Projektbeschreibung
Eine innovative Lösung, mit der die Vergabe von Treueprämien für die Kundschaft zuverlässiger wird
Bei den sogenannten kartengebundenen Angeboten (Card-Linked Offers, CLO) handelt es sich um eine Technologie, die ein Sonderangebot an die Kredit- oder Debitkarte einer Verbraucherin oder eines Verbrauchers koppelt. Das Potenzial der CLO-Technologie ist jedoch eingeschränkt, da der Abruf des Transaktionsverlaufs von Karten damit nur bedingt möglich ist. Um dieses Problem zu beseitigen, hat das französische KMU PayLead eine neue Methode entwickelt: Account-Linked Offer (ALO®). Diese kann alle Transaktionsdaten erfassen und ebnet damit den Weg zu intelligenten Treueprogrammen, die auf präzisen Ausgabenmustern basieren. Das EU-finanzierte Projekt PayLead führt nun eine Machbarkeitsstudie durch, um die ALO®-Lösung im Hinblick auf ihre kommerzielle Tragfähigkeit und ihre Finanz- und Schutzrechtsstrategie zu prüfen.
Ziel
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
Wissenschaftliches Gebiet
- 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
Programm/Programme
Thema/Themen
Aufforderung zur Vorschlagseinreichung
Andere Projekte für diesen Aufruf anzeigenUnterauftrag
H2020-SMEInst-2018-2020-1
Finanzierungsplan
SME-1 - SME instrument phase 1Koordinator
33000 BORDEAUX
Frankreich
Die Organisation definierte sich zum Zeitpunkt der Unterzeichnung der Finanzhilfevereinbarung selbst als KMU (Kleine und mittlere Unternehmen).