Predicting visitorship at Sentosa (Part 1 of 3).

Why we do what we do.
27 June 2017

Sentosa is an island resort off the southern coast of Singapore, connected to the city by road, cable car, pedestrian boardwalk and monorail. As an iconic leisure destination, Sentosa had the pleasure of welcoming 19.5 million guests last year. With that many guests, having a strong operations team would come in handy.

As a general rule of thumb, the effort required to streamline operations is only ¼ that of external activities. What this implies is the need to prioritise operations as it is controllable and results in cost savings that can be quite substantial. In the world of service processes (like an island resort), it also results in a positive customer experience.

With any supply or service chain, the ability to predict demand within a given error bound is useful as it will drive decisions throughout. For the case of Sentosa, these predictions are in the form of visitorship, i.e., the number of people visiting the island at different entrances and attractions.

Why is visitorship important?

Visitorships are touch points and touch points present an opportunity to engage and interact with guests. This is an important part of the visitor journey and experience. Having the capability to predict visitorship adds on to this experience by being able to ascertain how crowds will form. With that, we can:

  1. mobilise customer service ambassadors to redirect guests to less crowded entrances or attractions (aiding with operational efficiency)

  2. inform guests of crowded areas so they can plan their itinerary better (contributing to the guest experience)

Interestingly, depending on the time of year, guest satisfaction is both proportional and inversely proportional to crowdedness, i.e., it has a time-variant property.

Are crowds forming?

We built QSenseTM to predict and simulate crowd movement. For Sentosa, it provides insights into when and where crowds will form across several entrances and attractions.

The figure below shows predicted hourly crowd levels at the following entrances:

and the following attractions:

Indicators for crowd levels are:

On to Part 2 of 3.

(Cosmiqo QSenseTM)
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