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.
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 flow
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
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)
JCDecaux/Connecthings experiment beacons devices in several pieces of urban furniture (including bus shelters) and can provide the data for all the parisian territory
Social networks (e.g. Instagram, Facebook and Twitter) may enhance quantitative data with qualitative data
Suez Consulting - Director, Territories, Cities and Transport
PARIS CITY COUNCIL
Head of Tourism
Big-Data Product Manager
Senior Technology Manager
Big-Data Project Manager
Open Tour Director