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Tracking and understanding tourist flows

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Backgrounder


How to track, analyse and understand tourist flows in a city, with a view to adapting services to tourists’ changing habits?

Paris has long been one of the world’s most popular tourist destinations, and tourists account for a significant portion of the people in the city at any point in time. Tourists also contribute a substantial circa €40 billion a year to its finances, and this sector is enjoying buoyant growth on the back of strong demand from emerging countries. International tourism is expected to continue to grow at a sturdy 5% or so a year.

 

In light of the stakes, Paris City Council has mapped out a 59-initiative master plan to develop tourism. In practice, those initiatives add up to improving tourist reception facilities and providing a range of novel products and services tailored to the foreigners visiting the city.

 

Enhancing a city’s appeal and upgrading its tourist attractions also involves understanding tourist flows. For example, it is important to know what countries visitors come from, which neighbourhoods they prefer, how they travel around the city, what routes they follow, when they travel, their profiles, how many tourists return to the city, and what they visit the second time.

 

Today, habits, wants and needs are evolving faster than the tools we have to analyse them. Most existing studies are based on surveys providing macroeconomic figures such as tourist footfall or, conversely, qualitative studies shedding no light on mass effects.

 

Data-centric analytics should help to fine-tune deals to tourists’ requirements, or even tailor deals individually, before and after trips. They should also help to assess policies aimed at improving tourist reception and Paris’ appeal more rapidly.

Corporates

Startups

  • The problem

  • Use case and data sets

  • Business opportunities for the stakeholders

  • Results

How to track, analyse and understand tourist flows in a city, with a view to adapting services to tourists’ changing habits?

Startups are asked to develop a tool in Paris to:

 

  • Initially study tourist flows in one or more arrondissements (preferably not the must-see landmarks on standard tours)
  • Then measure the impact of city policy on tourism in target neighbourhoods
  • Subsequently recommend ways of tailoring tourist amenities (reception, signage, transport) based on the study of existing flows

 

The data sets provided by the partners include:

 

  • SFR will provide aggregated and anonymised data from mobile devices (technical data from its radio antennas) for a representative sample (approx. 30% of the population), covering uninterrupted periods (24/7) in France (locals and foreigners)
  • RATP Dev will supply Open Tour ticketing data, Wi-Fi data and Open Tour bus geolocation data
  • MasterCard will share anonymised data from its transaction records, i.e. transaction amounts, user profiles, payment methods (contactless, smartphone or touchpad), geolocations (merchant codes), times and average purchases
  • Cisco will pool a data analytics platform to gather, combine and process data with a view to producing historical analysis (near-real-time or predictive)
  • Social networks (e.g. Instagram, Facebook and Twitter) may enhance quantitative data with qualitative data
  • Suez: help cities fine-tune their initiatives to improve tourist transport and services
  • SFR: extract value from mobile data, market solutions to tour operators and communities, and jointly develop solutions combining data from various sources
  • RATP Dev: use the solution to reviews available tourist bus services and size future services, then possibly use the solution on its conventional bus lines
  • Cisco: develop predictive analysis solutions jointly with startups
  • Paris City Council: enhance Paris’ appeal and tourist amenities following a decline in tourist numbers
  • The startup: develop a new service, leveraging the large amount of data supplied by the partners listed above

Solution:

 

To solve this challenge, AiD-Add Intelligence to data, the selected startup, has developed an application, “Trend Shaker”, that mutualizes a variety of datasets including roaming data from SFR, cross border transactions data from Mastercard, as well as data from foreign users Uber. This enables the user of the application to understand, analyze and monitor how and where tourists travel in Paris, where and on what they spend money – all that at specific times of the day. It is therefore a powerful tool both for tourism department of the city of Paris, Paris’ tourist office as well as department stores, hotels, tourist sites and any service providers to the benefit of tourists in Paris.

Impact
Implementation of an interface of data analysis and visualization with

 

  • 3 different data sets mutualized and integrated in the solution
  • 20 main nationalities of tourists analyzed