◾RFM Analysis
RFM (Recency, Frequency, Monetary) Segmentation is a feature designed to help operators analyse and understand player behaviour.
By categorizing players based on their activity, RFM segmentation provides insights into how recently, how often, and how much players have deposited or bet. This segmentation enables operators to tailor their engagement strategies to match the specific needs and behaviours of each player group.
This feature evaluates player activity over the last 30 days, providing a clear view of user segments and their current engagement levels. By analysing these segments, operators can identify patterns in player behaviour, develop targeted campaigns, and implement strategies aimed at improving retention and maximizing the value of their player base.
There are 4 possible ways the business can choose to set up the RFM Segmentation Methodology:
Net Deposit by Label (default methodology)
Net Deposit by Brand
Casino GGR by Label
Casino GGR by Brand
Each methodology is based on different KPIs and aggregations. Detailed explanations can be found in the "How the RFM Model Evaluates Players" section below.
The simulation on how the distribution of the Segments looks under each of the 4 methodologies can be found in the RFM Model Alternatives tab:
RFM Segmentation is available under the RFM Analysis section in the Marketing view of the Smartico platform. It provides a visual representation of player groups and offers tools to create segments directly based on these insights.
What is RFM Analysis?
RFM analysis is a data-driven approach to segmenting users based on three core dimensions:
Recency (R): How recently a player made their last activity (either a Deposit or a Bet - subject to the chosen Segmentation Methodology).
Frequency (F): How often a player engages with the platform over time (either by depositing or betting - subject to the chosen Segmentation Methodology).
Monetary (M): The total value of a player's KPI (either a Deposit or a Bet - subject to the KPI of the chosen Segmentation Methodology), reflecting their financial or gaming impact on your platform.
With these metrics, you can identify distinct user segments and create targeted strategies to enhance their value and engagement.
How the RFM Model Evaluates Players
Eligibility:
The population, considered for the calculation of the segment, is all players with the relevant activity (either a Deposit or a Bet - subject to the KPI of the chosen Segmentation Methodology) during the last 30 days.
The population, considered for the calculation of the segment, is all players with the relevant activity (either a Deposit or a Bet - subject to the KPI of the chosen Segmentation Methodology) during the last 30 days.
Ineligible users are excluded from all RFM segments.
RFM Granularity:
The Model Values can be calculated either based on the Label Granularity of the Brand Granularity.
A Business can have more than one Brand for the Label. The business logic for having several Brands per Label might differ. One of the common reasons is defining each country operation as a separate brand. In case the Label has more than one Brand, it's recommended to use the "By Brand" type Methodology for more relevant Segmentation.
"By Label" type Methodology: The RFM related KPIs are calculated for all the player across the Label with the relevant activity (either a Deposit or a Bet - subject to the KPI of the chosen Segmentation Methodology) during the last 30 days. Then the segments are assigned based on the RFM Scoring logic.
"By Brand" type Methodology: The RFM related KPIs are calculated for all the player across the Brand with the relevant activity (either a Deposit or a Bet - subject to the KPI of the chosen Segmentation Methodology) during the last 30 days. Then the segments are assigned based on the RFM Scoring logic.
For example:
We have a population of 5 players with different attributions
Player 1 - Label A; Brand X
Player 2 - Label A; Brand X
Player 3 - Label A; Brand Y
Player 4 - Label A; Brand Y
Player 5 - Label A; Brand Y
Label A RFM Model calculation will be based on all the players' KPIs.
Brand X RFM Model calculation will be based on the KPIs of the Player 1 and Player 2
Brand Y RFM Model calculation will be based on the KPIs of the Player 3, Player 4 and Player 5
RFM Granularity:
The Model Values can be calculated either based on the Label Granularity of the Brand Granularity.
A Business can have more than one Brand for the Label. The business logic for having several Brands per Label might differ. One of the common reasons is defining each country operation as a separate brand. In case the Label has more than one Brand, it's recommended to use the "By Brand" type Methodology for more relevant Segmentation.
"By Label" type Methodology: The RFM related KPIs are calculated for all the player across the Label with the relevant activity (either a Deposit or a Bet - subject to the KPI of the chosen Segmentation Methodology) during the last 30 days. Then the segments are assigned based on the RFM Scoring logic.
"By Brand" type Methodology: The RFM related KPIs are calculated for all the player across the Brand with the relevant activity (either a Deposit or a Bet - subject to the KPI of the chosen Segmentation Methodology) during the last 30 days. Then the segments are assigned based on the RFM Scoring logic.
RFM Scoring:
Recency: Ranks players based on days since their last activity. For the "Net Deposit" Segmentation Methodology, the Deposit is considered as Activity, while for the "Casino GGR" Segmentation Methodology, the Casino Bet is considered as Activity
Frequency: Evaluates average activity intervals. For the "Net Deposit" Segmentation Methodology, the Deposit is considered as Activity, while for the "Casino GGR" Segmentation Methodology, the Casino Bet is considered as Activity
Monetary: Assesses cumulative KPIs. For the "Net Deposit" Segmentation Methodology, the Deposit Amount is the measured KPI, while for the "Casino GGR" Segmentation Methodology, the Casino GGR is the measured KPI.
Each user receives a score from 1 to 5 in each category, with 5 being the highest. Combining these scores determines their segment.
Monetary Rank - divides all players into 5 equal groups (quantiles) based on either the Net Deposit or the Casino GGR amount, from lowest to highest.
