AI Data Helper: ask your iGaming data.
Built for growing operators.
Stop waiting on analysts or building SQL queries. Ask questions like “Which players deposited over €500 last month but haven’t logged in this week?” and get instant, actionable answers from your live CRM data.
- Launch in 1 week
- Free data migration
- Dedicated support manager
WHAT IS A NATURAL LANGUAGE DATA ASSISTANT IN iGAMING CRM?
A natural language data assistant is an AI-powered interface that lets CRM managers ask questions about their player data using everyday language, and receive instant, structured answers without writing SQL queries, building reports or waiting for an analyst.
In most iGaming operations, the data exists: deposits, sessions, campaign performance, segment sizes, churn patterns. But accessing it requires either technical skills or a request to the data team. The CRM manager has a question at 3pm. The analyst queue returns an answer by Thursday. By then, the campaign window has closed.
Natural language analytics removes this bottleneck entirely. Instead of navigating dashboards, filtering tables or writing database queries, the CRM manager types a question in plain English. The system translates it into a data query, runs it against the live CRM database and returns the answer in seconds.
This is not a chatbot that generates generic advice. It is a data interface connected directly to the operator’s own player data, returning real numbers based on real activity.
- 01
Ask
“How many first-time depositors did we have last week?”, typed the way you would ask a colleague.
- 02
Query
The AI interprets the question and runs it against live CRM data: deposits, sessions, segments, campaign results.
- 03
Answer
A structured answer in seconds: a count with a daily breakdown, a filtered player list or a per-channel comparison.
- 04
Act
Save the result as a segment, open the winning campaign or start a flow, without leaving the CRM.
INSTANT ANSWERS FROM YOUR LIVE PLAYER DATA.
The core value of AI Data Helper is speed: the time between a CRM manager’s question and a usable answer drops from hours or days to seconds.
Instead of opening a BI dashboard, applying filters, exporting a CSV and building a pivot table, the CRM manager types a question directly inside InTarget:
- “How many players deposited for the first time last week?”: Returns a count with a daily breakdown.
- “How did last Tuesday’s cashback email perform compared to the previous week?”: Returns opens, clicks, conversions and attributed revenue side by side.
- “Show me players who were active in January but haven’t logged in since February 1st”: Returns a filtered player list that can be used for a re-engagement campaign.
- “What is the conversion rate of our welcome series by channel?”: Returns a per-channel breakdown (email vs. SMS vs. push) with deposit attribution.
These are not pre-built reports. They are ad-hoc queries against live data, interpreted by the AI and executed in real time. The CRM manager asks what they need to know and the system figures out how to retrieve it.
Every question that previously required an analyst, a SQL query or a BI tool can now be answered by the person who actually needs the answer: the CRM manager running the campaigns.
FROM INSIGHT TO ACTION. WITHOUT LEAVING THE CRM.
Most analytics tools stop at the answer. You get a number, a chart or a table, and then you switch to a different tool to do something about it. Export the list. Build the segment. Create the campaign. This context-switching is where insight dies.
AI Data Helper is built into InTarget’s CRM, which means the path from question to action is seamless:
- Ask, then segment: “Show me VIP players who haven’t deposited in 14 days”, then save this result as a dynamic segment directly, without re-creating the filter logic in the segmentation tool.
- Ask, then review the campaign: “Which reactivation email had the highest deposit conversion this month?”, then navigate to that campaign to clone it or adjust the next iteration.
- Ask, then prioritize: “Which player segment has the highest churn risk right now?”, then check whether that segment has an active automation flow, and create one if it does not.
- Ask, then validate: “Did our Monday free-spins campaign actually drive more deposits than last Monday’s?”, then decide whether to continue, adjust or stop the campaign based on actual revenue data.
Because AI Data Helper works on the same data layer as segmentation, automation and campaign execution, there is no gap between understanding the data and acting on it. The CRM manager does not need to translate an insight from one tool into an action in another. It all happens within the same interface.
BUILT FOR CRM TEAMS. NOT DATA SCIENTISTS.
Enterprise analytics tools are powerful, but they are built for analysts. Tableau requires training. Looker requires data modeling. Even “self-service” BI tools assume the user knows what table to query, what dimension to filter and how to interpret a pivot chart.
