Prefilling the bonus-code field with the two most recently redeemed codes reduced the median number of post-session-45 edits per player from 6.1 to 5.6, an 8.2% decline, in a controlled test across 14,300 accounts. The effect was measured over a 90-day window ending March 31, 2025, and held after controlling for deposit tier and device type. What follows examines where that reduction actually came from, what it did not fix, and why an 8% figure deserves more skepticism than the headline suggests.
What the intervention actually changed
The mechanic is modest. When a player returns to the cashier, the bonus-code field arrives pre-populated with the two codes most recently applied to that account, in reverse chronological order. The player can overwrite either, ignore both, or clear the field entirely. No code is auto-applied; the prefill is a suggestion, not an execution.
That distinction matters for how we read the result. The 8% reduction is not a reduction in edits per se — it is a reduction in session-45 edits, a specific behavioral marker defined in the test protocol as any manual change to the bonus-code field occurring after the 45th session on an account. The metric was chosen because it correlates with a cluster of downstream frictions: misapplied codes, support tickets about "wrong bonus," and abandoned deposits mid-flow.
The test population was split 50/50 between a control cohort (empty field, status quo) and a treatment cohort (two-code prefill). Assignment was by account, not by session, so a player stayed in one arm for the full 90 days. That avoids the contamination problem you get when the same user sees both experiences on alternating visits.
The baseline is doing a lot of work here
An 8.2% relative reduction sounds cleaner than it is. The absolute change is 0.5 edits per player over 90 days — roughly one edit every six months per account. If your baseline were 2 edits rather than 6.1, the same relative improvement would be far less interesting. The headline number is a function of a relatively high baseline, and baselines vary enormously by operator, jurisdiction, and promo calendar. A sportsbook running weekly reload codes will have a different edit profile than a casino with a static welcome offer.
Where the edits disappeared
The reduction was not evenly distributed. Three patterns emerged from the segment analysis.
By code type. Players redeeming the same recurring code across multiple sessions accounted for 61% of the total edit reduction. This makes intuitive sense: if your last two codes are the ones you keep reusing, prefill saves you retyping them. Players cycling through one-time promo codes saw a much smaller effect, roughly 2.4%.
By device. Mobile web users showed a 9.7% reduction; desktop users showed 6.1%. The gap is consistent with the input-friction hypothesis — thumb-typing a 10-character alphanumeric code on a phone is more error-prone than pasting on desktop, so removing that step yields more.
By tenure. Accounts older than 18 months saw almost no benefit (1.9%). Newer accounts, under six months, drove most of the aggregate figure at 13.4%. The interpretation the research team favored: experienced players have already developed workarounds — saved notes, password managers, muscle memory — that the prefill merely duplicates.
| Segment | Edit reduction |
|---|---|
| Recurring-code users | 61% of total reduction |
| One-time-code users | 2.4% |
| Mobile web | 9.7% |
| Desktop | 6.1% |
| Accounts < 6 months | 13.4% |
| Accounts > 18 months | 1.9% |
The tenure split is the most consequential finding and the one most likely to be buried in a press release. If your acquisition strategy skews toward new depositors, the intervention looks strong. If your book is mature and retention-weighted, you may be optimizing a problem your best customers solved for themselves two years ago.
The support-ticket question
The test was designed around edits, but the business case usually rests on support load. Here the results were softer. Tickets tagged "bonus code not applied" fell 4.1% in the treatment arm, well short of the 8% edit reduction and not statistically significant at the 95% level given the ticket volume (n = 1,840 across both arms).
That gap is worth sitting with. It suggests that a meaningful share of code-related support contacts are not caused by manual entry friction at all — they stem from eligibility mismatches, expired codes, or wagering-condition confusion that prefill does nothing to address. An operator reading the 8% headline and budgeting for an 8% support-cost reduction would be overcommitting.
There is also a countervailing risk the test did not fully capture. Prefilling a code the player is no longer eligible for could increase failed-redemption attempts, and failed redemptions are a worse experience than a blank field. The treatment arm showed a 0.9% uptick in redemption failures among players whose most recent code had expired within the test window, though the subsample was small enough (n = 410) that the confidence interval crosses zero.
A note on the responsible-gambling surface
Any change that reduces friction in the deposit-and-bonus flow deserves scrutiny beyond conversion metrics. Prefill makes it marginally easier to re-apply a deposit-match code without pausing to consider whether the player wants another matched deposit. The effect here is small — the intervention is a text suggestion, not a one-click deposit — but friction in the cashier is not purely a cost to be engineered away. Some of it functions as a natural circuit breaker. Operators testing prefill mechanics should track deposit frequency and average session deposit size in the treatment arm alongside the edit metric, and should be prepared to segment out players showing early markers of chasing behavior. The 90-day window in this test is too short to say anything meaningful about harm, and the study wasn't designed to.
What an 8% figure can and cannot support
The result is real and replicable within its parameters, but it is a narrow operational win dressed in a round-number headline. It applies to a specific metric, on a specific population, over a specific window, and it concentrates almost entirely among newer and mobile-heavy users redeeming recurring codes. For a mature operator with a desktop-skewed, long-tenure base, the expected effect is closer to 2% than 8%.
The open question is whether the edit metric was ever the right target. Edits are easy to instrument, which is why they get studied, but the player-facing problem is failed redemptions and unclear eligibility — and the prefill intervention moved neither convincingly. A more interesting test would prefill the field and surface a one-line eligibility status next to it, then measure redemption failures rather than keystrokes. If that combination moves support tickets by double digits, the conversation changes. If it doesn't, we may be looking at a well-measured solution to a problem that was never the expensive one.