133% NGR Uplift

Turning Raw Player Data into Revenue: A Segmentation Success Story

Executive Summary

An iGaming operator approached us with their data which was just large Excel files of raw player data and a desire to unlock quick, high-impact wins. Our goal was to transform their existing data into actionable strategies. By cleaning their data, employing behavioral segmentation, sub-clustering to refine targeting, and suggested promotions for each group, we helped the operator:

1. Identify high-value segments (e.g., VIPs, Bonus Seekers) and nuanced sub-clusters.

2. Craft tailored marketing approaches for each segment.

3. Validate the ROI of a new deposit-based bonus promotion via NGR (Net Gaming Revenue) measurement.

The operator gained faster marketing wins and clear next steps for ongoing optimisation—all from data they already had in Excel.

The Challenge

The operator was sitting on player data (spreadsheets of deposits, withdrawals, sessions, bonus usage, churn flags, etc.) but had no unified strategy to:

• Segment their players effectively.

• Pinpoint which groups would respond best to deposit bonuses.

• Measure the real impact (and ROI) of promotional campaigns.

They specifically asked, “How can we quickly boost revenue and retention using the data we already have?”

Our Approach - From Raw Data to Targeted Actions

First Steps

Data Consolidation -> Quality Checks -> Feature Preparation for Modelling all done in Python.

Clustering & Sub-Clustering

We ran our model to produce 3 main clusters:

1. Short‐Session Bonus Takers

2. Long‐Session Minimal‐Bonus

3. High Rollers

Then, to achieve more granular quick wins, we sub-clustered each main group into two, revealing 6 distinct player archetypes:

Long-Session High-Bonus (0,0)
Short-Session Moderate-Bonus (0,1)
Low-Withdrawal Minimal-Bonus (1,0)
High-Withdrawal Minimal-Bonus (1,1)
VIP Minimal-Bonus Players (2,0)
VIP Whales + Bonus Enthusiasts (2,1)

Why it Matters

Each sub-cluster had different deposit/withdrawal patterns, bonus usage, and session behaviors. This allowed the operator to quickly see where they could inject new promotions for maximum impact.

Segment Labeling & Strategies

We profiled each sub-cluster, assigning descriptive labels and targeted tactics. For example:

Short-Session Moderate-Bonus:
- Tends to log in briefly, uses bonuses moderately.
- Quick Win: Time-based incentives to lengthen sessions or encourage larger deposits.

VIP Whales + Bonus Enthusiasts:
- High depositors who also love promotions.
- Quick Win: Tailored high-limit deposit matches, invite-only tournaments.

High-Impact A/B Test on a Specific Sub-Cluster

Objective

The operator wanted immediate validation of a new deposit bonus. We focused on one sub-cluster—“Short-Session Moderate-Bonus”—to test a deposit-based promotion and gauge impact in Net Gaming Revenue.

Method

A deposit $100 get $20 bonus was launched to a test group while the control group received no promotion.

We gauged changes in the two cohorts’ NGR and found a +133% uplift in NGR for the test group versus the control group with a promotion ROI.

Tailored Recommendations

Assign players to each sub-clusters in CRM tools.
Personalised Campaigns based on player sub-groups & behaviours.
Ongoing Data Strategy – Re-run clustering on a monthly basis to adjust for behavioural shifts and incorporate more advanced metrics for further enhancement.

Impact & Conclusion

Within weeks, the operator:

Identified 6 key segments for immediate targeting.
Launched a test deposit bonus to one sub-group of players, validating a clear NGR uplift (+133%!)
Aligned marketing spend with specific behaviours—no more blanket promotions.

ROI soared for segments that responded well to deposit incentives, and the operator saved money by not offering expensive bonuses to segments unlikely to show positive net revenue.