Descrizione del progetto
Una soluzione innovativa che contribuisce a far sì che il cliente sia premiato al momento dell’acquisto
La tecnologia per le offerte legate a una carta collega una particolare offerta alla carta di credito o di debito del singolo consumatore. Le prestazioni di questa tecnologia vengono tuttavia limitate dalla sua capacità di ottenere estratti cronologici delle transazioni della carta. Per superare queste limitazioni, la PMI francese PayLead ha sviluppato un nuovo approccio, definito offerta legata a un account (ALO®, Account-Linked Offer), che raccoglie tutti i dati delle transazioni e apre la strada a programmi di fedeltà intelligenti basati su precisi profili di spesa. Il progetto PayLead, finanziato dall’UE, sta conducendo uno studio di fattibilità per convalidare l’attuabilità commerciale e le strategie della soluzione ALO®, sia in termini finanziari che sui diritti di proprietà intellettuale.
Obiettivo
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
Campo scientifico
- 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
Programma(i)
Argomento(i)
Invito a presentare proposte
Vedi altri progetti per questo bandoBando secondario
H2020-SMEInst-2018-2020-1
Meccanismo di finanziamento
SME-1 - SME instrument phase 1Coordinatore
33000 BORDEAUX
Francia
L’organizzazione si è definita una PMI (piccola e media impresa) al momento della firma dell’accordo di sovvenzione.