Video Poker Analyzer (“VPA”) is an innovative application made possible by Foundation, the Acres hardware platform that reports granular, real-time slot-machine data. For each video poker hand played, VPA analyzes the player’s skill by comparing their actual hold / discard decision to the mathematically optimal play. With the ability to distinguish between desirable High Margin players and unprofitable Advantage Players (“APs”), casino operators can make significantly more efficient player reinvestment decisions and ultimately enhance profitability.
VPA data from Casino A, which caters to the video poker-centric Las Vegas locals market, shows APs have a tremendous negative impact on video poker profitability. With data from over three million skill-rated hands and more than 1,000 identified carded players, VPA reveals that a small group of APs inflict a roughly 28% loss on carded win, which is defined as the casino’s win on carded play.
Overall, APs represent less than 2% of the broader player population yet are responsible for more than 25% of coin-in. This dynamic inflicts a double negative on Casino A’s bottom line. First, APs are beating Casino A to the tune of $0.15 per hand played. Second, because APs produce a disproportionate amount of coin-in, Casino A’s player’s club rewards them with a disproportionate amount of free play that is used to fund additional risk-free play.
VPA identifies each player error in video poker and quantifies the value of that mistake. As players complete more and more hands, a skill rating can be assigned to help predict the casino’s profit and loss each time the carded player returns.
In each game of video poker, a 5-card hand is dealt. The player strategically decides which cards to hold and which to discard, with a goal of making a winning hand. VPA compares every player’s decision against the optimal strategy for any 5-card hand dealt to quantify the added value of suboptimal play.
The chart below demonstrates a real-life player’s error rate as tracked by VPA. In this single hand of Bonus Poker Deluxe, Player 1’s optimal play would have been to hold the dealt pair of 5s in attempt to make a 3-of-a-kind, 4-of-a-kind, or full house. But Player 1 instead made a suboptimal decision that resulted in a diminished chance of winning. This single error benefited the casino by adding an additional $0.76 of theoretical win to the hand.
A long-term analysis of this individual player over multiple visits reveals mistakes on 9.04% of all hands played for a net mistake value of $541.66.
Average Bet
$16.68
Hands
4,622
Mistakes
Mistakes
Mistake Rate
9.02%
Mistake Value
$541.66
VPA evaluates all play and classifies players according to skill level, identifying players as High Margin, Expert or Advantage.
Not Yet Rated
Expert
Advantage
Player hasn’t played a skill-rated hand yet
Player error on greater than 2% of hands
Player error on fewer than 2% of hands
Player error on fewer than 1% of hands, or
Experts with over 1,000 hands
As demonstrated below, the player’s skill rating significantly impacts casino profitability with high skilled APs producing a significant net loss to the casino.
The chart below shows the distribution of player skill levels at Casino A and provides conclusive proof that APs represent a significant negative impact on profitability. While accounting for just over 1% of the population, APs spend over 26% of total coin-in and reduce Net Win by a similar amount. Given this data, Casino A should take immediate action to disincentivize these players by making them ineligible for free play, marketing or casino credit offers.
Advantage
Expert
High Margin
TBD
Total
% of Players
1.47%
3.24%
86.82%
8.47%
100%
% of Coin-in
26.82%
0.28%
69.24%
3.66%
100%
% of Win
-25.64%
0.80%
120.47%
4.36%
100%
Win per Hand
$(0.15)
$0.17
$0.08
$0.08
$0.06
In addition, the distribution of Net Negative players – players who have inflicted a net loss to the casino over their lifetime play history – across each skill level demonstrates the threat APs pose to profit. The chart below shows that over two thirds of APs are winning money against Casino A.
Advantage
Expert
High Margin
TBD
% of Players who are Net Negative
66.67%
24.24%
18.35%
15.12%
Casino rewards today – powered by outdated legacy systems technology that can’t process granular, real-time data – are driven almost entirely by coin-in. Determining rewards based on coin-in is incredibly counter effective to video poker profitability because it incentivizes unprofitable players at the expense of profitable ones. Over half of Casino A’s marketing expense is devoted APs who produce 27% of coin-in – and redeem casino marketing offers at incredibly high rates – yet represent just 1% of the population.
A typical casino point program can be applied to VPA data to model what reinvestment expenses look like across the High Margin and AP populations. Acres’ model assumes a theoretical win rate of 2% on video poker and a free play reinvestment of 10% of theoretical loss. While actual reinvestment structures vary by casino, all reinvestment is a derivative of coin-in, resulting in APs receiving an outsized portion of reinvestment at the expense of more profitable High Margin players.
