to help them plan and align their promotions. The revenue management team should be charged with evangelizing these forecasts and the current processes to lay the foundation for analytics across other departments in order to move to the next stage.
Walk - Hospitality companies wishing to move into this stage must do two things: One, as mentioned above, there should be awareness of analytics-based decision making within the organization, and revenue management data and information should be made available to other departments. Processes could still be manual, but at least sharing information and insights is critical. Further, revenue management team becomes an honest broker of information and the main support function as other departments and stakeholders begin embracing data analytics.
A second key requirement for this stage is for companies to embrace price optimization, which admittedly a few hoteliers have either adopted or are in the process of. Today's world is dominated by tech-savvy consumers who are smart, connected, and have access to significant information before making their choices about which hotel to stay in. Price optimization technologies offer a first step for organizations to begin adapting to the new consumer demands by synthesizing historical data with competitor/market data and customer price sensitivity. In addition to producing occupancy forecasts, hotels can now also forecast likely customer response to price at a granular level of detail and determine how best to adjust price to meet company goals of revenue, profitability and market share.
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