A Player Reinvestment Conundrum

Jan 23, 2018

Robbing Peter to Pay Paul

It is no secret that without patrons, casinos would cease to exist. Casinos will go to great lengths to encourage patrons to visit and revisit their property. In all markets, but particularly in highly competitive markets, marketing spend to attract players can have a large impact on patron visits and property revenues.

One area of marketing spend can generally be referred to as player reinvestment. This can take on many forms such as comps (meals, rooms, etc), free play, match play, loss rebates, or tickets/invitations to special events. Reinvestment rates can vary by market and macroeconomic cycles and can range from 5% of revenue to 35% or higher.

Typical reinvestment strategies are based around ADT (average daily theoretical) or a variation of ADT. Reinvestment will try to match some percentage of ADT based on overall management strategy.

The theoretical revenue (theo) formula is fairly straightforward with:

Theo = Total Wagers * House Edge

Total Wagers in practice is calculated as:

Total Wagers = Average Bet * Rounds per Hour (RPH) * Time on Device (TOD)

Since table games operators typically don’t have actual metrics due to a limitation of data collection, an estimated House Edge (Est. HE) and game pace (Est. RPH) is used to calculate ADT:

ADT = Est Average Bet * Est RPH * TOD * Est HE

These estimates are necessary but come at a cost.Let’s use Blackjack (BJ) as an example. Our casino makes the following observations:

  • supervisors enter rated Avg Bet after observing a few hands
  • BJ tables typically deal 80 rounds per hour on average
  • average players play to an effective house edge of 1.75% considering the occasional mistake and side-bets offered

This means that a player sitting down at a $25 table, who plays for 4 hours with an average bet of $30 has an ADT of $168 ($30 * 80rph * 4hr * 1.75% edge).

A property with a reinvestment rate of 15% would try to offer this player $25.20 in reinvestment to entice them to return.

We mentioned these assumptions come at a cost. Let us consider two players, Peter and Paul.

  • Casual gambler
  • Comfortable income
  • Doesn’t take game too seriously

  • AEP (Avid Experienced Player)
  • Knows perfect basic strategy
  • Knows ADT formula

Both players play BJ on a $25 table and both play for 4 hours

  • Peter likes the action and finds an empty, or near empty table and plays 130 round per hour
  • Initially bets $30 per hand and is rated this way, but spreads his bets between $30 and $50, averaging $40 per hand
  • Makes some mistakes and makes a few side wagers; has an effective edge of 2.5%
  • His actual theo is $520, or 3x our basic ADT ($40*130rph*4hrs*2.5% edge)
  • Paul wants the slowest game possible
  • Knows a 6 person game gets 50rph and can be pushed down to 45 with some social engineering
  • Starts betting $30 for his rating, but drops his bet to $25 when the supervisor looks away
  • Plays perfect basic with edge of 0.75%, no side bets
  • His actual theo is $40.50, or ¼ of our basic ADT ($25*45rph*4hrs*0.75% edge)

After subtracting our $25.20 reinvestment in both Peter is worth more than 30X what Paul is worth to us. After considering gaming taxes and operating costs, Paul is actually costing us money!

The problem is that we are incentivizing both players in the same way to visit us again. We are essentially robbing Peter to pay Paul. Paul has every reason to come back, while Peter may be stolen away to another property who recognizes his value.

So What Can We Do?

There are a few simple steps which can help to close this gap:

  • Update average bets as frequently as you can throughout a session to keep that value as accurate as possible.
  • Use a speed scale based on occupancy. Many table management systems allow you to enter ‘slow’, ‘medium’, or ‘fast’ to give a more accurate pace, or better yet, have estimates for each occupancy.
  • Track main bets and side-bets separately. Again, many table management systems will allow separate entries for each of these, with associated house edge values for each.

We started out valuing each player’s ADT at $168. These simple changes will adjust our player ADT to over $300 for Peter, and less than $100 for Paul. It doesn’t completely correct the situation but goes a long way to valuing the players accurately and reinvesting accurately.

Looking forward, there are technologies on the horizon which may eliminate this concern entirely. RFID and video analytics solutions could provide accurate total wagers, real-time occupancy, and game pace information. Card reading solutions could calculate an exact edge per player based on an evaluation of mistakes and side-bet utilization.

Many of these solutions are yet to be proven in a real casino environment and could be expensive to implement, but the trends in technology are towards more power, more intelligence, and more cost-effective. When this happens, table games could start having the data accuracy taken for granted in the slots and video poker worlds. Until then, there are simple practices that operators can adopt today that would better allocate player reinvestment spend to the right players and properly incentivize them to come back.

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Paul Stephens

With over 10 years experience in data analytics, client success, and table optimization, Paul has worked with many casinos to enhance their table games operations. He earned his Honors Bachelors in Math, Actuarial Science at the renowned University of Waterloo.

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