Measuring Reinvestment
As we continue to explore Coin In, the most fundamental measurement in gaming, it becomes apparent that there is no single measurement that can answer all questions. In a strange way this means that all measurements are right in the correct context.
One of the most tricky areas to cover is the reinvestment of freeplay. I have some examples that are worth considering.
Example Number One: Winners and Losers
Lets consider a large group of players who more than 50% of are now spending their own money (after using up their freeplay), lets take this example a little further and say they were all playing the same kind of game with the same theoretical hold. Is it correct to say that the players who are spending their own money should be measured in different way to those who are still spending the winnings from the free play, even though the only difference is the random influence of the game.
Example Number Two: Cashouters
A second example is to consider people who cash out the free play as soon as it is played through. At this point do we consider the money they place back into the game to be their own or a continuation of the free play. Moreover if they wait a day or two before they continue to game do we consider that to be a continuation of free play.
It gets even more interesting when one compares the cashouters to the players who leave the money in the game, is there a difference?
These example freeplay questions have twisted the minds of analysts for years and with more variations they will twist us more. To resolve these questions I tend to look at players over longer periods and focus on building profitability models rather than play models.
Next I will take a look at the age old Theo Win.
Andrew Cardno
Comments
Freeplay Analysis and Yield Management
I have seen dramatic results from managing critical marketing activity associated with Freeplay. Unlike other promotional offers (such as hotel rooms) we are giving away “cash” so freeplay lends itself to optimization and this optimization falls directly to the bottom line.
Around Freeplay there are two fundamental questions, the first question is does the freeplay displace a cash player from their gaming choice, in other words does it cause a yield management issue and secondly does it replace the players actual cash spend.
The question of freeplay replacing cash spend is truly one of the great challenges to the database marketer. We can categorize the response to a freeplay offer into at least three buckets.
Bucket 1) Players who do not respond to freeplay offers, I have seen this happen if the offer is too low or the player is simply uninterested in more trips.
Bucket 2) Players who spend the freeplay and no more, this does happen especially if the freeplay amount if too great.
Bucket 3) Players who respond to the freeplay offer by giving an extra trip.
I have found that in building marketing programs it is critical to find these three groups of customers (in a deeper analysis there are other groups beyond the three also worth considering). I consider this one of the fundamental challenges in constructing a good marketing analytical strategy.
--- Andrew Cardno
Freeplay analysis
I look at the freeplay differently, depending on the purpose of the analysis. If we're trying to examine "game performance" (asking the question: "Is this a good game?" for example), then I consider the freeplay the same as cash. Our focus in these examples is to observe customer preference and produce comparative data points - Where the money originated is less important than looking at which games are selected, Avg Bet, etc.
When looking at high-level profitability, freeplay is discounted, but all subsequent winnings from freeplay are considered cash. The customer could walk away, but has made the decision to cycle the money.
To your point, we don't look at individual or micro-level play - we tend to look at the facility as a whole for a given period.
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