🌐 Challenge: Launch more effective paid acquisition campaigns for high-LTV & high-value traders by identifying trending instruments across competitor brokers, segmented by trader experience, to improve targeting, messaging, and conquest efficiency. Also, identify how high-churn, high-risk traders could be coached for higher LTV & retention on the platform.
👀 Task: Use real-time trading behaviour, segmented by experience, account size, and instrument, to uncover what beginner and experienced traders are doing, and how recently, to inform instant & ongoing targeting and content development.
🎯 Problem
The acquisition team lacked clarity on what experienced, wealthy & highly frequent traders were doing across the competitive landscape. Without segment-level behavioural visibility, their campaigns relied on generic messaging, leading to their current situation of low-frequency and smaller account sized traders making up 54% of all users, their campaigns wasted CAC and led to high-churn.
🧠 Solution
TFE’s behavioural intelligence layer helped the platform:
- Identify which instruments were trending by experience level
- Detect high-risk & high-value segments based on account size, behaviour (timeframe & risk appetite)(e.g. aggressive exposure among new traders)
- Segment users into actionable clusters for targeting (e.g. Overexposed Rookies, Trend Followers, Selective Veterans)
- Deploy targeted messaging and creative aligned to real-time trading behaviour
💡Key Results
📉−28% reduction in cost-per-funded-account
after launching behaviour-based campaign targeting
🎯+12% increase in funded accounts
from higher account sizes who traded more frequently vs previously underperforming in generic campaigns
🧲+41% lift in CTR
on creative aligned to specific instruments (e.g. gold, EUR/USD, oil)
📈3-week campaign sprint
informed by real-time TFE segmentation and instrument trends
🔍 Initial Assumptions
The team hypothesised that:
- Traders with high lot sizes in volatile instruments (e.g. oil, USD/CAD, XAU/USD) were either overexposed or trend-following and would lead to high-churn
- Newer traders were more likely to be swayed by market narratives, and thus needed messaging that addressed risk and control
- Experienced traders would respond to more precise or directional content (sentiment alignment, timing windows, etc.)
📊 Data Required
In order to calculate and validate high-value switchers, we use the following data signals:
Area to Assess | Rationale | Combination Variables |
Experience Level | Strategy quality and trade durability differ by tier | All for Low experience level |
Instrument Popularity | Identifies content themes & trending narratives | Instrument + experience tier |
Lot Size Patterns | Indicates risk appetite and intent | Lot size + experience tier |
Trade Recency | Content/campaign needs must follow real-time behaviour | Time of last trade + instrument volume |
Broker Movement | Targets conquesting based on quality flows | From-broker + instrument overlap |
Data Transparency Note
This case study uses partial, illustrative data extracted from the TFE platform to demonstrate how experience-level and instrument-based trading behaviour can be used to drive strategy.
We’ve intentionally omitted all provider-identifiable intelligence and broker-level signals in this public-facing version. Full access is available via the live TFE platform at TFE.ai.
🧪 How They Did It
The growth team used TFE’s API to:
- Pull real-time instrument usage data segmented by experience level
- Calculate average lot size per asset, exposing speculative patterns in key instruments
- Cluster user types based on directional bias (buy vs sell), lot size, and instrument overlap
- Feed these segments into a multi-touch campaign across PPC, paid social, and CRM
Experience Level Across Instruments & Lot Size
🔹Insight: Average Lot Size By Experience Level
🧠 Insights:
- Traders with 3–4 years of experience had the highest average lot size (20.56), suggesting growing confidence or risk tolerance.
- In contrast, newer traders (1–2 years) showed a moderate average (6.46), while the 5+ years group had the smallest lot sizes (2.96) — potentially signalling tighter risk control.
- The trend suggests that while intermediate traders increase position size, the most experienced may trade more selectively or hedge risk more precisely.
🔹 Insight 2: Recent Instrument Choice by Low Experience Level
🧠 Insights:
- XAU/USD (Gold) and EUR/USD dominate with 38.16% and 35.75% of trades respectively among inexperienced traders — indicating risk appetite skewed toward volatility and familiarity.
- Crypto (BTC/USD) and exotic FX pairs (e.g. GBP/JPY, USD/JPY) also appear, reinforcing a possible pattern of speculative overconfidence in low-experience cohorts.
- These choices offer key opportunities for content targeting (e.g., gold volatility explainers, EUR/USD trade timing guides) and campaign conquesting.
Instrument Choice by Lot Size & Direction
🔹 Insight 2: Instrument Choice by Direction
🧠 Insights:
- Buy orders dominated among low-experience traders (63% vs. 37%), indicating trend-following or FOMO-based trading behaviour.
- Educational content that frames when not to buy or highlights profit-taking strategies could improve engagement and retention.
🔹 Insight 2: Average Lot Sizes for instrument Choice
🧠 Insights:
- The largest lot sizes were concentrated in:
- USD/CAD (13.69)
- GBP/USD (13.25)
- OIL (13.25)
- These patterns suggest aggressive exposure in macro-driven instruments among less experienced users.
- Conquesting content should prioritise volatility management and instrument-specific risk framing.
🔎 What They Saw
- XAU/USD and EUR/USD dominated novice trader volume cross platform - the team quickly pivoted to new article creation for new traders who seemingly fell into this bucket for quick retention wins.
- Highest lot sizes came from USD/CAD, GBP/USD, and oil — instruments with macro/trend sensitivity
- Buy bias (63%) signalled FOMO-driven positioning in early-stage traders
- Veterans traded less frequently, with lower exposure and higher timing precision they did this across several providers wh0 we have omitted for privacy purposes.
⚡ Comparative Overview: Segment Breakdown
Segment Name | Avg Lot Size | Risk Score | Instrument Bias | Notes |
Overexposed Rookies | 13.2 | High | Oil, USD/CAD, GBP/USD | Aggressive lot sizing, low control |
Trend Followers | 6.4 | Medium | Gold, EUR/USD | Volume-led, pattern dependent |
Selective Vetrans | 2.9 | Low | GBP/USD, EUR/USD | Small size, high-timing precision |
👀 Final Signal: Competitive Instrument Movement
While we’ve removed broker-specific data, the directional trade activity, lot size concentration, and instrument trends point to clearly shifting trader intent. This insight layer can:
- Inform instrument-led conquest campaigns
- Support better risk-adjusted messaging
- Reveal real-time volatility opportunities
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✅ Outcome
To solve this challenge, the client used TFE’s API to:
- Pinpoint which instruments were trending among inexperienced traders for quick retention wins
- Map lot size patterns and behavioural signals by experience tier for acquisition opportunities by segment based on competitor strengths and weaknesses
- Identify buy/sell sentiment shifts across segments to inform timing and creative messaging to improve LTV and caution newer traders, and position to attract wealthier, higher frequency traders.
This enabled them to attract a wider group of traders with specific behavioural intent, reduce blanket targeting, and increase both funded account rates and engagement depth amongst key segments..
📉 Result:
They improved campaign relevance, cut wasted CAC, and increased first-time funded accounts from key segments by double digits.
📦 Get Full Access
With full access to TFE, you get:
- Real-time trader flow data segmented by instrument, experience tier, and lot size
- Access to the Trader Conquest Index — showing which assets and segments are heating up
- Directional trade bias data (buy vs. sell) across asset classes
- Cross-broker account size & lot size heatmaps to identify overexposure or risk clusters
- API and dashboard access for your growth, content, and product teams
- Embedded chart and signal modules to support campaign planning or content strategy
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