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Gamblitude predictive models

Content Recommendations for iGaming.

Help players find
what interests them.

Bring player preferences into sportsbook event discovery and casino game selection. Build recommendations around your available content, the placement players see and the experience your team wants to improve.

An operator-specific implementation, scoped with your product and data teams.

Discuss Content Recommendations Explore the approach
An ordered array of graphite tiles with two raised cobalt and titanium selections.
Sportsbook eventsCasino gamesLobby personalisation

01 / A more relevant selection

Connect player interests
with available content.

Content Recommendations is an operator-specific implementation for ranking sportsbook events or casino games around player interests and a defined product placement.

The intended output is an ordered selection of eligible content for a player or audience. Your team defines where it appears, what can be recommended and which interaction would make the selection useful. We agree the recommendation approach, delivery and evaluation around those decisions.

Decision supported
Which eligible content to show,
and in what order
Intended output
A ranked selection for an
agreed player or audience
Applications
Sportsbook events, casino games
and lobby ordering
Delivery
Placement, integration and
evaluation agreed together

02 / Two distinct product experiences

Relevant events.
Relevant games.

Sportsbook

Sportsbook event
recommendations

Help players discover available events that fit their interests. A sportsbook project starts with the event offer and the available evidence of interest in sports, leagues or competitions.

Timing is part of the design. Upcoming events, changing availability and start times determine which selections remain useful. We agree the refresh and expiry rules around the placement and its data.

The recommendation concerns event relevance. It does not predict the result, identify a winning bet or set odds and stakes.

Player interestsEvent availabilityEvent timing

Casino

Casino game and
lobby recommendations

Make game discovery more personal within an agreed recommendation area or lobby section. The project can focus on which games to show, their order, or both.

Start with the eligible game catalogue, available play history and the role of that placement. Agree how the selection should work alongside new releases, featured games and other editorial priorities.

Your product team retains control of the experience. Applying a ranking to the lobby is part of the agreed integration.

Game discoveryCatalogue contextLobby ordering

03 / Design the placement first

One selection.
A clear place in the product.

Start with one event-selection area, game carousel or lobby section and define the decision it needs to support.

Player context.

Establish whose selection is being produced and which history is available. Agree a starting approach for new players or sparse histories.

Content boundaries.

Define the eligible catalogue, availability and access conditions. Include the operator’s player-protection requirements in the delivery design.

Product ownership.

Agree the number of items, ordering, editorial rules and receiving interface. Define who owns publication and what the product shows when no suitable selection is available.

04 / A broad feature store

Learn from the player
and the content.

Gamblitude’s feature store brings together behavioural, transactional and contextual attributes. Recommendation work combines player history with available content and placement information.

These are candidate data families. The final selection depends on your catalogue, data quality and the interaction the recommendation is designed to support.

01

Sessions & activity

Patterns of returning, recent activity and the rhythm of product use.

SessionsRecencyFrequency
02

Product usage

The products, games or sporting content players have used over time.

Product usagePlay historyInterests
03

Channel & acquisition

Available channel, acquisition-source and cohort context.

ChannelAcquisition sourceCohort context
04

Content interactions

Recorded views, selections and subsequent activity, where those events are available.

Content viewsSelectionsInteraction history
05

Catalogue & metadata

Identifiers, categories and descriptions that make content comparable and selectable.

GamesEventsContent attributes
06

Availability & timing

The content available in a placement and the periods in which it can be shown.

Catalogue availabilityEvent timingPlacement context

The final inputs follow the data review; this is not a fixed production feature list.

Explore the data foundation

05 / Individually built for every operator

Your players. Your catalogue.
Your product.

Every operator has a different player base, market and product mix. Each Gamblitude machine learning model is built individually and trained on that operator’s historical data.

Development starts with your catalogue, player interactions and chosen placement. Sportsbook events and casino lobbies have different content, timing and evaluation needs. The recommendation method follows that context.

Your player base.

Learn from the preferences and activity reflected in your available history.

Your market and products.

Shape the approach around the content your players can actually access.

Your definition of relevance.

Agree which interactions indicate that the selection helped the player discover useful content.

06 / Measure the experience

A useful ranking must work
where players see it.

Historical evaluation helps compare selection approaches. A controlled product test measures what changes when players see the recommendations.

Check the selection.

Assess whether the ranking reflects the agreed relevance target. Check catalogue coverage, availability and how the approach behaves when history is limited.

Observe the interaction.

Measure exposure and interaction in the chosen placement where those events are captured. A recommendation that was never displayed should not be treated as one a player rejected.

Compare the experience.

Test against a comparable control group seeing the current selection approach. Keep placement and promotional treatment comparable so the result is not confused with a redesign or a different incentive.

Agree the outcome and observation period before the test. Engagement, discovery and any financial measures should be interpreted within the operator’s player-protection requirements.

07 / From placement to evaluation

Start with one
discovery decision.

  1. Choose the placement.

    Define the product area, audience and useful interaction.

  2. Review the history and catalogue.

    Assess player activity, content identifiers, metadata and availability.

  3. Develop the selection approach.

    Agree ranking, editorial constraints and the treatment of limited history.

  4. Connect the experience.

    Define delivery, publication ownership, refresh and fallback behaviour.

  5. Compare and refine.

    Evaluate the selection and run a controlled test against the current experience.

The practical details

Content
Recommendations.
Your questions.

The placement, player history and evaluation behind an operator-specific implementation.

Is this a standard service ready to connect?

This is an individually scoped implementation. We agree the recommendation approach, deliverables, integration and evaluation with your product and data teams for the selected placement.

Can the scope include both sportsbook and casino?

Yes. Sportsbook event discovery and casino game or lobby recommendations are distinct applications. Each needs its own content, relevance and timing decisions; a project can start with one placement in one product.

Are sportsbook recommendations betting predictions?

They concern which available events may interest a player. They do not predict sporting outcomes, recommend a stake or determine whether a bet offers financial value.

Does a ranking automatically change the lobby?

The ranking and the product display are separate parts of the implementation. We agree the receiving interface, publication process and ownership with your team.

What happens when a player has little or no history?

We agree and test a starting approach, such as an editorial, catalogue-based or popularity-based selection. Its purpose is to provide a useful starting experience while individual preference evidence is limited.

How often are recommendations refreshed?

The schedule is defined for the placement. Sports event timing, catalogue changes, available data and delivery requirements determine how often selections need to change.

How are availability and player-protection rules handled?

Your team defines the content and access conditions the experience must respect. We agree where those checks are applied in the delivery process and how unavailable or excluded content is handled.

How do we know whether recommendations improve the experience?

Assess selection quality, then compare with a control group seeing the current approach in the same placement. Agree the success measures and observation period in advance, and keep other product or promotional changes comparable.

Content Recommendations by Gamblitude

Start with the content
your players see today.

Bring one product placement and the rules it uses to select content.
Let’s define a recommendation, the history it can learn from and a practical way to test the experience.

Discuss Content Recommendations Explore all models