Customer profiles, cookies and artificial intelligence are all more or less technologically advanced solutions that we can use to simplify and improve the online customer pathway. However, they are still under used on most websites, hence the litany of the same offerings that are rarely contextualised.
SNIPS, the French start-up that is currently working on implementing voice activated personal assistants, is a specialist in artificial intelligence, and has worked on many“predictive” projects. With LaPoste, to anticipate and foresee when post offices would be busy, with SNCF and the Tranquilien application for how busy trains and individual carriages would be, or even with San Francisco to predict road traffic accidents. By combining Big Data, AI and the participation of a community of users, Tranquilien allows users to choose a carriage on a train where they can find a seat, depending on the events in a diary or current affairs and the weather by analysing traffic over the preceding months.This is a service that is certainly interesting, but it has not yet been generalised and offered to the general public.
Without getting to this level of sophistication and using the high level of skills and technologies that allowed SNIPS to raise several million euros, any website can now assist its customers to guide them to a choice that corresponds to their current situation.
At Agitateurs de Destinations Numériques, we aim to achieve this objective in online applications developed for destinations and tourism professionals on websites and mobile apps using very simple principles and technologies.
In a customer's shoes
The promise is to deliver a limited number of tourist offerings that are appropriate to the position, the geographic location, the mode of transport and the time that the user intends to spend travelling (from 15 minutes on foot to a few hours in the car) using various existing API to greatly filter the available offer.
As we do not consume the same thing if it is raining or if its 35°C or 10°C, a grid with the appropriateness of each offer is linked to the API for the weather to consider this criterion. Although the terrace of a restaurant may be nice when the weather is good, a fire in a cosy corner would be better in the winter.
Depending on whether you are travelling alone or as a couple, with children or a group of friends, for fun or for business, again there are offerings that would be more or less well suited.
By qualifying the offer, by contextualisingthe request, by taking into account externalfactors, we can narrow down the range ofoptions rather than presenting the user witha never-ending directory of offerings to trawlthrough, often on the screen of a smartphone.
If the user is won-over there is a great chance that they will use the tool regularly. Then it is important not to disappoint them by always offering the same thing! Using a simple cookie, or even better by creating a connected customer profile, we can avoid showing the same offers they have already seen, already consumed or put in their favourites. That way we can offer regular travellers or those on long journeys the chance to enjoy local nuggets and not just the “Eiffel Tower” for their destination.
Using emerging technologies, which are sometimes expensive, or using existing ones and a little common sense, you can easily put yourself in your customer’s shoes, foresee their needs, predict what they will be looking for and maximise their satisfaction, and their consumption.
LUDOVIC DUBLANCHET - Cofounder, Agitateurs de Destinations Numériques
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