Description du projet
Une solution innovante pour que les clients soient récompensés lorsqu’ils effectuent des achats
Les offres liées à une carte (CLO pour «Card‑linked offers») sont une technologie qui permet de relier une offre spéciale à la carte de crédit ou de débit d’un consommateur. Les performances des CLO sont toutefois limitées par leur capacité à obtenir l’historique des transactions par carte. Face à ce problème, la PME française PayLead a développé une nouvelle approche appelée Account Linked Offers (ALO®), qui collecte toutes les données de transaction et ouvre la voie à des programmes de fidélité intelligents basés sur des modèles de dépenses précis. Le projet PayLead, financé par l’UE, réalise une étude de faisabilité pour valider la solution ALO® en termes de viabilité commerciale et de stratégie financière et de droits de propriété intellectuelle.
Objectif
Credit and debit card transaction histories are a valuable source of information about their owners’ purchase patterns. This information can be used to accurately address promotional offers, called Card Linked Offers (CLO). Today, CLO performance is limited due to the nature of accessible information from credit cards: it is often fragmented and and is not customer-centric as it does not take into account habits / historic of purchase to develop personalized recommandations. It is basic coupon with no intelligence.
PayLead is a French SME founded in 2016 developing a new approach - called Account-Linked Offer (ALO®) - that allows all transaction data to be collected, not only bank card payments. ALO is based on cross-checking transaction data with several external sources making it possible to improve prediction relevance and accuracy. This allows us to create smart loyalty programs based on precise patterns of spend propensity.
The bidding of offers is based on profiling and targeting resulting from customer payment flow analysis at both the bank level and external partners (merchants, third-party solutions).
Profiling is done on the basis of big data principles through machine learning algorithms and non-linear classifiers. The first commercial partnerships have been finalized to allow speed, critical mass of customers, users and partners in order to legitimize the offer in a very competitive market in terms of marketing and loyalty solution.
We have validated our model, developing a profiling and decision-making engine for merchant based on machine learning, and we prepared us for scaling. We have built our tech platform and we are now ready to take off through the consolidation of stakeholders.
We are currently deploying our solution with a major bank (BNP Paribas) and a major insurance group (Groupama). In 2023 we expect the have €23M of revenue and €13M of EBITDA.
This phase I project will support further the development of our go-to-market strategy in Europe
Champ scientifique
- humanitieshistory and archaeologyhistory
- natural sciencescomputer and information sciencesdata sciencebig data
- social scienceseconomics and businessbusiness and managementbusiness models
- natural sciencesbiological sciencesecologyecosystems
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Programme(s)
Régime de financement
SME-1 - SME instrument phase 1Coordinateur
33000 BORDEAUX
France
L’entreprise s’est définie comme une PME (petite et moyenne entreprise) au moment de la signature de la convention de subvention.