Advantage
High Margin
$118,263.18
$5,187.04
Theoretical Win (2%)
$2,365.26
$103.74
Free Play Reward (10%)
$236.53
$10.37
Actual Win
($1,602.91)
$105.05
The data makes it very clear that Casino A’s inability to differentiate between High Margin players and APs leads to wildly inefficient player reinvestment strategies that reward Net Negative customers significantly more than High Margin players. Consider the table above, where the average AP produces 23 times more coin-in than the average High Margin player. With no ability to identify player skill, Casino A’s marketing analysis can only falsely conclude that the AP is 23 times more valuable.
Simply put, the inability of legacy casino management systems to process all the data reported by the slot machine results in Casino A literally paying AP to repeatedly come beat the house.
By clearly identifying players according to skill level and profit potential, the combination of Acres’ Foundation and VPA enables casinos to exclude APs from marketing rewards and instead redeploy those incentives to High Margin players. The table below shows the unlocked hidden value of High Margin players by adding player mistake value to the theoretical win per hand.
High Margin
Average Bet
$3.15
Theo Win per Hand
$0.06
Mistake Value per Hand
$0.05
Added Theo Profit per Hand
83.33%
With the small AP population receiving an average of $236.53 in rewards, there is significant opportunity to eliminate incentives to APs and instead redeploy to the High Margin population.
VPA doesn’t merely inform casinos which players to exclude from marketing. It also highlights specific players to target with increased incentives. These decisions can be made thanks to Player Budget, a new tool offered through Foundation that measures a player’s gambling budget by simply identifying their largest in-session out of pocket loss during any lifetime session.
To understand Player Budget, picture a player inserting a $100 bill into a slot machine. After 15 minutes of play, this player has just $20 in credits on the machine, before hitting a $50 win and cashing out a $70 ticket. Player Budget sets a benchmark for the player’s loss tolerance by identifying the lowest point in the session – $80 in this case – thereby highlighting the largest amount of money the player is willing to spend.
Casinos can use Player Budget to identify players with deeper pockets. While players with large gambling budgets are universally attractive on slots, video poker APs present a significant threat to the casino because of their ability to win large jackpots and inflict massive amounts of point liability.
By combining Player Budget with other data points such as the player’s skill level, visit frequency and session duration, casinos can use VPA to create a strategic matrix that offers far more accuracy and profitability than the old coin-in driven model. In this new VPA- and Player Budget-driven model, casinos can optimize a marketing strategy specific to each player and that player’s position on the customer relationship lifecycle.
Objective
Meet
Maintain
Exclude
Definition
Exceptionally attractive uncarded player
Enrolled player with additional profit potential
Enrolled player with consolidated frequency and loyalty
Unattractive member, such as AP or Problem Gambler
Action
Enroll into database
Enhance offers to maximize relationship value
The below chart shows how this new data-driven strategy can be applied to four real-life players from Casino A’s database. Using newly available data from VPA in the yellow-shaded columns, Casino A can see the true profit potential of each player and create highly efficient, data driven marketing strategies that only target players who bring profit to the casino.
Player B
Player C
$33,522.50
$158,701.25
$160,041.50
$67,236.50
Play Days
5
39
55
2
Theo Win
$670
$1,344
Skill Level
Action
Exclude
NOTE: Data in yellow columns only available through Foundation and not legacy systems
Players A and B are both APs who should be immediately excluded from the casino’s marketing offers. Deciding to exclude these players will likely push both to stop playing at Casino A and take their unprofitable play elsewhere.
Players C and D are both High Margin players, but further analysis of individual habits suggests a different marketing strategy should be deployed for each player.
Player C is one of the casino’s very best players with multiple visits each week, leading to a conclusion that C’s loyalty and spend are already maximized. Casino A should simply maintain its existing efforts related to Player C.
Player D is where the real new profit potential lies. Player D is a low-skilled video poker player with a demonstrated gambling budget of $5,019. With only two visits during the period, Casino A should place a high priority on generating more trips from Player D, who has a local address and is likely loyal to a competing casino. With full knowledge of Player D’s profit potential, Casino A can produce a more compelling marketing offer than its competition and ultimately flip the player’s loyalty.
VPA helps grow casino profits on video poker by identifying players according to skill value and profit potential. The table below summarizes how Casino A can grow its video poker profits by 46.77% simply by pinpointing marketing offers according to skill and budget instead of merely issuing blanket offers according to coin-in.
The largest impact to video poker profits – over 35% – comes from a combination of excluding and reducing unprofitable APs from Casino A’s operations. Further, by looking to drive High Margin player hands by 10%, Casino A can add a further 11.67% to their overall video poker profits.
Reduced Marketing to APs
Incremental HM Play
Net VPA Impact
Added Profit Margin (%)
25.64%
9.46%
11.67%
46.77%
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