System admins see a read-only operational mirror, not one line more than the impact analyst. Both roles can read these aggregate, de-identified checks; only the Impact Analyst role can close a reporting period or export the aggregate pilot report. Holding the system admin role does not expose an individual profile.
Pilot figures & impact reporting
Three-month pilot · 1 April 2026 – 30 June 2026 · single arm, no control group. Everything below is aggregate and de-identified.
Three conditions to read before using any number on this page
- De-identified. Behaviour logs hold no identity. This page has no search by person, no profile button, and no path back to a particular account.
- Minimum group size of 10. Any group smaller than 10 people is hidden, even when the total is large enough, because a small group can be re-identified.
- Self-reported figures are an indicative proxy, not an objective measure. They must not be presented as evidence of effectiveness.
Quiz score, before and after
n = 41 matched pairsA separate knowledge assessment runs at the end of onboarding and again two weeks after each participant's baseline. Marked out of 20.
A difference of +3.8 points (+19 percentage points). That is a change within the same group. With no control group it cannot be stated as "the app increases knowledge" — practice effects from sitting the test twice and self-selection into the pilot have not been ruled out.
Broken down by age group
| Age group | n | Baseline | Endline | Change | Status |
|---|---|---|---|---|---|
| 18–24 | 18 | 10.8 | 15.0 | +4.2 | shown |
| 25–34 | 14 | 12.1 | 15.6 | +3.5 | shown |
| 35–44 | 6 | — | — | — | hidden · n < 10 |
| 45 and over | 3 | — | — | — | hidden · n < 10 |
The 9 people in the two hidden groups are still counted in the total of 41 above; only the breakdown is withheld.
Retention at day 1, 7 and 30
base sample 64Retention is not a success measure for this product and must not be treated as one. For a tool about gambling harm, high retention can be a bad sign. Read it only alongside the self-reported survey and the risk-warning measure below. A report that leads with retention is reporting against the wrong goal.
Self-reported survey (opt-in)
INDICATIVE PROXYPeriodic questions about real-world gambling. Entirely voluntary; declining costs nothing and changes nothing about the experience. Stored de-identified.
The only permitted way to present this figure: "38 people reported the following", never "the app reduced real gambling for 39% of users". These are self-reported answers, subject to social desirability bias, collected from a group that chose to take part.
The "more than before" group is only n = 5, so it must not be broken down any further. As it stands this survey is not sufficient to conclude anything about a gateway effect; answering that question would take an independent study design.
Continued after a risk warning
main behavioural measureCounts how often the risk warning appeared and how often the player continued anyway after reading it. Measured by week of use, not by calendar week. 1,842 warnings shown in total.
A fall of 19 percentage points over six weeks is the most interesting signal in this period. But only about a third of the original sample is still there in week 6, so the fall may simply reflect that the people most easily drawn in left earlier. Do not read this chart as cause and effect.
Weeks 7 to 12 are not plotted because each week falls below the minimum group size of 10.
Choosing to learn, or choosing to reset
244 decision pointsRecorded at the turning points: losing an asset, reaching a critical state, and the end of a run.
The recovery draw is not live, so this period has no three-way comparison. The group that left without choosing is the one we most need to watch and the one we know least about — they are no longer in the sample to ask.
Support link taps
214 taps · 37 people| Where the link sits | Taps | Share |
|---|---|---|
| Critical state screen | 68 | 32% |
| End of run screen | 52 | 24% |
| Seven-day cooldown | 44 | 21% |
| Footer & support centre | 31 | 14% |
| AI companion | 19 | 9% |
This is a count and nothing more. We do not store who tapped, we do not store AI conversations, and nothing is passed to the helpline. A tap does not mean the person called or got help — it measures reach, not outcome.
Previous closed period · Q1 2026
Read-only aggregateThis system view exposes only the closed aggregate needed to check platform operations. Cohort interpretation, reporting and instrument review remain with the impact analyst.
| Measure | Q1 2026 | Q2 2026 | Comparison note |
|---|---|---|---|
| Enrolled | 12 | 64 | Q1 was an internal trial; Q2 is the pilot cohort |
| Matched assessment pairs | 0 | 41 | Q1 had no endline; no knowledge-gain claim |
| Day-30 retention | not measured | 31% | Operational signal, not a success measure |
| Continued after a risk warning | 71% | 68% | Descriptive only; no causal claim |
Small groups remain hidden at n < 10. No row can be opened into an individual account.
Reporting hand-off
This role can verify platform aggregates but cannot close the period or export the report. Those actions remain with the Impact Analyst and its limitations block.
The first period is only meant to establish a baseline, not to hit a threshold.
Limitations of this pilot — these travel with every external presentation
- Single arm, no control group. There is no comparison group that did not use the app, so no before-and-after change can be attributed to the product.
- Self-selected participants. People who enrolled may already care about gambling harm; the results do not generalise to the wider population.
- A sample of 64 over three months is too small to detect a real effect, especially within subgroups.
- Attrition. Only 41 of 64 have a complete baseline–endline pair; people who left early are barely measured at all. Later figures always lean toward those who stayed.
- The survey is self-reported and cannot be checked against real-world betting.
- Commitment to publish. Negative or neutral results are published in full. If we see a signal that people bet more after using the app, the agreed response is to pause or change the product and publish the finding.
What must not be done with this data: reading it back into one person's gameplay; using it to personalise odds, rewards or notifications; or implying that the product helps anyone win real bets.