AI That Drives Real Trading Revenue

How a U.S. brokerage partner turned WNSTN’s AI assistant into a measurable revenue engine, with 2.3× more trades and higher retention.

Sample: 88,000+ trades

Period: 12 months

Control group: non-WNSTN users

Revenue impact: 8.4%

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EXECUTIVE SUMMARY

Conversational engagement, converted into trades 

A U.S. brokerage partner deployed WNSTN’s AI assistant across its trading platform. Users who engaged the assistant traded significantly more, converted conversations into action within 24 hours, and showed stronger retention. This translated into an estimated 8.4% contribution to the partner’s trading revenue at a 95% confidence level.

THE CHALLENGE

Engagement that a commoditized market can’t deliver

The brokerage faced a common industry problem: how to re-engage dormant users, increase trading frequency, and improve client lifetime value without adding headcount or operational complexity.

Declining trading activity among inactive users

Difficulty retaining clients in a commoditized market

No scalable way to deliver personalized engagement on demand

DATA & METHODOLOGY

How we measured it

The findings are built on raw, anonymized data provided by the broker, not survey responses or projections. The analysis was designed around a clean control group and conservative assumptions.

Sample & period

Over 88,000 trades executed by end users, collected across an engagement of approximately one year.

Dataset

Anonymized broker and WNSTN user IDs (UUIDs), including users who never used WNSTN, enabling a clear control group.

Quality scoring

Offline LLM-as-a-Judge annotations scored conversation complexity, interaction type, and answer quality.

Three mutually exclusive user segments

Non-WNSTN users

Did not interact with WNSTN (the control group).

Non-active WNSTN users

Interacted with WNSTN exactly once.

Active WNSTN users

Interacted with WNSTN more than once.

All users were analyzed, regardless of WNSTN usage.

Outliers and super-engaged users were removed to normalize results and avoid overstating impact.

Revenue simulations used observed usage uplift and conservative growth assumptions (2.5% / 5% / 7.5% monthly adoption growth).

Conversion rates from non-active to active users were benchmarked against other WNSTN clients.

All users were analyzed, regardless of WNSTN usage.

Outliers and super-engaged users were removed to normalize results and avoid overstating impact.

Revenue simulations used observed usage uplift and conservative growth assumptions (2.5% / 5% / 7.5% monthly adoption growth).

Conversion rates from non-active to active users were benchmarked against other WNSTN clients.

RESULTS SNAPSHOT

Outcomes observed during the engagement

Key outcomes observed during the partner engagement period.

2.3×

Trading Activity

Active WNSTN users traded 2.3× more than non-users (median uplift in trading volume).

53.1%

Conversation-to-Trade

Of users who chatted with WNSTN, 53.1% executed a trade within 24 hours.

8.4%

Revenue Contribution

Estimated long-term trading-revenue contribution at a 95% confidence level.

ENGAGEMENT METRICS

High-intent, decision-oriented usage

Transaction volume (trades / user / day)

WNSTN users, mean

0.89

Non-WNSTN, mean

0.51

WNSTN users, median

0.62

Non-WNSTN, median

0.27

WNSTN users show materially higher average and median trading volumes.

CONVERSATION INSIGHTS

What users actually talked about

Conversation complexity

Advanced

78.1%

Medium

14.7%

Simple

7.2%

Top conversation categories

Deep research

42.8%

Data snapshots

30.2%

Guided learning

12.0%

Quick insights

8.5%

Tier-1 support

4.1%

Quality scoring

(LLM-as-a-Judge, 1–10)

Compliance level

9.96

Answer relevance

9.07

Engagement

8.75

Satisfaction

8.36

REVENUE SIMULATION

Modeled impact over 12 months

We projected 12-month trading revenue impact after full WNSTN deployment using a 2.3× median uplift and conservative adoption growth assumptions (2.5% / 5% / 7.5%).

4%

Bear Case

Conservative adoption growth (2.5%/mo).

8.4%

Base Case

Central estimate at 95% confidence (5%/mo).

14%

Bull Case

Faster adoption growth
(7.5%/mo).

WHAT THIS MEANS

A revenue engine, not just an engagement tool

Engagement influences trading

The correlation between WNSTN usage and trading volume is clear and consistent: users who interact with the assistant don’t just browse, they act.

Higher engagement, higher value

Traders who used the assistant more frequently showed stronger retention and LTV signals, suggesting AI drives long-term platform stickiness

Measurable, not theoretical

With an estimated 8.4% trading-revenue contribution at 95% confidence, this is a quantifiable investment in platform
growth.

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