---
title: "High-Value Players in Days, Not Months | Fusemachines"
description: "Graph-based behavioral scoring made identifying high-potential players 97% faster, cutting the time from months to days and enabling earlier outreach."
url: https://fusemachines.com/success-stories/finding-high-value-players-in-days-not-months/
last_updated: 2026-10-05
---

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# Finding high-value players in days, not months

![Dollar sign over a laptop keyboard](https://fusemachines.com/assets/images/2026/casestudy/high-value-payers.avif)

## 97% Faster Identification

Reduced identification time from months to days, enabling earlier engagement with high-potential players.

## Earlier Engagement Opportunities

Surfaced valuable players previously overlooked by existing targeting processes.

## More Focused Outreach

Turned broad candidate lists into prioritized groups aligned with the CRM team’s outreach capacity.

Global sports betting & gaming group

INDUSTRY **Sports/Interactive gaming**

USE CASE **Entity resolution and knowledge graph for planning**

AI SOLUTION **AI-based forecasting**

TOOLS USED

READ TIME: 3 MINS

## Overview

### Accelerating high-potential player identification with graph-based behavioral scoring.

A global gaming operator’s existing identification process relied on lagging behavioral indicators, delaying recognition of high-potential players and limiting opportunities for early engagement. Broad candidate lists also made it difficult for the CRM team to prioritize outreach.

Fusemachines developed a graph-based scoring model that analyzes player activity and engagement patterns to identify high-potential players within days. The solution enabled 97% faster identification, aligned candidate prioritization with the CRM team’s outreach capacity, and established a reusable data pipeline to support future machine learning capabilities.

## The challenge

1

**Delayed Player Identification:** Existing processes took months to recognize high-potential players, limiting opportunities to engage them during the critical early stages of activity

2

**Missed Retention Opportunities:** Players showing strong early engagement were not consistently surfaced for tailored CRM outreach and retention programs.

3

**Lagging Behavioral Insights:** Traditional targeting relied on historical indicators that did not capture emerging patterns in player activity quickly enough.

4

**Operational Constraints:** Broad candidate lists made it difficult for the CRM team to prioritize outreach within its available capacity.

## Fuse solution

1

**Graph-Based Behavioral Scoring:** Combined graph analytics with signals from player activity and engagement to identify high-potential players earlier in their journey.

2

**Targeted Player Prioritization:** Calibrated selection criteria to produce focused candidate lists, balancing player potential with the CRM team's outreach capacity.

3

**ML-Ready Data Pipeline:** Engineered behavioral features and integrated them with Vertex AI to support future graph-based machine learning development.

4

**Reusable Cloud Architecture:** Built configurable workflows in BigQuery, allowing the approach to be adapted to additional markets without rebuilding the core solution.

## The impact

### Achieved 97% faster identification of high-potential players, turning a delayed process into a more timely, actionable source of CRM insights.

#### 97% Faster Detection

Reduced identification time from months to days, enabling CRM teams to recognize and engage high-potential players much earlier.

#### Earlier Retention Opportunities:

Surfaced valuable players missed by existing targeting processes, creating opportunities for more timely, personalized outreach.

#### More Efficient Prioritization:

Transformed broad candidate lists into a focused, manageable group aligned with the team's capacity for follow-up.

#### Improved Data Quality Visibility:

Identified gaps in upstream activity data, helping the team address issues that could affect the completeness and reliability of player insights.

## What to read next

![Retail associate serving a customer](https://fusemachines.com/assets/images/2026/casestudy/demand-and-pricing-1.avif)

Demand forecasting

### Demand and pricing forecasts for a global accessories and luxury group

[Read more](https://fusemachines.com/success-stories/ai-powered-demand-forecasting-for-a-global-retailer/)

![Retail associate serving a customer](https://fusemachines.com/assets/images/2026/casestudy/price-elasticity-1.avif)

Demand forecasting

### Price elasticity for every SKU across Coach North America

[Read more](https://fusemachines.com/success-stories/price-elasticity-for-every-sku-across-north-america/)

## Make your forecast more useful to the people who depend on it

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