Recency Rank - divides all players into 5 equal groups (quantiles) based on how recently they made a Deposit or placed a Casino Bet.
Frequency Rank - divides all players into 5 equal groups (quantiles) based on activity intervals (either a Deposit or a Bet - subject to the KPI of the Segmentation Methodology) — longer intervals imply lower frequency. Sorted descending (longer intervals = less frequent).
How It Works
Visualizing Your RFM Segments:
Graphical Representation: The "Currently Used RFM Model - Population Distribution" chart provides an intuitive visualization of player segments
Segment Details: Hovering over or selecting a segment provides a detailed breakdown, including the size and percentage of active players it represents.
Each player will have their own property for RFM Analysis which can be used in segmentation and in campaigns.
Campaigns can also be triggered by the event of "RFM segment update"
Interactive Features:
Segment Insights: Click any segment to view its characteristics and suggested strategies for engagement.
Create Targeted Campaigns: Use the Create Segment button to define custom user journeys, such as tailored tournaments or personalized offers, directly from the analysis.
1. Champions
Traits: Recent, frequent activity with high spending.
Strategy: Reward them with exclusive bonuses, VIP perks, and personalized experiences to maintain their loyalty.
2. Loyal Customers
Traits: Consistently active users with moderate to high spending.
Strategy: Strengthen their loyalty through personalized rewards programs and early access to new features.
3. Losing But Engaged
Traits: Recently active, but showing a declining frequency of activity.
Strategy: Rekindle interest with time-limited offers and challenges.
4. At Risk
Traits: Previously active, but with no recent activity, indicating declining engagement.
Strategy: Encourage their return with cashback offers or personalized reactivation campaigns.
5. Need Attention
Traits: Low-frequency players with small KPI amounts.
Strategy: Target them with promotions tailored to their favourite games.
6. Promising Customers
Traits: New users with moderate KPIs, showing growth potential.
Strategy: Provide incentives for early engagement and introduce community challenges.
Traits: Recently acquired players with initial KPIs of moderate size.
Strategy: Gradually increase rewards and tailor game recommendations.
8. Hibernating Customers
Traits: Previously active players who have disengaged.
Strategy: Reconnect by offering nostalgic incentives like free spins on their favourite games.
Monitoring the RFM changes
Daily Trends: Daily share of the players with the Segment and Model-related activity.
Best used to monitor short-term changes in the Business. For example, the day-over-day increase in the "New Customers" segment share might indicate an immediate increase in acquisition.
Weekly Trends: Weekly share of the players with the Segment and Model-related activity.
Best used to monitor mid-term changes in the Business. For example, the week-over-week increase in "Champions" segment share might indicate marketing optimization.
Monthly Trends: Monthly share of the players with the Segment and Model-related activity. Best used to monitor long-term changes in the Business.
For example, the month-over-month increase in the share of the "About to Sleep" and "Need Attention" segments might indicate either a retention issue or faulty acquisition.
FAQ
Q: How often are RFM categories recalculated?
They are recalculated daily
Q: What metrics are taken as the basis for RFM segmentation?
We employ the classic RFM approach and adhere to the generally accepted practice.
"Days since last activity" (either a Deposit or a Casino Bet - subject to the used Methodology) - for Recency,
"Average activity interval" (either a Deposit or a Casino Bet - subject to the used Methodology) - for frequency,
"KPI" (either a Deposit or a Casino GGR - subject to the used Methodology) - for Monetary
Q: Are the segment boundaries rigid?
The exact thresholds are constantly changing, based on the player's activity, and the numbers are based on the specific client's data. Also, the measured population is dependent on the Segmentation Type and can be based on either the entire Label or a specific Brand.
If we assume that all players, split in equal numbers per day, made an activity each within the past 20 days, then the recency 5 will be given to those that were active within the last 4 days, recency 4 will be assigned to those that were active from 5 to and including 8 days, and so on.
Q: Can I see the exact matrix, how players are assigned to different segments based on R, F, and M scores?
Sure, here it is
Champions
555, 554, 544, 545, 454, 455, 445
Most active, very recent and high-value players.
Loyal Customers
543, 444, 435, 355, 354, 345, 344, 335
Repeat players, consistent activity and decent value.
Potential Loyalist
553, 551, 552, 541, 542, 533, 532, 531, 452, 451, 442, 441, 431, 453, 433, 432, 423, 353, 352, 351, 342, 341, 333, 323
Active and promising — may turn into loyal customers.
Promising
525, 524, 523, 522, 521, 515, 514, 513, 425, 424, 413, 414, 415, 315, 314, 313
Recent players with moderate activity and spend.
New Customers
512, 511, 422, 421, 412, 411, 311
Just joined — limited activity so far.
Need Attention
535, 534, 443, 434, 343, 334, 325, 324
Decreasing activity — may require re-engagement.
About To Sleep
331, 321, 312, 221, 213, 231, 241, 251
Activity is fading — players might stop soon.
Hibernating Customers
332, 322, 233, 232, 223, 222, 132, 123, 122, 212, 211
Very low interaction — likely dormant.
At Risk
255, 254, 245, 244, 253, 252, 243, 242, 235, 234, 225, 224, 153, 152, 145, 143, 142, 135, 134, 133, 125, 124
Used to be active — now disengaging.
Losing But Engaged
155, 154, 144, 214, 215, 115, 114, 113
Still online, but less valuable than before.
About To Churn
111, 112, 121, 131, 141, 151
Nearly lost — minimal play and spend.
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