Content Recommendations 

A module of our real-time AI platform

Engage every player with 1-2-1 recommendations 

The Future Anthem real-time AI platform delivers personalised content for every player – increasing engagement, retention and measurable revenue growth

BoyleSports are utilising our 'Trending Games' output to power their 'Popular Recommendations'.

5-8%

Uplift in NGR

For a leading UK Bingo Operator 

2-2.5x

More games played

For a lottery in Canada

4-8%

Increase in spins

For a leading international gaming operator 

15-20%

Weekly engagement uplift

For a European gaming operator

Case study

Buzz Bingo

Achieving a 10% revenue uplift with personalised recommendations from Future Anthem

Buzz Group's Chief Product Officer, David Evans said: "Partnering with Future Anthem and leveraging their predictive models to create personalised recommendations and offerings has become a game changer for us."

Learn about the partnership with Godwin Allert & Chris Conroy

React in real-time

3 simple steps to Content Recommendations

Step 1

Integration

Anonymised transactional data

Minimise any roadmap impact by using existing transactional data feeds

Step 2

Utilise machine learning

Build behavioural profiles

Our real-time AI platform utilises proprietary AI technology to build individual player behavioural profiles

Step 3

Delivery

Real-time recommendations

We deliver recommendations back to you in real-time or batch

Request and response example

In this case, the client has requested “Recommended for you” specifically for slot games

[ 
{ 
"player": { 
"player_id": "000001", 
"site": "futureanthem.com", 
"jurisdiction": "uk", 
"control_group": false 
}, 
"categories": [ 
"slots" 
], 
"context": [], 
"recommendations": [ 
{ 
"category": "slots", 
"recommendation": [ 
{ 
"gameId": "000126", 
"gameName": "slot_125", 
"gameProvider": "provider_712" 
}, 
"last_updated_at": "2025-04-07" 
} 
]

Frequently Asked Questions

What data do I need to get started with Content Recommendations?

For Game Recommendations: player-level session and transactional data. Game metadata (theme, RTP, volatility, mechanics) is helpful, however Anthem has an in-house metadata store which can fill in this information in most cases. For Bet Recommendations: anonymised transactional data (ideally a stream of betslip events from your sports platform) and bet metadata — detailed information on markets and outcomes, used to understand transactional data.

How does the recommendation engine decide what to show each player?

Recommendations are generated using the player's entire transaction history, not just from the previous day. While recent transactions have a higher influence, the system takes into account broader patterns from all past activity to ensure recommendations are both relevant and personalised.

What happens if a player is new and has no history?

If a player has no transaction history, they receive the new player recommendations. These are games most likely to convert new players (i.e. all new players get the same recommendations) designed to activate brand new players who have no prior activity.

Can recommendations be personalised beyond gameplay - by theme, volatility or provider?

Yes. With Metadata Driven Personalisation, operators can create carousels tailored to attributes such as volatility, mechanics, or theme.

What uplift can operators expect from Content Recommendations?

Results vary by market and operator, but customers typically see measurable improvements within the first weeks of go-live: 3–4x ROI, 5–8% increase in NGR, 2–2.5x increase in games played.

How frequently do recommendations update?

Recommendations are re-calculated for every player every 7 days maximum, so even during inactivity a player's recommendations can change. Recommendations can be updated in real-time or batch, with the most common update frequency being daily.

Documentation

Getting Started with Content Recommendations

Data Integration Guide

Index

Deliver relevant content for every player

Index