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PayLead

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

Total Cost €

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EC-Contrib. €

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Partnership

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 Outcomes and results

 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.

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

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

 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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