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PayLead

Smart Loyalty Program: Using Data Science to Inspire the Next Purchase

Total Cost €

0

EC-Contrib. €

0

Partnership

0

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 PayLead project word cloud

Explore the words cloud of the PayLead project. It provides you a very rough idea of what is the project "PayLead" about.

prepared    programs    linear    propensity    model    checking    french    commercial    merchant    stakeholders    habits    accurately    speed    payment    personalized    europe    party    sme    2016    smart    platform    accessible    flow    cross    patterns    critical    23m    cards    classifiers    sources    bank    bnp    coupon    principles    reg    data    decision    performance    big    learning    transaction    13m    2023    fragmented    consolidation    solution    recommandations    relevance    histories    accuracy    engine    centric    limited    paylead    spend    mass    ebitda    market    bidding    finalized    nature    offers    purchase    ready    linked    loyalty    revenue    machine    first    external    precise    paribas    card    prediction    algorithms    ing    alo    insurance    create    collected    competitive    clo    off    valuable    source    credit    historic    customers    profiling    marketing    promotional    groupama    owners    merchants    tech    intelligence    deploying    payments    basis    basic    validated    legitimize    strategy    founded    group    solutions    customer    scaling    debit   

Project "PayLead" data sheet

The following table provides information about the project.

Coordinator
PAYLEAD 

Organization address
address: 24 COURS DU MARÉCHAL FOCH
city: BORDEAUX
postcode: 33000
website: n.a.

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country France [FR]
 Project website http://www.paylead.fr
 Total cost 71˙429 €
 EC max contribution 50˙000 € (70%)
 Programme 1. H2020-EU.3. (PRIORITY 'Societal challenges)
2. H2020-EU.2.3. (INDUSTRIAL LEADERSHIP - Innovation In SMEs)
3. H2020-EU.2.1. (INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies)
 Code Call H2020-SMEInst-2018-2020-1
 Funding Scheme SME-1
 Starting year 2019
 Duration (year-month-day) from 2019-06-01   to  2019-09-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    PAYLEAD FR (BORDEAUX) coordinator 50˙000.00

Map

Leaflet | Map data © OpenStreetMap contributors, CC-BY-SA, Imagery © Mapbox

 Project objective

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

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The information about "PAYLEAD" are provided by the European Opendata Portal: CORDIS opendata.

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