◾Favorite product, game, game type & game provider
Smartico is making a statistical analysis of user activity and bringing the 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 favorite games - 3 games ranked by user preference.
Top 3 game types - 3 game types ranked by user preferences
Top 3 game providers - 3 game providers ranked by user preferences
All these categorization types use a time-decay approach, meaning that if a user changes their preferences, the model captures those changes. For example, if the user played a lot of "Golden slot" a year ago, that will be less important than what they play now.
Favorite product
The "Favorite Product" feature enhances marketing and gamification 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 properties 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 who have a strong focus on Casino games to participate in sports 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 the report is showing the general behavior of your users, you can also use this information on a personal level.
Using game type, name, and provider in segmentation
So, you can use properties like:
core_fav_game_type_top3 - indicates the user’s top 3 most-played game types, ranked by importance (slot, table...)
core_fav_game_top3 - indicates the user’s top 3 most-played or preferred games, ranked by importance
core_fav_game_provider_top3 - indicates the user’s top 3 most-played or preferred game providers, ranked by importance
And, a special operator in conditions like "Has on pos1. any of" gives you the possibility to find the most popular game, type, and provider.
Using game type, name, and provider in the missions or tournaments
You can target missions according to users' preferences, for example, to target "Blackjack" missions to users who like to play Blackjack
You can follow the same approach in tournaments, jackpots, or any other gamification mechanics
For example, the 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, sports & 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: How often are favorite properties recalculated?
We aim to recalculate after midnight, within the time window from 2:30 to 4:30 AM UTC.
This time is chosen to have minimal impact on the overall system performance and have fresh betting activities already delivered from the integrated systems to Smartico.
Q: Are there any weights used in the calculation
We use an optimized exponential function delivering coefficients for each day, so that the highest weight is assigned to today's bets and a very small weight is assigned to the bets made 30 days ago. This way, we achieve a smart bias towards players' recent activity.
Q: Does it include events that happen only after the feature activation, or does it take historical events too?
The model includes all historical bets and is not dependent on the time of feature activation.
Q: If there were favorite games defined, but then the user places no bets in 30+ days, does the model keep the value that was previously recorded?
The model will preserve the user's latest favorite preferences, even if the user is no longer active.
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