My January column, “Isn’t It Time for Change?” drew a number of comments. Virtually every comment agreed that change is necessary, but several also asked for specific examples of how to implement such change. From now on, I’ll offer more ideas, starting with this discussion about the myth of win/day.
New game selection is usually based on how much money that game earns in comparison to house average. Those that perform far above house average are eagerly acquired, while a game that performs at, or below, that average is rejected. I contend that such a selection process is shortsighted and harms profits. Here’s why:
Suppose you install five brand-new “Lucky Charm” games, and customers are fighting each other to play them. Over a period of 60 days, they earn 5X house average. In this scenario, Lucky Charm is a revenue-share game and the provider takes 20 percent of revenues, leaving an apparently still-healthy net performance of 4X average.
You also install five brand-new “Pot of Silver” games, but most players won’t go near them. The play mechanics are just too different for them to accept. Over that same 60 days, this game earns only half house average. Which game do you keep?
Most managers would choose the high-earning game. That might be a mistake though, if the goal is maximum profit. To achieve that, you must choose games that raise revenue, not ones that simply move it around. A technique I call Player-Based Analysis can help you build the highest-earning floor possible.
First, identify the customers who played the game being evaluated. (Your player tracking system should be able to provide this). Then look at each player’s history to determine if her average spend per visit, or frequency of visit, has increased since she began playing the new game. In other words, is the new game generating new revenue?
Of course, single comparisons aren’t meaningful. An individual player might have left the casino earlier than she wanted and cut her spending short. By comparing many hundreds, or even thousands, of player records though, you’ll get an accurate portrayal of where a game’s revenues originate.
Suppose Player-Based Analysis shows that Lucky Charm players were already frequent visitors, and they didn’t increase spend per visit or frequency of visit. In other words, Lucky Charm simply shifted play from games you own outright to a game that costs 20 percent of win. This game is actually reducing net win/day by the amount paid in revenue share. Instead of earning 4X house average, you incur a net loss of revenue by having the game on your floor.
Now consider Pot of Silver. Can a game that earns just half house average possibly be worth keeping? Player-Based Analysis confirms it attracted only a fraction of the players Lucky Charm did. But that small group is loyal to Pot of Silver and plays regularly. Their average spend per visit actually increased by 25 percent. That’s newfound revenue.
Just as importantly, the game attracts players who rarely visited your casino previously. In sum, you learn that Pot of Silver’s win/day is 90 percent incremental profit: money you’d lose if the game were removed.
Presume this casino’s house average is $175/day. Player-Based Analysis demonstrates that each Lucky Charm actually reduces net revenue by $175/day, while each Pot of Silver increases net revenue by $78.75. Now which game would you choose?
You might play “follow-the loser” and keep some Lucky Charm games to protect your players from weaker competitors that do offer them. At least now you know what it costs to do so. Better yet, use your newfound understanding of player segments and behaviors to create promotions aimed at those players at risk. Since you now know what other games they like, you can create a program that merchandises those games to specific players, thereby building better player relationships without foregoing profits.
Player-Based Analysis also helps you identify new marketing opportunities. You have an extensive database of player information that mostly sits underutilized. Pot of Silver appealed to a segment that was otherwise underserved. Search through your database for members with low play that share characteristics with Pot of Silver players and let them know this new game exists. Offer incentives for their visits and you’ll quickly build a broader base of players than you ever had before.
Casino gambling has always been about people, not machines and suppliers. By analyzing data by player behavior, preference and segmentation, instead of game titles and location, you’ll better meet player desires and increase play volume. SlotManager
John Acres is the founder and chief executive officer of Acres 4.0. He may be reached by e-mail at firstname.lastname@example.org.