CRM managers in iGaming do not operate this way. They think in terms of players, campaigns, deposits and lifecycle stages. Their questions are practical and operational:
- “Is our VIP segment growing or shrinking this month?”
- “What percentage of players from the January cohort are still active?”
- “How much revenue did the sports pre-match SMS campaign generate last weekend?”
- “Which game category drives the highest deposit frequency among new players?”
AI Data Helper speaks this language. It does not require the CRM manager to know the database schema, select the right dashboard or understand query syntax. The manager asks a question the way they would ask a colleague, and the system returns a clear, structured answer.
This is particularly important for growing operators with small CRM teams. When there is no dedicated data analyst, the CRM manager is the analyst. The result: faster campaign iteration, better-informed decisions and a CRM team that operates at the speed of the data, not the speed of the analyst queue.
TRADITIONAL BI WORKFLOW VS. AI DATA HELPER.
Most operators rely on dashboards, analyst requests or SQL queries to answer CRM questions. Here is how the traditional approach compares to natural language data access inside the CRM.
| Aspect | Traditional BI or analyst workflow | AI Data Helper (InTarget) |
|---|---|---|
| Who asks the question | CRM manager submits a request to the data team | CRM manager asks directly in plain English |
| Time to answer | Hours to days (analyst queue) | Seconds, with real-time query execution |
| Skills required | SQL, BI tool proficiency or analyst dependency | None, natural language input |
| Data freshness | Last export or scheduled dashboard refresh | Live CRM data: deposits, sessions, campaigns in real time |
| Follow-up questions | A new request in the queue | Instant, ask the next question immediately |
| Action on insight | Export, switch tool, rebuild segment, launch campaign | Insight and action happen in the same CRM interface |
| Cost | BI tool license plus analyst salary or hours | Included in InTarget, no extra tools or headcount |
Ask your first question in a personal demo.
Book a demoMADE FOR EVERY KIND OF OPERATOR.
InTarget works with all kinds of iGaming businesses. The same assistant, answering the questions that matter to your vertical.
Which game category converts.
Ask which game category drives the highest deposit frequency among new players, then build the segment.
Weekend campaigns, in numbers.
Ask how much revenue the pre-match SMS generated last weekend and compare it with the previous round.
Cohorts that keep playing.
Ask what percentage of the January cohort is still active and which reminder brought them back.
VIPs, watched daily.
Ask whether the VIP segment is growing or shrinking this month and who has not deposited in 14 days.
QUESTIONS, ANSWERED.
What operators ask before typing their first question. For anything else, book a demo and ask the team.
Book a demoWhat is AI Data Helper?
AI Data Helper is InTarget’s built-in natural language analytics interface. It lets CRM managers ask questions about player data, campaign performance and segment composition in plain English, and get instant answers from live CRM data, without SQL, BI tools or analyst involvement.
What kind of questions can I ask?
Any question about your CRM data. Examples: “How many first-time depositors did we have last week?”, “Which campaign drove the most revenue this month?”, “What’s the average deposit amount for sports bettors vs. casino players?” The system interprets the question and queries your live data.
Does AI Data Helper use my actual player data?
Yes. AI Data Helper queries the same live data that powers InTarget’s segmentation, automation and campaign tools. It returns real numbers from your actual player base, not industry benchmarks or generic estimates.
Do I need technical skills to use it?
No. The interface is designed for CRM managers without technical backgrounds. You type a question in plain English: no SQL, no query builders, no BI tool training required. If the system needs clarification, it asks a follow-up question rather than returning an error.
How is this different from a dashboard?
Dashboards answer pre-defined questions, the ones someone anticipated when building the report. AI Data Helper answers any question on demand, including ones that were never built into a dashboard. It is ad-hoc, conversational and returns answers from live data in seconds.
Is AI Data Helper included in InTarget, or is it a separate product?
AI Data Helper is included as a built-in feature of the InTarget platform. There is no separate license, no additional BI tool and no extra cost. It is available to all InTarget operators as part of the CRM.
GROW.
Join 100+ operators who trust InTarget to grow their player base. Ask AI Data Helper about your own players, campaigns and revenue in a personal demo.