Research Breakdown
How I Research Before I Build
My research process combines competitor analysis, user pain synthesis, and personal trading experience. Every product decision I make is grounded in at least two of these three inputs — usually all three.
Competitor Analysis
Crypto Charting App Landscape
Analyzed 8 top crypto charting apps across App Store and Play Store. Evaluated each on: mobile UX quality, automated analysis capabilities, macro context, pricing model, alert system, and retention mechanics. Ran each app for 2+ weeks as an active trader.
Key insight: Every major player treats mobile as a "viewer" — we treated it as a primary trading surface. That's the entire positioning gap.
Retail Trader Pain Points
Synthesized user feedback from competitor app store reviews (2,000+ reviews analyzed), community discussions on crypto trading forums, and my own 5+ years of trading pain. Identified 5 recurring pain points that no existing product solved simultaneously.
Most apps solve charting. We solved decision-making. That's a different product category entirely.
The Underserved Segment
TradingView dominates desktop. Delta dominates portfolio tracking. CoinGecko dominates price discovery. The gap: a mobile-first tool combining automated technical analysis + narrative context for the retail trader who can't afford institutional data feeds.
Build for the trader who doesn't have a Bloomberg terminal. That's the entire addressable market TradingView ignores on mobile.
What Reviews Revealed
Competitor 1-star reviews are the most honest product research available. Top complaints across all major competitors: "too slow on mobile," "too many false signals," "no context for why patterns matter," "notifications are useless." All four became Chart AI specs.
Your competitor's 1-star reviews are your product roadmap. I read 500+ before writing a single spec.
Competitor Feature Matrix
| Feature |
Chart AI (Us) |
TradingView Mobile |
Coinigy |
Delta App |
CoinStats |
| Automated Pattern Detection | ✓ Yes | ✗ No | ✗ No | ✗ No | ✗ No |
| Mobile-First UX | ✓ Native | ~ Adapted | ✗ Poor | ✓ Yes | ✓ Yes |
| Narrative / Macro Context | ✓ Built-in | ✗ No | ✗ No | ✗ No | ✗ No |
| AI Analysis Layer | ✓ Yes | ~ Premium only | ✗ No | ✗ No | ✗ No |
| Confidence Scoring | ✓ Yes | ✗ No | ✗ No | ✗ No | ✗ No |
| Free Tier Value | ✓ High | ~ Limited | ✗ Paid gate | ✓ Good | ✓ Good |
| Domain Expert-Built | ✓ Trader-led | ~ Mixed | ✗ Tech-led | ✗ Tech-led | ✗ Tech-led |
My Research Framework
How I build my research process for every product decision
1
Personal trading validation first — I test the problem myself in live market conditions before talking to any external source. This prevents confirmation bias from user research.
2
Competitor 1-star reviews — Genuine, unfiltered pain. I read hundreds per competitor. Patterns emerge in under 50 reviews if a problem is real.
3
Community signal (Reddit, Telegram, CT) — Real traders talking about real problems in real time. Not a survey — organic conversation.
4
Feature gap matrix — Map competitor feature sets. The empty cells are the product opportunity.
5
The "would I pay for this today?" test — If I, as an active trader, wouldn't pay for this feature right now during a live trade, it doesn't make the roadmap.
Key Research Insights (Applied)
Market Structure
The Mobile-Desktop Gap in Crypto Tools
TradingView's desktop product is excellent. Their mobile app is an adaptation — not a redesign. This creates a consistent pain: traders on mobile get a degraded version of a desktop tool, not a tool designed for mobile trading behaviour. Mobile users check charts in short bursts, need faster signal delivery, and tap — they don't click and drag.
Product application: Chart AI was designed mobile-first. Every interaction was validated with one thumb. Pattern detection delivers signal in one tap, not three.
User Behaviour
Traders Seek Signals, Not Charts
The insight that changed everything: most retail traders don't want more chart types — they want to know what the chart is telling them right now. The mental model shift from "charting tool" to "decision support tool" unlocked the entire product direction.
Product application: Removed 4 chart types from v2 roadmap. Added pattern detection, confidence scoring, and macro context instead. Retention lifted.
Retention Mechanics
Why Crypto Apps Lose Users in 7 Days
Competitor data shows 60-70% of crypto app users churn within 7 days. Common cause: users open the app, see charts they don't know how to interpret, and leave. The app solved no job-to-be-done for them beyond "see price." Chart AI's job: give users a reason to open the app every morning — and understand what they're looking at.
Product application: Pattern detection + daily briefing concept emerged from this insight. Something new to see every time you open the app.
Pricing Research
The Free Tier Problem in Crypto Apps
Most crypto charting apps gate core value behind a paywall immediately — before users have had a chance to build a habit. This is backwards. Free tier value should be high enough to form a daily habit; premium tier unlocks deeper features once the habit exists.
Product application: Chart AI's free tier includes pattern detection and core analysis. Premium gates advanced customization — not core utility.