The Rank Group is a FTSE-listed international gambling and entertainment company operating both physical venues and digital platforms across the UK and Europe. With more than 7,700 employees and 3.1 million active customers, its internal systems underpin a business generating nearly £800M in annual net gaming revenue.

Across Grosvenor Casinos, Mecca Bingo, and Rank Interactive, promotions play a central role in customer engagement, managed through shared tools used daily by marketing, compliance, and operations teams.

At the centre of this ecosystem is the Bonus Engine.

The Bonus Engine isn’t a single feature. It’s the system that defines, targets, and governs incentives across products.

As promotions grew more sophisticated, the legacy engine struggled to keep pace, creating the need for clearer validation and confidence at scale.

Understanding the Incentive Ecosystem.

To inform the redesign, we mapped how incentives operate across products, teams, and systems within a single platform.

At the centre sits the Bonus Engine, connecting teams with segmentation, payment systems, game providers, and analytics.

Deconstructing the Configuration Architecture.

A cross-functional workshop mapped the Bonus Engine’s fields, dependencies, and validation rules, revealing fragmented and often hidden system logic.

The visual revealed how six interconnected sections shaped promotions, requiring deep system knowledge to avoid errors and highlighting the need for clearer system structure.

Understanding Operational Workflows.

Mapping the promotion lifecycle with Support, Operations, and Compliance revealed the absence of a shared view for monitoring performance or diagnosing issues.

This reframed the challenge as a lifecycle visibility gap, highlighting the need for a centralised dashboard for oversight and decision-making.

Aligning on Design Direction.

Discovery insights were synthesised to align stakeholders on priorities, guiding principles, and success metrics for the redesign.

Cross-workshop synthesis revealed that complexity, hidden dependencies, and limited visibility were the primary drivers of errors and inefficiency.

We prioritised high-impact interventions, focusing on structured workflows, embedded validation, and improved monitoring.

Metrics such as ease-of-use, configuration time, support cases, and error rates ensured the redesign could be measured by real operational impact.

SOLUTION OVERVIEW

Guided by discovery insights, the Bonus Engine was redesigned to reduce cognitive load and support safer configuration through structured workflows, embedded validation, and improved visibility.

Structured Configuration Flow

The legacy experience relied on a monolithic form requiring full system knowledge. We replaced it with a step-based flow that grouped decisions into logical stages and reduced cognitive load.

After exploring multiple stepper patterns, we selected a horizontal model for clearer progress, context, and scalability.

Why This Matters.

Validation was embedded within each step, surfacing warnings, risks, and conflicts early to prevent invalid configurations.

This shifted the system from reactive correction to proactive risk mitigation.

Explicit Rules & Dependencies

System logic was previously hidden, forcing users to rely on experience. We surfaced dependencies directly within the workflow.

Before: Hidden Dependencies

After: Inline System Feedback

If user selects: Segmentation → First-time depositor | Device → Phone only | Deposit → Min £20

Then Rewards only shows: Eligible game types | Allowed bonus types | Correct wagering logic

Other irrelevant fields are hidden.

A structured summary layer allowed users to review campaign mechanics, eligibility logic, reward rules, and risk indicators before launch.

This replaced reliance on manual QA checks with clear system-backed confirmation, increasing user confidence and reducing downstream correction rates.

Discovery workshops revealed the absence of a shared source of truth for monitoring campaign performance and identifying issues.

Strengths

High data density

Sortable columns

Familiar enterprise pattern

Strong for bulk comparison

Best for: Power users | Data-heavy workflows | Spreadsheet-oriented tasks

Weaknesses

Low visual hierarchy

Harder to spot risk at a glance

Status easily buried in columns

Less contextual awareness

Weaknesses

Lower density

Less suitable for bulk edits

Exploring List vs Card Layouts

We explored a data table and a campaign card system. While the table offered higher data density, it reduced visibility of campaign risk.

The card layout was selected to prioritise status awareness, surfacing campaign metrics, lifecycle stage, and risks at a glance.

Dashboard components were directly derived from workshop insights:

The interface was designed to support rapid scanning rather than deep data inspection.

Planned Evolution: Dual View Mode

While cards prioritised visibility, some teams preferred high-density comparison views. A future Card/List toggle will support both workflows without fragmenting the experience.

VALIDATION, OUTCOMES & IMPACT

We measured the redesign’s impact by running the same internal survey before launch and after release.

A structured workflow and embedded validation improved clarity and ease of promotion setup.

The redesign reduced operational overhead, enabling faster campaign launches with fewer corrections.

Making rules explicit and embedding validation into the workflow improved trust and prevented invalid configurations before launch.