Analytics

Custom data sent to HiPay can be used in our Analytics Platform. The tables below describe the fields to set up and the reportings in which custom data may be available.

      • Transactional reporting refers to Payment page and Payment performance analyses.
      • Marketing reporting refers to acquisition, RFM and churn rate analyses.
      • Specific reporting refers to dedicated reportings developed for instance on shipping, usage of discount codes…
      • Machine learning refers to our “Fraud Advisor” application. These additional fields sent to our machine learning algorithms strengthen the prediction when analyzing if a transaction is fraudulent or not.

Custom data fields  

      • “Label” is the key name sent in the custom data.
      • “Other labels accepted” defines additional key names that can be used for a given label.

Store and delivery information  

Label

Other labels accepted

Comment

Transactional reporting

Marketing reporting

Specific reporting

Machine learning

delivery_type

Shipping Method, Livraison

Click and Collect, Home delivery, Pick-up & Go…

✅

 

✅

 

carrier

shipping

E.g., DHL, Chronopost, La Poste…

✅

 

 

✅

 

 

pick_up_go

commande_point_relais

E.g., Name of Pick-up & Go location

✅

 

 

✅

 

 

pick_up_go_zip_code

None

E.g., 75012

✅

 

 

✅

shipping_option

envoi

Additional options for delivery

✅

 

 

✅

store

enseigne, CodeMagasinVendeur

E.g., Champs-Élysées store – Paris

✅

 

✅

 

delivery_cost

None

4.99

 

 

✅

 

Discount information  

Label

Other labels accepted

Comment

Transactional reporting

Marketing reporting

Specific reporting

Machine learning

discount_code

discounts, Promo Code

E.g., Delivery10, New50…

 

✅

 

 

Risk and marketing information  

Label

Other labels accepted

Comment

Transactional reporting

Marketing reporting

Specific reporting

Machine learning

is_first_order

first_order, first_time_buyer, customer_first_order

Format: 1/0 (1 for Yes, 0 for No)

 

✅

 

✅

customer_registration_date

client_date_inscr

Format: YYYYMMDD

 

✅

 

✅

is_suspicious

suspicious_order, suspicious

Format: 1/0 (1 for Yes, 0 for No)

 

 

 

✅

has_high_risk_product

None

Format: 1/0 (1 for Yes, 0 for No)

 

 

 

✅

has_sponsor

None

Format: 1/0 (1 for Yes, 0 for No)

 

 

 

✅

acquisition_period

campagne

Format: YYYYMM

 

 

 

✅

acquisition_channel

canal

E.g., Web, Store, Campaign

 

 

✅

 

affiliate

None

Name of affiliate

 

✅✅

 

customer_id

numCli

E.g., 123456789

 

✅✅

 

campaign

None

E.g., Facebook_Ad654AZ

 

 

✅

 

campaign_type

None

E.g., Facebook campaign

 

✅✅

 

Purchase and product information  

Label

Other labels accepted

Comment

Transactional reporting

Marketing reporting

Specific reporting

Machine learning

product_family

None

E.g., Clothes, T-shirts…

 

 

✅

 

product_quantities

quantity, quantities, product_quantity

Quantity of products in basket/order

 

 

✅✅

product_collection

club

E.g., Fall 2016, Book series

✅

 

✅

 

Marketplace and invoice information  

Label

Other labels accepted

Comment

Transactional reporting

Marketing reporting

Specific reporting

Machine learning

invoice_reminder

relance

E.g., Yes, 2nd, No…

✅

 

 

 

merchant_name

None

E.g., DOE John (ABC NETWORK S.L.)

 

 

✅