BlumBuilt Spot Trading for casual
crypto users
and experienced
traders, reaching 50.8M MAU
at its peak
Blum is a crypto trading app with Spot Trading, Perpetual Trading, and Launchpad, scaled
to 95M+ users. I led the end-to-end design of Spot Trading, building the core trading flows,
UX architecture, and scalable design system for token trading on TON and Solana.
Context
Blum was planned from the start as a crypto trading app, but building a full trading product
takes time. To grow the audience before trading was ready, it launched first as a tap-to-earn product with an airdrop mechanic. This helped Blum attract millions of users and build early loyalty before the full trading app was ready. Spot Trading came next: the moment users
could move from earning rewards to actually buying and selling tokens.
Blum was planned from the start as a crypto trading app, but building a full trading product takes time. To grow the audience before trading was ready, it launched first as a tap-to-earn product with an airdrop mechanic.
This helped Blum attract millions of users and build early loyalty before the full trading app was ready. Spot Trading came next: the moment users could move from earning rewards to actually buying and selling tokens.
Problem
For business: Blum had attracted millions of users through tap-to-earn and airdrop
mechanics, but most of them were still engaged through rewards, not trading. After the
airdrop, Blum needed to keep this audience active and give them a new reason to stay
inside the product.
For users: After earning rewards and receiving tokens, users needed a clear next step:
a simple way to trade the tokens they received, discover new tokens, and buy or sell them
on TON and Solana without leaving Blum for another product.
Goal
of trading volume and fee revenue, and keep trading activity inside the Blum ecosystem.
My role
trading flows, user testing, a scalable design system, and handoff. I built the UX architecture
for a trading experience that worked for both casual crypto users and experienced traders
with very different ways of trading.
Benchmarking
I started with a competitive analysis of two types of products: CEX platforms (Bybit, OKX,
Binance, BringX) and on-chain tools (Axiom, GMGN, BullX, DEX Screener). I looked at how
they organize trading information, help users evaluate tokens, and serve both casual users
and experienced traders.
Blum vs. CEX and on-chain trading tools
Benchmarking insights
1. Most trading tools were built
for experienced traders
CEX platforms and on-chain tools gave users advanced market data, charts,
token metrics, order controls, volume, and liquidity. This worked well for traders
who understood the mechanics and knew what to check before making a trade.
2. Token discovery was mostly
built around lists and market data
Across the products I analyzed, discovery usually started with token lists, charts,
price changes, volume, and other market signals. These patterns helped users
compare tokens, but still left them to figure out what was worth buying.
3. Blum could make trading
easier to access
Blum allowed users to trade directly inside Telegram, with no registration and
no KYC. Instead of moving to a separate exchange or on-chain trading tool,
users could trade inside the app they already used every day.
First version: Fast Launch
core hypothesis: whether users would buy and sell tokens inside Blum. The hypothesis
was validated: users started trading, and the first version helped us find friction points,
gather feedback, and understand what to improve next.
Key problem in V1
The first version had one structural problem: the trading flow was built as a single interface
with no separation by user type. A casual user who just wanted to buy a token quickly and
an experienced trader who needed metrics, market context, and fast execution both landed
on the same screen.
Discovery
To understand how different users approached Spot Trading, I ran in-depth user interviews with
8 casual users and 5 experienced traders, then compared the findings with product metrics.
This revealed three key problems:
1. Casual users struggled with
basic trading terms
Too much information on the screen made them less confident before making a trade.
2. Discover didn't help users
figure out what to buy
Tokens looked similar, and users had little guidance on what was worth attention.
3. Experienced traders needed
a full trading workspace
Key metrics, charts, an order form, fast actions, and a clear view of open positions.
Strategy
The key insight — one trading interface could not serve both user types well.
The main design decision was to structure Spot Trading around two different trading experiences instead of one universal flow. Casual users needed a simple path to discover, buy, and sell tokens, while experienced traders needed a professional trading tool with market context, speed, and control.
