The Multilocal Challenge

How to insure a local visibility on the internet for each shop?

From national to multilocal

Each store is different and has its own local identity: their customers, with their favorite products, their catchment area, etc. National campaigns don’t take these parameters into account and show ads in a homogeneous way across all territories. Some stores will thus randomly be allocated more budgets than others, a pertinent message to the North of the territory will be diffused in the South, and so on.

So the stake is to know how to manage a local media planning: a campaign for each shop. This local media planning is very complex to be executed “manually” because it requires the creation and management of as many campaigns as there are stores in the network. We named this technical problem “multilocal challenge”.

Towards a more adapted and better targeted local digital advertising

Une campagne nationale homogène pour tout le territoire

A homogeneous national campaign for the whole territory

Une campagne spécifique pour chaque magasin

A unique campaign for each store

ARMIS solved this multilocal problem by developing an artificial intelligence called FLAI (Fast Learning AI)


FLAI is an artificial intelligence able to generate “multilocal” advertising, that means creating advertising messages and displaying them in an optimal & local way for each retailer’s store. FLAI can be controlled by a man-machine interface: the ARMIS SaaS Console.


FLAI is able to take into account and load the entire print circular and create a database from this input. This “circular feed” is cleaned, structured and enriched by FLAI.


FLAI can enrich the information in the product feed and then create millions of ads and thousands of campaigns on all the digital platforms (Google, Facebook, Xandr, DV360, Waze, etc.) thanks to deep learning techniques such as visual recognition and Natural Language Processing (NLP) for all devices (mobile, desktop, tablet). These advertising objects (messages, campaigns) are personalised for each catalog and each retailer’s store.


FLAI is able to make decisions using only data from the stores and the current campaigns in order to optimize the ad delivery on multiple dimensions: budget allocation between the different platforms, selection of the best offers, devices allocation, by effectively taking into account the native intelligence of the platforms as well as the multilocal constraint.


AI steps up to this challenge by automatically creating campaigns and advertising for each store by optimising investment on each digital platform in order to respect the budget given and that store by store. This results in an optimized and targeted advertising message at the local level, while also ensuring sufficient advertising pressure.

ARMIS has developed Machine Learning algorithms that bring complementary intelligence to digital platforms (Google, Facebook, Xandr, DV360, Waze, etc.) and calibrate these platforms for multilocal contraints.

A massive data volumetry

personalised ads created for each

store generated per operation

communication opportunities to chose

around each store per campaign