MarTech & Player Intelligence

You have millions of player records. The signal is in there. You just can't hear it yet.

Vous disposez de millions de profils joueurs. Le signal est là. Vous n'avez pas encore les outils pour le lire.

Most operators can tell you how many players registered last month. Few can tell you which players are about to go dormant, which segments have never been touched by a retention campaign, or what a 5% improvement in attrition would be worth in hard revenue. VA Tech fixes that — using the player data systems, CRM, and lifecycle automation that the industry calls MarTech.

Request a Revenue Diagnostic How we work
The Cost of Doing Nothing

When player data sits unused, the cost is measurable — and it compounds.

We build the data infrastructure, predictive models, and automated CRM systems that turn transaction logs into player intelligence — and player intelligence into incremental GGR. The operators who move earliest have the clearest competitive advantage. The ones who wait find out what it cost them at the end of the quarter.

  • Digital channel share flat for 6+ consecutive quarters — despite continued acquisition investment
  • 360 million+ annual transactions in the dataset — the vast majority generating no analytical output
  • Retention costs 5–7× less than acquisition — yet acquisition receives the majority of budget
  • High-value players go dormant undetected — identified only after the revenue is already gone
Typical operator — before diagnostic
13%
Digital share — flat for 6 quarters
360M+
Transactions in dataset — largely unanalysed
63bn XOF
Identified annual upside across 5 segments
What We Deliver

Six capabilities. One outcome: players who stay, spend more, and generate measurable GGR uplift.

Each capability is deployable independently or as part of a full-stack programme — depending on where you are in your maturity curve.

01

Predictive Attrition Scoring

We build and deploy ML models that assign a daily attrition probability score to every active player — flagging at-risk accounts before they go dark. Our production models have achieved 87% accuracy in a West African sports betting deployment. You get a working model, not a prototype.

87% accuracy
02

Player Lifecycle Segmentation

We divide your player base into behavioural cohorts based on recency, frequency, spend trajectory, and channel preference. Each segment gets a distinct treatment logic — not a mass message with a first-name personalisation field.

Behavioural cohorts
03

Automated Retention Journeys

We configure and launch triggered communications across SMS, push, email, and USSD — based on player behaviour, not calendar schedules. When a player's spend drops or a high-value account goes quiet, the system responds within hours, not days.

SMS · Push · USSD
04

Loyalty Programme Architecture

We design and implement tiered loyalty systems calibrated to your player economics: thresholds that drive the right behaviour, rewards that carry perceived value, and mechanics that survive margin pressure. We build for sustainability, not just launch-day excitement.

Tiered systems
05

Bonus Engine Setup & Governance

Bonus spend is one of the largest cost lines in any gaming P&L. We configure your bonus engine with segment-specific eligibility rules, wagering conditions, and approval workflows — so your promotional budget drives retention and measurable LTV growth, not bonus arbitrage.

Margin-safe
06

Cross-Sell & Upsell Trigger Logic

We map the behavioural signals that indicate a player is ready to move — from retail to digital, from single-product to multi-product, from low to high value — and build the trigger logic that acts on those signals at the right moment.

Signal-based
01 The process

We do not start with a platform recommendation. We start with your data.

Diagnose Weeks 1–2

Before we recommend anything, we conduct a structured audit of your player data, CRM setup (or absence of one), channel mix, and current retention economics. We identify which segments exist in your data, which are being addressed, and where the measurable revenue gap sits. The primary deliverable: a player segmentation audit and revenue gap report — a quantified picture of what your current player base is worth versus what it should be worth.

Design Weeks 3–6

We produce a full MarTech architecture specification tailored to your player base and market context. This includes the Central Data Hub design, data model, and CRM workflow blueprints — along with ML model specification, journey logic, bonus governance framework, and a measurement plan with KPIs tied to GGR impact.

Deploy Sprint-based — 90-day embedded review

We implement in sprints, with weekly output reviews so you can see progress against the baseline established in Step 01. Integration, testing, and go-live are structured to minimise disruption to live operations. We stay inside the operation for the first 90 days post-launch, monitoring model performance, adjusting trigger logic, and tracking revenue impact.

The diagnostic snapshot
Methodology illustration — Conducted for a national lottery operator in Côte d'Ivoire. At proposal stage. We present it to illustrate our diagnostic approach — not as a delivered engagement outcome.

Our analysis of 360 million annual ticket transactions, 45 million USSD betting events, and 12 million app interactions identified five distinct player segments that had received no targeted retention or lifecycle communication. The problem was not acquisition — the digital player base existed and was growing. The problem was what happened after registration.

63bn XOF
Annual upside identified across 5 player segments
02 Results from the field

Numbers from live deployments.

252%
Projected ROI within the first year of deployment
Modelled against pre-engagement GGR baseline — West African sports betting operator, 2024
87%
Attrition prediction accuracy in production
Deployed operator — West African sports betting engagement, 2024
50%
Reduction in player acquisition cost post-implementation
West African sports betting operator engagement, 2024
Conversion uplift on reactivation campaigns
Against broadcast control group — same operator, 2024
<9 mo
Payback period, full programme
Initial infrastructure investment recovered before end of first year of operation

VA Tech's default engagement model is incentive-aligned: we earn a share of the incremental GGR we help you generate. If your digital revenue does not grow, we do not earn. That changes everything about how we work — and everything about the rigour we bring to the diagnostic.

All engagements are conducted under mutual NDA. We do not extract or retain operator data beyond the diagnostic period.

See our engagement models →
Revenue Diagnostic

Get a quantified view of your revenue gap — in your market, against your numbers.

Our revenue diagnostic gives you a quantified view of the gap in your current player base — the segments that exist in your data, which are unaddressed, and what closing that gap is worth in hard GGR. Before you commit to anything.

Typically completed within 5 working days. No commitment required.

We'll respond within one working day. No commitment required.

Or explore our engagement models →