What's an RMG Player Worth? Approaches to Modeling Lifetime Value and Target CPAs
The lifetime value (“LTV”) of a player is the amount of revenue you expect that player to generate over the course of them being active. LTV is a critical component to managing any consumer-focused business and because of the skewed distribution in the RMG (real money gaming) industry, LTV is an even larger component in how online sportsbook, casino, fantasy sports, or other RMG businesses should be operated.
Modeling lifetime value allows companies and marketers to determine the revenue that is generated from a player (or cohort of players) over time. The ability to create a relatively accurate model allows for better real-time business and marketing decisions and sets expectations to manage revenue flow in the future (and liquidity).
LTV modeling will require assumptions and in the case of companies with historical data on play, these assumptions can be guided by these trends and for companies that have limited or no data, reasonable assumptions can be inputted into the model.
Simple extrapolation takes the revenue generated from a cohort of players and then extrapolates that revenue on historical trends.
For example, in the below chart, we want to project the value of Cohort A after we have one month’s worth of data. Cohorts B through E represent segments of players and the historical data that we have collected and the cohorts become more narrowly defined from Cohort B → E. For example, Cohort D is the same as Cohort C, except only includes players that bet on football as their first bet after depositing.
* Segmentation by state can be important provided different economic standards and taxations laws
** Important to review the distribution of revenue to ensure outliers (or any data point that may not be indicative of the future is removed or adjusted)
The advantage to being more inclusive in the reference cohort is that you have a larger sample size and more reliable data and the advantage to having a smaller curated reference cohort is that the composition of the cohort is more similar to the cohort you are projecting out.
If we wanted to project revenue for Cohort A referencing Cohort E then Cohort A’s:
= (Cohort A’s 1-month revenue) x [(Cohort E’s 3-month revenue) ÷ (Cohort E’s 3-month revenue)]
= $55 x ($171 ÷ $57) = $165
In other words, this formula grew Cohort A’s revenue by the same ratio that Cohort E’s total revenue grew between the first month and the third month.
Important to note that there are factors that impact the 1-month LTV beyond the factors listed in “Sample of Factors that can change LTV and relationship to LTV” including the state (economic status and tax implications), the size of the offer (e.g., risk-free bet up to $1,500), and any significant change between sports or the economy in the reference year versus the current year.
Fundamental models can be leveraged to model LTV taking into account the impact of strategic initiatives. A simple fundamental model could take into account actives by sports played and then leverage a year-over-year reactivation rate multiplied by the average revenue per active to estimate LTV.
For example, if you had 1,250 actives during NFL this year and on average each generated $35 of revenue on NFL games, then you could model out the revenue expected from that cohort by:
= prior NFL actives x reactivation rate x revenue factor for each active
If we assume a 65% reactivation rate and a 1.3 year-over-year revenue factor, then that is:
= 1,250 x 65% x 1.3
= $36,968.75 of revenue projected for next year from this year’s NFL actives
In this example, you would have to be cognizant of promotions since a very compelling promotion may lead to people betting on a new sport but these people are likely to have a higher churn rate compared to people who bet on the sport without an incredibly compelling promotion and may make sense to model out the types of players separately.
Fundamental models can be built in a variety of ways and in addition to projecting future revenue can also assist in prioritizing initiatives by segmenting the revenue that would be impacted by an initiative and then assuming different levels of impact (for example, a small iOS product improvement may make a bigger difference to LTV and projected revenue than a medium-sized Android product improvement, if 85% of the revenue is generated on iOS).
Setting a Target Cost-per-Acquisition:
Now that we know how much a player is worth we can set target CPAs to determine how much we want to pay for a player, in an industry where 10% of players may make up as much as 50%+ of the revenue.
The chart below illustrates the most basic cashflows associated with acquiring a player:
The expense segment can include a variety of subcomponents including the marketing costs to acquire a player, promotion costs, and any additional expenses associated with acquiring a player.
The revenue segment can be defined in a variety of ways. Gross gaming revenue (“GGR” and also known as “hold”) is typically defined as player losses minus player wins. Net gaming revenue (“NGR”) takes into consideration additional expenses that may include promotional bonuses among other expenses.
The lifetime value approach is generally leveraged when the objective is to maximize profit and the payback period approach is utilized when shorter-term liquidity may be the priority.
Lifetime Value: Your target CPA is set to be the revenue expected over a specified period of time. This approach tends to be preferred by companies that want to scale aggressively with longer durations associated with being more aggressive. For example, in the above chart, if you want to acquire with CPA being defined as the 3-year LTV, then you would set $1,500 as the target.
Payback Period: This approach defines target CPAs by the amount of time required after a player is acquired in order for the revenue to equal the target CPA. The Payback Period generally aligns with the liquidity preferences of the company. This approach is applied when maintaining liquidity is a priority and results in a more conservative growth trajectory. For example, an 18-month payback period on the above chart would equate to a target CPA of $800.
Sample of Factors that can change LTV and Their Relationship to LTV:
- CRM (taking unsubscribe and block rates into consideration)
- Retention Offers
- Product changes that increase retention, play, and ratings
- Interest in sports:
- Local (micro markets)
- National (macro markets)
- Level of aggressiveness in acquisition
- Cross-selling into additional products
- Casino game inventory
In conclusion, the ability to model LTV allows decision-makers to determine target CPAs, project future revenue, and ensure that the growth and marketing strategies developed to align with the organization's objectives.
This article is a high-level overview of common approaches to LTV, every RMG operator has its own unique combination of priorities and modeling has to be adjusted to account for the nuances. If you have any questions about LTV modeling, setting target CPAs, or want to learn more about Flatiron Gaming and how we can help, please visit our website or email me directly at email@example.com
Flatiron Gaming is a leading digital agency for US sportsbook and casino platforms (real money gaming). We’ve efficiently spent $300M+ on US RMG advertising, won 3x Webby Awards, achieved two #1 sports apps, and are live across all legalized US states. Some of our partners include: Caesars Sportsbook & Casino, FanDuel, William Hill, WSOP, Parx Casino, Firekeepers Casino, Jackpocket, Lucktastic & more.