ES Loyalty 4.5 release notes
These release notes cover features introduced or completed since ES Loyalty v4.4.1.
What's in this release
- Offer Validator (quality improvement)
- Tier Status Rollover (enhancement)
- Bulk Offer Import (new feature)
- Member Scoring and Intelligence (Beta) (new feature)
Quality improvements
Offer Validator
Overview
This backend architectural improvement introduces an additional validation layer for offer configurations, preventing promotion setup errors from progressing to problematic downstream states in the system.
Users will almost never see any indication of this feature. If a validation error does appear, notify your TSA. Some minor configuration by TSAs is required to allow certain promotion types in advance of use, on a per-client basis.
Why it's valuable
Ensures that every promotion launched is accurate, compliant, and aligned with business intent — while reducing costly errors and the need for manual review.
What it does
Introduces a flexible backend service that validates all offer configurations using centralized, JSON schema–based rules. The service automatically detects offer types and applies both standard and type-specific validations.
How you benefit
You can create promotions with greater confidence, streamline approval workflows, and simplify ongoing maintenance through a single, consistent source of validation logic across your loyalty ecosystem.
Details
The offer validator checks an offer for correct configuration when a Console user attempts to publish it. This prevents offers with validation issues from being saved to the database until configuration issues are resolved. If errors are found, an in-Console message lists all issues the user must correct before the offer can be published.
The offer validator has two layers:
-
Offer Schema validation: Checks that the incoming offer's structure (fields and types) matches the required schema — for example, verifying that a Start Date has been selected.
-
Offer Archetype validation: Ensures the offer follows additional business rules based on its specific type. For example, if a promotion is set to "Spend $100, get 1,000 points" but the usage control sets a maximum of 500 points earned, that conflict triggers an archetype validation error.
The offer validator does not validate offers that were published before this release, nor does it validate offers saved as Draft.
See also:
- Offer Validator — Confluence
- Offer Validator — Archetype Validation — Confluence
- [WIZ-9510] Offer Validator — Jira
Enhancements
Tier Status Rollover
Overview
This enhancement modifies the Tiers functionality to support a common loyalty program design pattern — allowing members to immediately progress to the next tier status and retain that status, once earned, through the following benefit period.
Why it's valuable
Maintaining member motivation and loyalty is easier when high-value customers can retain their hard-earned tier status into the next period. This pattern is common among leading travel and hospitality programs.
What it does
Enables a configurable setting that rolls over a member's highest achieved tier from the previous period, ensuring continuity and clarity across tier evaluations, member messaging, and analytics.
How you benefit
You can boost tier engagement and reduce member churn with a fair, transparent tiering experience — while gaining better insights into retention behavior and simplifying communications through enhanced APIs and reporting.
Details
Previously, a member's tier status lasted only until the end of the tier period, at which point it reset to the lowest tier. With this enhancement, clients can configure the program to retain member tier status from one period to the next based on contributions (spend).
For example, if a member achieves Silver tier status in one period, Silver carries over into the next period. If their contributions in the new period are high enough, they move to Gold — and Gold then carries over into the period after that. At any point, a member's tier status reflects the highest contribution across the current or most recent period.
Example walkthrough:
- Period 1 — A new member joins with no contributions and no tier status (Unranked). They accumulate enough spend to move through Bronze and into Silver.
- Period 2 — The member retains Silver from Period 1, then reaches sufficient contributions to move to Gold.
- Period 3 — The member retains Gold from Period 2 but only contributes $200 during Period 3.
- Period 4 — After spending $50 in Period 4, the member's status downgrades to Bronze based on their total contribution from Period 3 ($200).
The tier rollover option can be selected when setting up an offer in the Console. The appearance of this control depends on configuration.
See also:
- Tier Enhancements — Tier Status Rollover — PRD
- Tier Status Rollover — Confluence
- [WIZ-9551] Tier Status Rollover — Jira
New features
Bulk Offer Import
Overview
This feature allows clients to skip the manual steps of creating mass offers in the Console by automating the import of externally produced offers — such as flyers.
Why it's valuable
Enables marketers to save time and reduce manual workload by automating the ingestion of high-volume promotional offers, especially from flyer campaigns.
What it does
Allows clients to import a structured file of externally created offers into ES Loyalty for immediate behavior recognition, with flexible validation options to ensure smooth processing.
How you benefit
You can scale your personalized offer programs faster and with fewer resources, while expanding your ability to activate traditional flyer content in digital channels.
Details
This feature enables clients to import externally created offers (such as flyers) into ES Loyalty for behavior recognition. Instead of manually creating up to 150 offers daily, clients can upload offers in a supported format and integrate them efficiently.
Clients provide a JSON file containing offer data, which is ingested into ES Loyalty. The process primarily focuses on flyer offers but may also support other offer types based on scope and customization.
Validation modes:
An optional configuration defines the validation rules for the ingestion process, including mandatory fields, product validity, and store existence. Validation operates in one of two modes, controlled by a configuration setting:
| Mode | Behavior |
|---|---|
| Strict Validation | All validations must pass for every record. If any record fails, the entire import is rejected. |
| Flexible Validation | Allows partial ingestion — invalid entries are skipped while valid records are processed. |
For more information about offer validation, see Offer Validator.
See also:
Member Scoring and Intelligence (Beta)
This feature is in Beta for selected clients and prospects in this release. General Availability (GA) is targeted for the next release. This release includes enhancements to the Beta version, primarily around the functionality and selection of individual metrics.
Overview
Member Scoring and Intelligence (Beta) is available for client and prospect demonstrations, product feedback, and to generate interest in feature adoption.
Why it's valuable
Your marketing team can instantly segment loyalty members based on key metrics — such as purchase frequency, engagement, or value — through a guided AI interface. It's the fastest way to create smarter audiences and power more relevant campaigns.
What it does
MSI lets you interact with your member data conversationally. It guides you through creating and managing segmentation scores — including Recency, Frequency, Monetary Value, Offer Engagement, and Churn Probability — with an AI engine that walks you through scoring methods, segment options, and saving results.
How you benefit
Member Scoring and Intelligence puts advanced segmentation in the hands of every marketer — no SQL or data science required. You get quick access to meaningful member insights, reusable scoring models, and campaign-ready segments that improve targeting and drive results.
Details
MSI supports two categories of member scores:
- Descriptive scores: Built from user-defined parameters and transaction history using Snowflake database queries.
- Predictive scores: Leverage machine learning models trained on user-defined parameters — such as churn threshold — to forecast member behaviors.
Descriptive score types:
| Score | Description |
|---|---|
| Recency | How recently members engaged |
| Frequency | How often members interact |
| Monetary Value | Member spending patterns |
| Profitability | Revenue-to-cost analysis |
| Offer Engagement | Response to promotional efforts |
Predictive score types:
| Score | Description |
|---|---|
| Churn Prediction | Likelihood of membership lapse |
| Purchase Intent | Probability of future transactions |
| Redemption Intent | Likelihood of reward utilization |
The following screenshots show the end-to-end flow of generating and saving a Recency score using the MSI feature.
See also: