Machine learning and AI
The reason travels with the score.
Six machine learning model families, each built to answer the question your commercial team most needs answered, and each returning the driver behind its answer. The category sells a score. You asked for the why.
Six model families. Each moves a line you already measure.
Player retention and churn
Moves Player lifetime, with one more month as the aim
Which players are about to go quiet, and what is driving it?
Finds the players who are drifting, with the reason attached, early enough for your team to act.
Player value and VIP identification
Moves Margin per player
Which players carry the margin, and which are being carried?
Shows which players carry the revenue and which cost more than they return, so each gets the treatment their value calls for. Includes segmentation and lifetime value prediction.
Abuse and revenue leakage
Moves Revenue lost to abuse
Where is revenue leaking to abuse, and who is behind it?
Spots abuse at player level across bonuses, payments and play, and links the accounts working together as a syndicate. Stop the leak before it shows in your revenue.
Responsible gambling
Moves Regulatory exposure
Which players show harm markers, and from what behaviour?
Surfaces harm markers from the same behavioural signal that drives retention, so your team can respond with care.
Affiliate scoring
Moves Affiliate return
Which affiliates send players who stay and pay, and which send players who cost you?
Scores every affiliate from 1 to 100 on the value and quality of the players they send, so commission follows the partners worth paying.
Recommendation
Moves Player lifetime and margin
What keeps this player playing, and paying, next?
Suggests the next game or offer for each player, drawn from the same reconciled data as the other five families.
A score is an instruction. A reason is a decision.
A churn score sends a CRM manager to a bonus. It arrives without the one thing that makes it actionable.
Every PlayAIO model returns the driver as well: for example, that the problem sits in one acquisition channel, in one game category, in the fortnight after one promotion. Your team can defend that to a board and act on it.
A score with its reason
Why
What one more month is worth.
The retention model sets out to extend the paying life of each player by one month. Put in your own figures to see the arithmetic.
One answer for each person in the room.
- CEO or MD
- More revenue from players already on the book. The retention figure, measured on your own numbers.
- COO or Head of Operations
- A month end that closes. One set of numbers, finance to marketing, reconciled before any model runs.
- Head of Data or BI
- A check you can test. The 99.9% figure, against your own system. Ingest, reconciliation and model upkeep come off your backlog, and PlayAIO anonymises your data to protect your players' personal information.
- CFO
- The reason back on the bonus invoice. Promotional spend becomes a line you can control, with the cause attached.
Where machine learning and AI meets the other two.
With market intelligence
Choose the model by what the market shows, so you build the one that addresses the real problem, not the one that sells fastest.
With reporting
Your reconciled numbers show where the money goes, such as revenue lost to abuse, so the first model is the one that stops the leak.
Which question should the first model answer?
Book a call and bring one. We will tell you what it would take.
Book 30 minutes with the team. No data needed and no commitment: just a discussion about your data.