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Audiences

Purpose of this document​

This document provides details at the business, technical, and implementation levels for specific features within the ES Loyalty feature set.

Audiences​

Audiences are reusable assets that can be referenced when defining member targeting, such as during offer setup. At its core, an audience is a logic statement that defines a specific set of members. Audiences can be created using either Audience Builder or Audience Recommender.

Audiences created with either tool can be saved, exported, analysed for member counts, and applied across various targeting scenarios, including banner targeting, offer targeting, and offer pool inclusion logic.

Audience Builder​

Create member audiences by building query logic statements. This feature enables marketers to define precise audience segments using customisable rules and conditions.

The Audience Builder enables users to create sophisticated segments by combining multiple member data attributes. It supports advanced logic, including greater than, less than, equals, in, AND/OR clauses, and clause grouping.

With access to a wide range of data points—such as profile attributes, EMD, and past purchase history—the builder is suitable for both simple segments (for example, gender = male) and complex audiences requiring precise logic.

Audience Recommender (Gen AI)​

Audience Recommender is a generative AI-powered tool, accessible through the Audience feature in the Console, that enables marketers to explore and discover new member audiences. It improves marketer productivity through text-to-query audience setup. The underlying AI technology uses large language models (LLMs) and natural language processing (NLP)— similar to AI capabilities becoming prevalent in the CDP, ESP, and digital advertising spaces— to allow the marketer to describe an audience in plain language, then generate a matching audience with a single click.

This feature enables marketers to explore and discover new member audiences while improving productivity through text-to-query audience setup. When combined with other ES Loyalty features (for example, Offers), it helps drive business results.

Like the Audience Builder, this feature can set up audiences based on a broad range of data, including profile attributes, EMD, past purchase history, point balance, partner linking, and offer engagement data.

While it supports both simple and highly complex audience creation, its key differentiator is its ability to translate natural language into intricate logic that would otherwise be difficult to configure manually in the builder. It can also refine vague or open-ended inputs into precise audience definitions (for example, identifying members likely to be environmentally conscious based on their purchase history).

The intent is to help marketers determine their audiences based on the following:

  • Products: Who is buying particular products and who is not.
  • Matching customer segments to categories: How specific customer segments can be matched to particular products—for example, ride-share drivers to fuel, health-focused members to nutritional products and vitamins, or new parents to baby supplies.
  • Spending patterns: How to increase spending for low or non-spenders, or for those who only buy sale or low-margin items; how to identify members who are no longer actively engaged in the loyalty program.
  • Engagement with other ES Loyalty features: Who is earning badges, completing frequency offers, or likely to join a loyalty household.

Using Audience Recommender, marketers can automate the generation of targeted audiences that engage with these trends and drive business results through related features such as Offers.

Audience Recommender is aware of a diverse set of member data that can be used to create the desired audience, including:

  • Standard profile information: City, province or state, country, gender, postal or ZIP code, and so on.
  • Past purchase information: Spending on particular products as defined by department, group, vendor, category, sub-category, or brand (either name or code). Note: the feature can answer product ID (SKU/GTIN) queries with some success; however, this is not an officially supported use case as it has not been tested or QA'd in this area.
  • Store and channel purchases: Members who have purchased at different stores and channels.
  • Extended member data (EMD): Additional information provided by and relevant to the client.
  • Partner linking information: Details about which partners the member is linked to.
  • Member point balance: The point balance on the member account.
  • Offer engagement: Member engagement with specific offers and types of offers, including accepting and completing offers, accepting offers at a particular time, completing a specified number of offers, accepting or completing any offer, completing offers with a particular Reporting Identifier (such as "Flyer"), or multiple conditions (for example, a member who did not accept an offer in 2023 but has done so in 2024).

Gen AI module integration​

This feature interacts with enterprise-grade, third-party generative AI products via the ES Loyalty Gen AI module. All data processed through these features is handled securely and is not used to train or improve AI models. The information provided to these services includes:

  • Console user input (for example, "members who are male").
  • ES Loyalty database schema overview (table structure only, no client data).
  • The client's EMD attribute labels (for example, "Favourite Color EMD", but no member data).
  • The client's product hierarchy (for example, brand and category structure).
  • Client partner names only.

At no point does the AI have access to the ES Loyalty database or any member data, such as profile or transaction details.

Audience export: account IDs, loyalty IDs, or external IDs​

This feature is available for audiences created using either the builder or the recommender.

The user can export the account ID, the loyalty ID, or—if the environment is configured for it—an external ID for each account in the audience as a CSV file. When the user clicks Generate File in the Audience Export section, they can select one of the two or three available identifiers (depending on configuration) in the Export Audience dialog.

After selecting an identifier type, the user clicks Save and the file is exported containing the selected ID.

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