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If you are not a math genius or aced your quantitative analysis courses at MIT, the current focus on big data delivers either a migraine headache or a wish for the days when spreadsheet analysis meant

If you are not a math genius or aced your quantitative analysis courses at MIT, the current focus on big data delivers either a migraine headache or a wish for the days when spreadsheet analysis meant a promotion.

At a recent Eye for Travel Analytics conference, experts from Hilton, Wyndham,, United Airlines, Travelocity International, Novatel, Highgate Hotels, and many other gurus threw huge amounts of quantitative data at hundreds of corporate executives who are charged with designing, developing, and analyzing their marketing campaigns and business development. Everyone is struggling to stay up-to-date with the current measurement imperative – not only to gather the data, but to also make some sense out of it.

Data Shows
There is no problem in collecting the data, as dozens of companies are set up to do this for free or nominal costs; the problem is determining what data should be collected, its validity, and what should be done with it. The even bigger challenge is getting senior management to take the information seriously and use it to develop, modify, and/or change strategies.

At this moment, the marketing research segment of the industry appears to be like a dog with a bone – the dog really likes the bone and is happy to share the prize with his/her owner – however, doggie bones are not useful or attractive to the owner and are only beautiful to the dog. In other words, the marketing research gurus are happy as clams with all this information – but they cannot even give it away.

A recent study by The Relevancy Group finds that less than half of marketers have dashboard access to the critical metrics needed to drive optimization programs. This 2012 report determined that one-third of marketers blast email with no knowledge of deliverability inbox placement rate and no understanding of the importance of this information.

To make matters even worse, Gilmartin (April 1,2013), in his review of Wolfe’s 1998 research found that we falsely assume that consumers decide rationally and queries that force rational answers are flawed. Current research does not consider subtle changes in values and world views and ignores changes in mind mapping that occurs with age.

Brain science research has challenged consumer behavior. It was assumed that people accurately state their values, needs, and motivations; however, research finds that this not true, and in reality, consumers are not qualitatively conscious. The realization that people do not understand their motivations challenges the basic tenets of research. According to Massy, Frank, and Lodahl’s research, “Multivariate statistics that describe personality traits can account for no more than 7 percent of purchasing behavior.”

Another false assumption is the belief that consumers make buying decisions based on self-interest and use reason in product selection. Brain research finds that reason plays a smaller role in personal decisions since consumers do not choose rationally; any research that forces rational answers will be incorrect, according to Kevin J. Clancy and Robert S. Shulman (Marketing Revolution).

Smart Travel Analytics Offers Insight
Although the basic tenets of data are being reevaluated, research offers – at a minimum – the opportunity to reflect on where we have been, where we are, and the opportunity to consider where to go next. Understanding that data is not perfect should not mean that it eliminated as a marketing tool – what it suggests is that it be used as part of an arsenal that attempts to provide a portal to the future (which is unknown).

Lessons Shared at Eye for Travel
1. PWC found the strongest Rev PAR (occupancy x ADR = Rev PAR) gains in the luxury, upper upscale, and upscale hotel categories. There are signs of recovery in the lodging industry – especially in the upper tier of chains, and there is also room for continued growth.

2. Smith Travel Research categorizes hotels by brands that include:

– Luxury chains: Fairmont, Four Seasons, ICH, Mandarin Oriental, Ritz Carlton, St. Regis, and Waldorf
–73.1 percent occupancy; ADR US$274.11. Result: Rev PAR US$208.28

– Upper Upscale properties: Hilton, Marriott, Hyatt, Sheraton, Westin, and Wyndham
–70.7 percent occupancy; US$154 ADR. Result: Rev PAR US$109

– Upscale hotels: Courtyard by Marriott, Crowne Plaza, Four Points, Hilton Garden Inn, and Radisson
–70.8 percent occupancy; US$116.51 ADR. Result: Rev PAR US$82.46

– Upper midscale hotels: Comfort Inn, Holliday Inn, Fairfield Inn, and Hampton Inn & Suites
–62.9 percent occupancy; US$97.21 ADR. Result: Rev PAR US$61.14

– Midscale: Best Western, Holiday Inn Express, La Quinta Inn & Suites, plus Wingate
–54.7 percent occupancy; US$74.30 ADR. Result: Rev PAR US$40.67

– Economy: Days Inn, Motel 6, Red Roof Inn, and Super 8
–54.3 percent occupancy; US$52.34 ADR. Result: Rev PAR US$28.41

-Independent properties: Fontainebleau, Hotel Gansevoort, Watergate, and Hotel Bel-Air
–61.3 percent occupancy; US$105.81 ADR. Result: Rev PAR US$64.88

3. Peter Lim. Wyndham Hotel Group.

Wyndham has over 7,000 hotels in 66 countries that represent 15 brands. One of Lims’ metrics is used to prioritize international development efforts worldwide. Collecting data from the Americas, Europe, Middle East/Africa, and Asia Pacific his focus is on forecasting revenue EBITDA (Earnings before Income Taxes Depreciation and Amortization) in order to prioritize global development efforts.

4. Martin Stolfa, Hilton Worldwide. Analytics maturity model.

– Stage 1. Senior management has limited interest in analytics.

– Stage 2. The “line-of-business” management drives analytics.

– Stage 3. Senior executives are committed to analytics and align resources to support data.

– Stage 4. The entire organizations is analytically capable and being developed as a corporate priority.

– Stage 5. The entire organization benefits from enterprise –wide analytics and continuous improvement becomes the mantra.

5. Thomas H. Davenport and D. J. Patil. The Data Intelligence Organization.

– Data is everywhere and silo based – but should be used strategically in organizations

– Data considered a repository item

– Data collection includes:

— Customer (i.e., behavior, preferences, aspirations, purchase and life cycle)

— Frontline (i.e., tailored services, situational readiness, and offer response)

— Technologists (i.e., storage, product catalogue, loyalty systems, pricing/inventory systems)

– Data collected and analyzed leads to price positioning, disservice avoidance and predictive maintenance

GIGO. Garbage in. Garbage out.
There are many challenges facing the acquisition and functionality of data, and separating the wheat from the chaff is only one part of the process. Selecting the gold nuggets from reliable sources can improve results; therefore, the requirement to secure quality data can be daunting.

Warden, Curator or Manager
As the amount of data collected and analyzed increases and new software emerges, finding the talent to handle the process is another formidable task. Big data requires specific management considerations:

1. Quality. Is the information accurate, complete, and reliable?

2. Control. Is there a privacy policy in place for sensitive data; who is authorized to view and utilize the information and what uses are acceptable?

3. Ownership. Who possesses the data and who makes the final decision on its disposition?

At this moment it appears that the industry is in the hunting/gathering stage of analytics. Now would it be appropriate to put best practices in place to assure accuracy and wisdom in its usage.