SOLUTIONS · LOTTERY
Lottery analytics.
Every draw in context.
Connect game performance, sales channels and reporting on shared definitions.
Bring retail, online and mobile data into one analytical model. Compare sales and game performance across channels, and analyse player behaviour where a reliable player identity is available.
FROM DRAW RESULTS TO BUSINESS CONTEXT
Comparable numbers across channels with different data.
Lottery analytics examines ticket sales, draw and game performance, prize payments and channel activity. Gamblitude connects these measures for reporting and investigation. Retail transactions can support location and product analysis even when the purchaser is anonymous; individual retention and risk analysis require identifiable players.
Compare channels on the same basis.
Analyse sales by game, draw, channel, region and point of sale. Align reporting periods, cancellations and settlement rules before comparing results. Preserve the detail that makes each channel useful, while giving commercial and finance teams an agreed total they can trace to its sources.
Understand what shapes game performance.
Compare draw games and instant-win products using measures suited to their economics and cadence. Put sales changes alongside jackpot levels, calendar effects, distribution and campaigns. For identifiable digital players, add returning-player cohorts and product preferences.
Turn reporting changes into focused investigations.
Bring sales, prize-payment and settlement measures into Dashboards and Reports. Schedule a check by game, channel or location, or request a recurring portfolio report. Give operations the supporting records they need to investigate a change and decide what to review next.
The data foundation
A shared model that respects the identity available.
Connect channel data at its actual level of detail. A ticket or transaction identifier supports event investigation; it is not automatically a persistent player identity. Define how sales, cancellations, prizes and reporting periods reconcile before adding player-level measures.
Sources to connect
- Retail or partner sales and settlement records, with available point-of-sale and location identifiers
- Digital account, purchase and payment events where a reliable player identifier exists
- Game and draw catalogues, draw calendars, jackpot information and prize-payment records
- Campaign and CRM data for attributable activity, with explicit coverage limits
Metrics to define
- Ticket sales and sales value by game, draw, channel and location
- Cancellations, recorded prize payments and settlement differences under agreed rules
- Active points of sale, comparable-draw performance and game mix
- Purchase frequency and returning-player cohorts for identifiable players only
Example questions
Start with the question
behind the number.
Which games and regions explain the change in retail sales after accounting for the number of draws and active points of sale?
How did ticket sales change across comparable draws at different jackpot levels, and what else changed during those periods?
Among identifiable digital players, which first-purchase cohorts returned for a later draw or tried another product?
AI in the workflow
Ask. Follow. Investigate.
AI Agent
Investigate sales or game performance through governed definitions and the source dimensions available to your operation. Ask follow-ups to compare like-for-like periods.
Insight Radar
Specify a recurring comparison, including its scope and reporting period. Radar investigates the defined question and brings the result back with context.
Autonomous Agent
The agent chooses investigative threads in connected business data, including aggregate retail data where identities are absent. It presents related observations and evidence; your team decides how to respond.
Explore the products
The capabilities
behind the workflow.
Data Warehouse
Connect operational sources and agree the data model behind your analysis.
Business Intelligence Platform
Explore performance through Dashboards, Master Chart, Segments, Reports and Targets.
AI for iGaming
Ask questions, schedule recurring investigations and commission reports.
Autonomous Agent
Review independently initiated investigations and follow the evidence into chat.
Predictive Models
Explore available player-level models and discuss their fit with your data and objectives.
Questions & answers
Lottery analytics
explained.
What is lottery analytics?
Lottery analytics connects ticket sales, game and draw performance, prize payments and channel activity. It helps teams compare products and locations, explain changes and prepare reports from consistent definitions. Player-level analysis is an additional layer where a reliable account or other agreed player identifier exists.
Can retail, online and mobile data be analysed together?
Yes, where the relevant source records are connected and mapped to shared definitions. Analysis can compare channels by game, period and region while preserving their different levels of detail. The integration scope and available identifiers determine how far those comparisons can go.
Can you profile someone from an anonymous paper ticket?
No. An anonymous ticket or cash transaction does not establish a persistent player identity. Such records can support aggregate sales, location and product analysis. Individual retention, cross-channel journeys or responsible-gaming signals require an identifiable player and suitable linked history.
How can a lottery use Insight Radar?
Define a question and schedule, such as reviewing sales deviations by location each morning after the reporting cycle. Insight Radar repeats the investigation using connected data and governed Metrics, then brings the findings to your team for review.
How can we scope lottery forecasting or predictive models?
Start with the forecast you need, the available history, draw schedule and business objective. Draw-sales and jackpot-rollover forecasts require a separate assessment. Player-level models may fit identifiable lottery accounts where the target and data support them. Predictive Models are scoped and quoted separately.
Can the platform support management and regulatory reporting?
Reports assemble connected data, shared KPIs, charts and commentary for management or external reporting. Agree the required coverage and formats during implementation. Your team checks the figures, confirms applicable submission requirements and approves the final report.
See Gamblitude in action
Make the next draw easier to explain.
Bring a channel, game or reporting question. We’ll demonstrate how the available data and shared definitions support the investigation, then discuss your retail and digital sources.