On Trials & the “Drug-Dealer” Model of Hard Currency

If only he were around to experience hard currency today.

One of my favorite folk-lore design positions is the notion of giving away free hard currency. Without hesitation, every product manager I’ve worked with will off-handily assert the “drug-dealer” model. Supposedly, after experiencing the wonders of hard currency, players will be more likely to spend real money on hard currency rather than just enjoying the free stuff. While free giveaways sound nefarious, it’s a common strategy in nearly all walks of commercial life: streaming services, cars, software, and Costco all employ trial mechanics to no moral confusion or controversy.

Despite strong priors displayed by PMs, I’ve never seen empirical evidence supporting the claim. But more importantly, I’ve never seen the argument entirely fleshed out. For example, does the theory suppose I’d maximize conversation if I gave players $100 worth of hard currency? Or will a mere $1 of free hard currency suffice? Do I send players hard currency on a schedule, or is an initial allocation enough? In true fashion, we need a model!

There are three critical variables at play:

  1. The amount of free HC
  2. The rate of HC sourced
  3. What the HC can purchase

The Amount of Free HC

Consider a game economy with $500 in total spend. If we initially allocated $500 in hard currency to players, real money spend would drop to $0. Of course, no PM would suggest as much, but testing the endpoints helps form the shape of the optimality curve. Presumably, low levels of HC are needed to maximize conversion, but too much and conversion returns are negative. There is a global optimum somewhere in the early part of the curve; the amount of HC sourced here maximizes conversion.

If sourcing HC has a significant effect, we need to experiment to find just the right amount sourced – remember there’s a negative area to the curve. Do we know where we’re currently operating on the curve? 

Rate of HC Sourcing

Instead of an initial allocation, we could conceive of giving players hard currency on a fixed or variable schedule. After all, the theory claims that getting players into a “routine” is key to the conversion. Psychology research supposedly demonstrating “routines” are sticky but as a misreading of the evidence that lacks external validity. The research draws a sharp line disambiguating routines, habits, and rituals. But, turning to habit research, little of the work introduces friction into the habit formation process. In other words, how much do I need to pay players or persons to give up their habit?

Moving players from spending free currency to buying real currency presents significant “friction” into the habit; it’s not clear the habit is sticky enough to resist this. It’s as if proponents of the idea imagine a slingshot propelling users to buy to maintain the habit with the introduction of real-world payment. However, ever-present friction can decay the power of habits over time.

Furthermore, players and people can swap out parts of the habit supply chain. For example, if a Starbucks were to close down, a given person may switch to another local coffee shop to grab morning coffee rather than travel to a further away Starbucks location. Similarly, the habit HC sourcing creates is not spending HC but spending free HC. While I’m highly skeptical of the rate of HC sourcing positively affecting conversion, it’s still a plausible lever that stands the theory on its legs, falsification required.

How Compelling HC is to Spend

As we’ll see in the general model, HC spending resembles a free trial. A trial allows consumers to gather new evidence and convert it to complete the purchase of a product. More generally, a trial helps potential consumers solve an asymmetric information problem. Despite a given firm’s enthusiasm that everyone and anyone needs their product, consumers have a skepticism that this information may is accurate. But the actual product plays a significant role in determining the level of skepticism, with new items or product categories meeting the most skepticism. Telsa has famously claimed it has a test drive conversion rate of 10%, far above industry norms, and showcasing a wide variety of outcomes from trials. It follows that not every game would benefit similarly from a trial, and instead, effects localize to the type of product purchased with HC. We’ve generally observed cosmetic games to have lower ARPU and conversion, suggesting that they may benefit less from a trial than a gameplay-based game, i.e., Battlefield v. FIFA Ultimate Team.

Towards a General Model

I model a given trial \(LTV \) or \(LTV_t\) as a function of the expectation gap, \(EG_t\) subtracted from the satiation of the trial, \(S_t\).
$$LTV_t = EG_t – S_t$$
\(EG_t\) measures the trial’s ability to communicate valuable information about whether the consumer values the product above its cost. Again, we assume trials solve an asymmetric information gap between producers and consumers. \(EG_t\) can take on negative or positive values; it’s entirely possible consumers come away less likely to convert after partaking in a trial. Producers should actively guard against games that don’t “trial well” or showcasing the wrong type of content. Yes, some movie trailers hurt the movies they trial for. But even if producers choose the correct content, there’s a risk of satiating players with too much product. EA Play includes a 10-hour trial program: players have 10 hours to play any title, including new releases, at no additional charge. However, trial play is disabled for games like Jedi Fallen Order, a single-player linear storytelling game from Respawn. Gamesradar estimates the title takes 14-18 hours to complete; players could complete more than half the game with the EA Play trial alone. Large values of \(S_t\) turn the entire trial \(LTV_t\) negative and, producers should pull these from the market.


I’m unsure if free hard currency maximizes conversion, but I have seen firmly held free currency priors with little empirical evidence. If free currency trials are as powerful or influential as we think they are, they deserve to be poked and prodded for optimization.

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