Dialog Insight's standard e-commerce structure provides a pre-built relational table model designed to host and organize sales data within a project. By choosing one of the 2 options (retail or event) you automatically get a set of tables (products, transactions, carts, events, etc.) already linked to each other and to the contact list. This structure simplifies e-commerce data integration, while also allowing you to segment audiences, personalize content, automate campaigns, and leverage purchase behavior metrics without manually configuring complex relationships.
Standard Structure
Dialog Insight offers 2 options for standard structures containing dedicated tables:
- Retail: tables for products, carts, transactions, etc.
- Event: tables for events, participants, registrations, tickets, etc.
Only one structure can be used per project. The tables within these structures are linked together in a consistent manner, allowing you to segment audiences, personalize content, and automate campaigns without having to manually create complex relationships.
→ Learn more about the Event structure
→ Learn more about the Retail structure
To generate one of these standard structures, 2 approaches are possible:
Main Entities (Tables)
Every project in Dialog Insight contains a contact list (contact table). In an e-commerce context, this is the central entity to which all e-commerce data is linked. Each customer is individually identified to connect transactions, behaviors, and communications.
The e-commerce structure includes several types of entities that are added to the contact table, each playing a specific role:
- Transaction: It represents a purchase or commercial transaction made by a contact. It is the starting point for the analysis of purchasing behavior.
- Product/Event: It stores data on the items or services sold. Transactions are linked to one or more products.
- Cart (only for Retail): This entity contains the items added by a customer, whether or not they complete the transaction. This allows for anticipating and following up on purchases.
Table Relationships
The strength of the model lies in its relational relationships:
- Each transaction is linked to a single contact.
- Each product is linked to one or several transactions.
- A cart (only for Retail) can contain several products. It is linked to one contact.
This organization allows for easy navigation between data. You can, for example, identify which contacts have purchased a specific product or which transactions originate from a given segment and target that segment in a campaign with product recommendations.
→ See the Event structure schema
→ See the Retail structure schema
E-Commerce Indicators
When a Dialog Insight project has an e-commerce structure (retail or events) with transaction data and product or event categories, several purchase preference indicators are automatically calculated from a contact's transactions. These indicators provide key metrics of purchasing behavior useful for segmentation or communication personalization.
Thus, you get an access to temporal and monetary indicators, such as the date of last transaction, the number of transactions, and the total amount spent over a period, as well as the average amount per transaction over those same periods. The model also provides a measure of the number of months since the last transaction and indicators based on product or event categories purchased, such as the most recently purchased category and the most recently purchased category. This information feeds the customer profile to better guide marketing strategy.
→ See the list of purchasing preferences indicators
Data Synchronisation
To synchronize data, you have several options available:
- Option 1: Use an e-commerce integration from Dialog Insight
- Option 2: Configure an import or an export
- Option 3 (for developers): Exploit web services or outbound webhooks
Data Exploitation
The e-commerce standard allows e-commerce data to be used in various ways:
- Segment based on purchases, cart value or purchasing frequency.
- Automate abandoned cart reminders or post-purchase campaigns.
- Personalize messages according to the purchase history.
- Analyze the performance of each campaign and its impact on sales.
The goal is to transform transactional data into concrete marketing actions that can be used at all levels.