Data Tables in the Predictions Platform

CrossEngage's Customer Prediction Platform uses 3 types of Data:

• Customer Data

• Transaction Data

• Activity Data (Optional)

Customer Data

The Customer Table contains details of your Customers. This table has two mandatory fields: customer_id (string) and customer_since (date). You can also add additional data present in your Customer records, which can help improve accuracy of your Models.

The customer_id field is the primary key of this table. Each data record must have a Unique value for this field.

Field Name	Data Type	Example	Description
*customer_id	String	e131498	This is the key field, used for Identification of Customers. All values in this field must be Unique.
*customer_since	Date	2010-12-21	Date of first contact with the Customer. This field is vital for future predictions, and every customer should have a value for this field.
customer_gender	String	female Ms. LLC	Gender or Title of the Customer; Used as Categories for Pattern Recognition.
customer_age	Date	1997-06-22	Date of birth of Customer / Date of foundation of Customer company. Missing values are acceptable.
zipcode	String	50676	ZipCode of the Customer.
customer_country	String	France DE	Country where the Customer is based; Used as Categories for Pattern Recognition.
email_ending	String	gmail.com live.com	Customer's email address provider. If email is mapped to this field, the part before @ is automatically removed; Used as Categories for Pattern Recognition
phone	String	0176 0153	The starting digits of the Customer's phone number; Used as Categories for Pattern Recognition

Transaction Data

The Transaction Table contains details of Transactions made by Customers, such as Purchases and Returns. The Transaction table has 7 Mandatory fields, as this data is required for understanding the long-term Value of every Customer for better predictions.

The primary key for this table is the combination of 4 fields: customer_id, invoice_id, transaction_timestamp and item_number. In other words, for every transaction made by a customer at a given time, each record should contain a Unique item.

Field Name	Data Type	Example	Description
*customer_id	string	e131498	The Customer ID; Unique identifier used for merging all tables.
*transaction_ timestamp	date	2013-01-17	Date and (optionally) Time of the Transaction
*invoice_id	string	i_1021	Unique ID of every Order; Used to track different items in the same Order, or to match Orders with Returns
*item_number	string	3	Item number of each item in an Order. Must not be repeated within a single Order/Invoice
*price	decimal	29.99 179	Unit Price per item. Can be Net / Gross / Discounted Price etc.
*order_type	string	sale return neutral	The Order Type ( Sale / Return / Neutral ) of each Order. By default, Sale is Positive, Return is Negative while Neutral is ignored.
*quantity	decimal	3.5 -1	Can be number of items ordered, or weight/volume. This is multiplied by price to get total value of Ordered Item.
gross_margin	decimal	17.04 29	Gross profit margin per unit. Can be used to select a Margin-based view instead of a price-based view.
order_channel	string	internet telephone mail e-mail fax in_person	Information about how the order was placed. The 6 values on the left are accepted by default. For passing more values, please Reach out to Customer Support.
productgroup_id	string	clothing furniture	Category to which product belongs to. Ideally, this should not have more than 20 Unique entries.
productgroup_id2	string	shirts shoes	A finer product group can be passed in this field to create finer NBO models in expert mode.
productgroup_id3	string	t-shirts sneakers	In this field an even finer product group can be passed to create finer NBO models in expert mode.
product_id	string	p063	This field is only used if referenced in expert mode. May then be used to compute product specific models.
return_reason	string	NULL wrong size	The reason for return or cancellation.
voucher	string	8WBA2TV	The presence or type of a voucher can be stored here; Used as Categories for Pattern Recognition.
size	string	XL 12L	This field can be used for Prediction Models; It also serves as a feature to analyze frequency distribution of different sizes.
product_supplier	string	IKEA S123	This field can be used for Prediction Models; It also serves as a feature to analyze frequency distribution of different Suppliers.

Activity Data

To improve model and forecasting quality, you can upload additional tables that contain specific activities and interactions with your customers. These tables must each contain at least a unique customer ID, a timestamp, and an activity type. Additionally, an activity_id can be passed that links the activity to a specific action. However, this data is not required for an initial well working model and is therefore optional.

You can upload one or more of these tables:

• Online activities (customer in login area on your website / click in email)

• Inbound activities (customer calls call center, letters and emails from customer)

• Outbound activities (print mailings, catalogs, calls from a call center)

• Payment activities (customer pays an invoice, receives a dunning level, is referred to collections)

In order to make use of these activities in the modelling process, it is important that they are stored in the so-called event format (just like transactions). This means that each row in an activity table describes exactly one 'event' at a time with a customer.

Field Name	Data Type	Example	Description
*customer_id	string	e131498	The Customer ID; Unique identifier used for merging all tables.
*activity_timestamp	date	2015-01-13	Date and (optionally) Time of the Transaction
*activity_type	string	CartAdd CartCancel	Used to generate patterns for the most common types.
activity_subtype	string	Trousers Black 4RTR3022DS	Can be used to generate additional patterns.