A player’s value can be boiled down to two functions. First is marginalization, i.e. the relationship between spending patterns and profit. How much revenue can I expect given a player’s spending patterns? What and how much cost should be attributed to such patterns? Second, the prediction, i.e. the forecast of a player’s spending patterns. What spending patterns can I expect from a player’s future visit? How many future visits can I expect? What factors influence spending patterns and visit frequency?
Customer valuation affects many parts of the organization. For the revenue manager, gaming value is the primary input to yield hotels rooms in a casino. Players are segmented based on their value, and the revenue managers forecast demand and recommend rates/COMPs at the segment level. Thus, an imprecise valuation methodology, e.g. one that overvalues players by 12%, will directly affect their pricing decision. Customer valuation also impacts marketing decisions, e.g. promotional offers, player development as well as financial ones since the value of the player database is a financial asset.
Customer valuation for a casino is particular for two reasons. Randomness make marginalization harder. Players mostly control how much and how they wager while randomness mostly drives how much they win. The cost side is driven by the redemption of COMPs and free play. As a result, identical wagering patterns can result in highly variable profit margins.
On the other hand, great, vast data makes prediction easier. Spending patterns of each visit are dutifully recorded and stored. A significant proportion of players are repeat or frequent customers and historical patterns are a good indicator of future visits and spend.
The win measures the actual gaming revenue. It lines up with the gaming revenue listed in the financial reports. In a sense, it measures the revenue from what was wagered by the player. The win is an unfair metric: when using the average win over the last 5 trips to forecast the value, 20% of the players had a negative value (18K out of 90K) and 69% of them had a positive win on the next trip (13K out of 18K). Plus its high volatility makes it particularly unsuited for forecasting. The theoretical win estimates the expected gaming revenue. It estimates the revenue from what was wagered by the player if we removed all randomness from the games
The theoretical win is usually underestimated. Players with limited bankroll can get unlucky and lose it all before having the opportunity to generate a significant amount of theoretical win. The worth refers to theoretical win once it has been adjusted. It is commonly defined on a trip basis as the maximum of the theoretical win — 40% of win, 40% being the worth coefficient. The net worth is an estimation of gaming profit and accounts for taxes, free play and COMP dollars.
Most players are repeat customers in frequency markets. Their past spending behavior is a good indicator of future one. The worth for a future trip is generally forecasted using the Average Daily Worth (ADW) over the last 12 months: Value = sum [Net worth over 12M] / sum [gaming days over 12M].
