GAMWIT's Game Recommendation model predicts which games a player is most likely to play next, so the lobby, the post-session prompt and the CRM message each show a game the player is likely to open.
Player Recommended next game
Example rows, not live data shape as in the GAMWIT platform
Why recommendations matter
Cross-selling works when the suggested game fits how the player already plays. GAMWIT's Game Recommendation model picks that next game for each player.
See what your team getsOne who also plays casino is usually worth more than one who only bets on sports.
One who tries table games is usually worth more than one who only plays slots.
Source: GAMWIT's use-case guide.
A real screen from a licensed GAMWIT account, on sample data. Recommendations per player, ready for the lobby or the CRM.
Every player, scored
Three sets per player: new games, popular games and the player's own top-rated games, as game codes from the catalog. The lobby, the post-session prompt and the CRM message can each draw from a different set.
Five players shown on screen; the full set downloads as a file. Sample data.
These are sample players. In a walkthrough, we run the Game Recommendation model on yours.
Book a walkthroughWhat changes
Same library, same players. A different game in front of each of them.
The lobby
Without GAMWITThe same featured row for everyone.
With GAMWITA recommended-for-you row per player, 20 to 50 titles deep.
New players
Without GAMWITGuesswork, or whatever the homepage promotes this week.
With GAMWITPopular and trending titles until GAMWIT has a history to read.
Existing players
Without GAMWITWhatever they found last time.
With GAMWITPersonalized to their play style, refreshed as it changes.
Cross-sell
Without GAMWITA banner everyone scrolls past.
With GAMWITA sports bettor shown a table game they are likely to enjoy, at half time.
New launches
Without GAMWITPromoted to the whole base, played by a few.
With GAMWITRecommended only to players who play similar themes and mechanics.
How it works
Why a big library goes unplayed
Libraries are too big to browse
Hundreds of titles, one featured row. Most players never see most of the catalog, and a player who cannot find a game they like plays less.
New games launch to the wrong audience
A launch promoted to everyone reaches the few it suits and annoys the rest. Traffic on a new title stays low because the right players never hear about it.
Cross-sell banners do not fit the player
Moving a player from sports to casino, or slots to tables, only works when the suggested game fits how that player already plays.
New games, popular games and top-rated games, each set built per player so the lobby, the post-session prompt and the CRM message can draw from a different one.
A player with a history gets recommendations from their own play style. A player without one gets what is trending, until GAMWIT has enough to read.
The depth of the list is set to the surface it feeds, from a five-title lobby row to a full recommended-for-you page.
In practice
Examples from GAMWIT's use-case guide. They show how the model is used, not a customer's results.
Sports to casino bridge
The situation
At half time, sports bettors leave the app.
What GAMWIT does
GAMWIT identifies the bettors likely to enjoy table games. A half-time notification offers a few free chips on live roulette, and play continues through the break.
Reviving a quiet launch
The situation
A new game launches to low traffic.
What GAMWIT does
GAMWIT finds the players who play similar themes and mechanics. The launch email goes only to them, and the game finds its audience.
Tournament recruitment
The situation
Slot tournament participation is low.
What GAMWIT does
GAMWIT recommends the games that appeal to competitive players, so the titles in the tournament reach the players most likely to enter.
How to use the results
Quick winAdd a 'Because you played' row to your email footer, fed from the recommendation set.
Try it yourself
Sign up and build your first model yourself, no setup call needed. Prefer a guided tour? Book a walkthrough instead.
Getting started
The Game Recommendation model at a glance
What it asks of your team
Marketing, Finance, Risk and Compliance teams use it directly, without an analytics background.
Wherever and however your gaming data is stored, the GAMWIT team handles the connection. See how GAMWIT connects
Once connected, the model runs on its own and delivers fresh predictions on every run.
FinalistCompliance Innovator of the YearVixio · 2024
NominatedCompliance Technology ProviderEGR North America · 2024
NominatedData and AI PartnerEGR B2B · 2022 · 2023The Game Recommendation model is already built. Connecting your data takes 2 to 4 weeks.
