Guidance for working with data as a hotel marketer   

For hotels, the promise of a well-developed data discipline is exciting: a clear-cut decision-making engine that takes the emotion out of marketing. Each decision is made for a reason, a reason that’s backed up by actual user behavior and not just instinct or gut-feeling.

Fulfilling this data science promise also means more accurate tracking, which results in better return on advertising investment (ROI), and ad spending (ROAS). With improved accuracy, hotel marketers can be more confident in their work. These stronger returns can then be used to marshal more resources for marketing, and to better communicate results to teams and financially oriented stakeholders.

This increased accuracy extends to how hotels understand guests. A strong data science discipline unites insights from disparate systems to give hotels a more complete picture of guests, as well as their preferences and behaviors. With that information pulled into rich guest profiles, hotels can deliver more personalized experiences that are also more relevant to guests — and yes, this personalization and relevancy drives better results both on and offline.

How to get started with data

Of course, a data science discipline isn’t an easy thing. And it certainly isn’t quick. There’s a lot of effort that must go into doing it right — because, all too often, the project is rushed or not fully fleshed out. When that happens, the data isn’t accurate and can’t really be trusted to tell the truth. Without an accurate picture, hoteliers can pull the data that confirms their own assumptions rather than data that reflects what’s actually happening in the business.

To launch a data science initiative at your hotel, there are three things you need to do: leverage multiple data sources from across your tech stack, lean on available expertise from around the industry, and collaborate cross-functionally to gain buy-in across the hotel.

1.Unite your data sources. The frustrating truth of many hospitality organizations is that there’s not a single source of truth for data. Without a data ecosystem or data warehouse, a data science initiative can never truly get off the ground. The data will always be incomplete and there will always be questions around its integrity, cleanliness and accuracy.

To bring your data sources together, it’s essential to invest in a home for all data. A centralized data intelligence warehouse gives marketers the power to deliver the promise of data. It should pull in data from across the acronyms (CRS, CMS, POS, GSS, PMS), as well as social, web, call center, loyalty, apps and other useful data, with a continuous refresh of input from these channels. From there, marketers can slice and dice, building micro-segments of guests that can be targeted at pinpoint accuracy with relevant messaging.

2. Use industry expertise. Don’t expect to get it all done in-house. It’s often the case that external hospitality experts can accelerate progress and improve the velocity of a data science initiative. It’s always helpful to look outside hospitality for inspiration and best practices.

3. Be curious and resourceful. Ask colleagues at other hotels, browse blogs and download guides (like this one!) that can guide you on this important journey. You may be surprised at how much knowledge and expertise lives in key stakeholders in other departments. Lean on those resources and ask them to share key industry benchmarks so you can set baselines for your own work.

4. Collaborate across functions. Every organization experiences its fair share of data silos. This is partially due to the natural separation of data across disparate systems. For example, information about a guest in the PMS doesn’t always correlate nicely to engagement metrics from the latest marketing campaign. To work towards a single source of (data) truth, it starts with visibility. Keep your ears open, ask questions, and start tracking which department has access to which types of data. Then you can prioritize collaboration to gather the data you need to accomplish your objectives.

This is why it’s so important to gather buy-in with others. This alignment will not only make the entire process easier; the initiative will be more successful at launch if it also includes input and insights into the problems faced by other teams across the organization.

5. The proof is in the reporting. The reasons why data science matters to hotels are numerous, but all boiled down to one thing: improving business outcomes. To show just how helpful this all can be, review these eight real-life examples. Each report slices into the data to reveal unique insights that answer specific hotel business outcomes.

Want to learn more? Download our Hotelier’s Guide to Data Science. 

Author
Casey Munck

Casey Munck is the Director of Marketing, Americas. In her role at Cendyn, she manages content marketing, lead generation, communications, social media and thought leadership.

Casey Munck is the Director of Marketing, Americas. In her role at Cendyn, she manages content marketing, lead generation, communications, social media and thought leadership.

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