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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.

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

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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