Big data = big opportunities for hotel design

A funny meme on the internet has a little boy asking Mark Zuckerberg whether it’s true that his father says Facebook spys on people. Zuckerberg replies, “He’s not your Dad.”

While the Cambridge Analytica scandal may have caused some PR damage, big data is here to stay. It’s literally unstoppable.

Big data has been used by advertising companies, insurance companies and supermarkets for a long time. It allows them to tailor their products to their customers. However, big data is still in its infancy in design.

We at Vale predict a major shift in hospitality design over the next ten year. This shift will see predictive behavioural analysis gradually replacing the industry’s traditional reliance upon intuition and guesswork. It is a shift that could end wasteful design once in a while.

Personalizing hospitality with big data

Big data is a large amount of data that can be analysed by computers in order to uncover patterns and trends about our behavior and interactions with one another. It is closely related to Artificial Intelligence (AI), which allows machines and programs to improve their learning through constant updating of their data.

Designers often believe that big data is irrelevant to hospitality design. We disagree. We believe big data has enormous potential to help us personalise our guest experiences in the bars, restaurants, and hotels we design at Vale.

Big data has been criticized for its shocking misuse of personal information by companies like Cambridge Analytica, and even Facebook. This is a temporary setback for society while it tries to understand some of the ethical boundaries.

We can’t see many ethical issues from a design standpoint. We are not trying to influence guests’ political or thinking. We are only trying to understand their needs so that we can predict their movement and behaviour on the site. This will allow us to design our building around them.

Predicting guest behaviour

Over the past ten year, personalizing guest experiences has been a major trend in hospitality. This is a frightening concept for many business owners. How can you personalize your services while maintaining your economies of scale? It’s also a problem for designers, as how can you design a building to cater to the unique tastes and needs of so many people?

Fortunately, despite how bizarre and unpredictable we may think ourselves to be, we are also creatures of habit. Our behaviour is governed by patterns, regardless of whether we like it. These patterns can be discovered with big data.

You can predict almost any type of behavior with the right information and the right tools. This includes their spending habits, their family structure, their living conditions back home, and how influential they are on Social Media.

This allows designers to predict how guests will use a building. For example, where they’ll choose to sit, what they’ll spend, and what kind of photos they’ll post to Instagram.

It allows you, as a business owner, to increase your profit margins by allocating your design budget to areas in your building where your future guests will spend the most time.

Guest profiling and big data

Personalization is a key concept for many designers and operators. They often use the millennial generation to identify their customers. However, those elusive millennials are fond of everything, from the cheap bamboo beach hut in Bali to the Hemingway Bar at the Paris Ritz.

Catering to such a large market is a waste of business. It is simply not practical to try to cater to such a large market.

We have previously written about the design approach that combines User Centric Design (UCD), and guest profiling. We are excited to see the work being done by other startups as well as our own guest profiling in the hospitality industry. These data provide the foundation for behavioural analysis that allows you to predict the use of your building more accurately.

Companies like Helix Personas and Defin’d use data sets created through websites, tracking apps, surveys, local statistic providers or a combination thereof. By combining their own data with other datasets like hotel bookings, surveys, and tracking apps, they can create very specific user profiles.

Picodash and Neighbourlytics are two other startups that go further. They combine their data with public information on social media accounts or with government data sets to gain a better understanding about public behavior in a specific local economy.

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