◾Favorite product, game and game type
Smartico is making statistical analysis of user activity and brining following information in the reporting and as properties available for segmentation:
Favorite product - categorisation of users by preferences in products: casino only, sport only, more sport/less casino etc
Top 3 favotite games - 3 games ranked by user preference.
Top 3 game types - 3 game types rank by user preferences
All these categorization types are using time decay approach, means that if user is changing his preferences, the model is catching such change. E.g. if user played a lot in "Golden slot" a year ago, this will have less important comparing to what he is playing now.
Favorite product
The "Favorite Product" feature is designed to enhance marketing and gamification possibilities by identifying and categorizing user preferences based on their betting activity.
This feature analyzes recent betting behavior to help you understand which products—Casino, Sports, or Lottery—your users prefer. This allows for more targeted and personalized marketing strategies.
Logic Behind
The feature calculates user preferences by analyzing their betting activity over the last 30 days. Each bet the user places is assigned a weight, where more recent bets carry more influence than older ones.
This is done using a time decay logic, which smoothly decreases a bet's weight as it ages. The weighted bets are then aggregated to determine the percentage share of each product (Casino, Sport, Lottery) that the user engages with. This approach ensures that the shares accurately reflect a user’s current preferences, leading to more precise and timely user categorization.
Available user properties
The following user propes are calculated daily for each active user and can be used in the segmentation for marketing and gamification activities.
core_fav_product_type - indicates the user’s preferred product category based on their engagement with Casino, Sports, and Lottery products. Possible values:
Casino Only
Sport Only
Lottery Only
More Casino, Less Sport
Casino and Sport
More Sport, Less Casino
Mixed Preferences
core_fav_product_casino_p - the percentage of the user’s engagement attributed to Casino products.
core_fav_product_sport_p - the percentage of the user’s engagement attributed to Sports products.
core_fav_product_lottery_p - the percentage of the user’s engagement attributed to Lottery products.
Example of usage
Let’s say an operator plans a new promotional campaign focused on Casino products. Using the "Favorite Product" feature, one can segment his/her users based on their preferences and engagement.
Case 1: Segmenting Users
Target Segment: Users categorized under Casino Only, More Casino/Less Sport, Casino and Sport, etc.
Why: These users clearly prefer or strongly engage with Casino products, making them the ideal audience for your Casino promotion.
Case 2: Enhancing Engagement
Track the campaign’s success by monitoring changes in core_fav_product_casino_p. If this percentage increases after the campaign, it indicates successful engagement.
Case 3: Product Adoption
Target players that have a strong focus on Casino games to participate in the Sport betting, build promotional campaigns for such users, address mini-games, missions and tournaments
Favorite Games & Game types
In the Games & Sports reports you can now identify the top favored casino games by name or type over a specifically set time window. The report is based on user preferences and engagement and it is based on the number of unique users, total bets, and share of favorite games.
While report is showing general behavior of your users, you can also use this information on the personal level.
Using game type and game name in segmentation
So, you can use properties like:
core_fav_game_type_top3 - indicates user’s top 3 most-played games types, ranked by importance (slot, table...)
core_fav_game_top3 - indicates user’s top 3 most-played or preferred games, ranked by importance
And special operator in conditions like "Has on pos1. any of", is giving you possibility to find the most popular game or game type.
Using game type and game name in missions or tournaments
You can taget missions according to users preferences, for example to target "Blackjack" missions to users that like to play Blackjack
You can follow same approach in tournaments, jackpots or any other gamification mechanics
For example, tournament target players of Gold Quest
FAQ
Q: What factors are taken as the basis for Product preference calculation
We are taking turnover for casino, sport & lottery.
Q: What is the time interval of data used in the calculation
We take and recalculate data daily using 30-days of historical data
Q: Are there any weights used in the calculation
We use optimized exponential function delivering coefficients for each day, so that highest weight assigned to today's bets and very small to the bets made 30 days ago. This way we achieve smart bias towards players' recent activity.
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