Based on this, I formed two hypotheses:
Hypothesis 1
If I build Discover around the way users look for tokens, users will find
relevant tokens faster and convert to trade more often.
Hypothesis 2
If I split the trading interface into two modes, Lite for casual users and Degen
for experienced traders, each group will get a flow built around how they trade, increasing retention, trading volume, and daily activity.
Success criteria
⁕ Increase conversion to first trade
⁕ Increase Discover-to-trade conversion
⁕ Increase 7-day retention after first trade
⁕ Increase average order size
⁕ Increase total trading volume
⁕ Both modes are actively used
⁕ Increase conversion to first trade
⁕ Increase Discover-to-trade conversion
⁕ Increase 7-day retention after first trade
⁕ Increase average order size
⁕ Increase total trading volume
⁕ Both modes are actively used
I evaluated the new experience after each mode reached a minimum threshold of 1,000+
token page sessions and 500+ executed trades, then compared key behavioral metrics
across activation, retention, and trading activity.
New Discover: the top
of the trading funnel
Before users could trade, they first needed to find something worth trading. The original
Discover was functional, but passive: mostly a token list with market data, while the main
user question remained unanswered: “What should I buy?”
I structured Discover around three ways users looked for tokens: market movement, guided search, and real trading activity. Each path led directly into the trading flow, turning Discover
into the first step of the trading funnel.
1. Spotlight
Spotlight helps users quickly find tokens worth attention when they don’t have a specific token
in mind. It surfaces curated feeds of trending tokens, top gainers, and notable market movements, giving users a faster path from discovery to trade.
2. Ask AI
Ask AI turns token discovery into a conversation.
Instead of scrolling through lists, users describe
what they’re looking for and get structured results ready to explore or trade.
AI helps users find tokens but doesn’t provide financial recommendations. The decision to buy always stays with the user.
3. Copy Trade
Copy Trade lowers the barrier for users who don’t know what to buy. Instead of researching on their own, users can follow real trades made by other traders and copy them in one click.
The token, position size, and PnL are visible at a glance. Users can copy a trade directly from the
feed with a preset amount that can be changed
at any time.
Lite and Degen trading modes
The core architectural decision was to build two separate trading modes, each designed
around a different way of trading. Lite gives casual users a simpler path to buy and sell
tokens without unnecessary complexity. Degen gives experienced traders a professional
trading workspace with market context, speed, and control.
The selected mode persists across sessions, so switching between Lite and Degen feels
like adjusting a preference, not moving to a different product.
Lite Mode
Lite Mode is built for users who want to buy and sell tokens quickly without diving into
market data. It reduces cognitive load and shows only what's needed to make a trade.
Degen Mode
Degen Mode is built for experienced traders who need market context, key metrics, charts,
and fast trading actions. Everything is on one screen: the order form, chart, open positions,
and key token metrics, without extra taps or screen switching. Traders can read the market,
make a decision, and act without losing context.
More filters for search results, displayed directly on the page: One-click buy amount setup, trading presets setup, key token metric. Customizable token info display: Volume / Price / Liquidity.
A widget with three buying modes: Instant — for quick one-click buy. Market / Limit with Take Profit and Stop Loss options
All open positions in one place — full portfolio view with current value, average buy price, and unrealized PnL for each token.
A live feed of other users' buys and sells in real time. Helps quickly assess trading activity and make decisions based on current market context.
UX Testing
To validate the new experience, I tested key trading scenarios with both user groups:
casual crypto users and experienced traders. I used moderated sessions, unmoderated
tests in Maze and Useberry, Hotjar analysis after rollout, and product metrics to understand
the impact on activation, retention, and trading activity. I focused on three key scenarios:
1. Find a relevant token through Discover
Users had to find a relevant token through Spotlight, Ask AI, or Copy Trade.
2. Buy or sell a token in Lite Mode
Casual users had to complete a trade without relying on advanced market data.
3. Analyze and trade in Degen Mode
Experienced traders had to review market context, use key metrics, and place
a trade without switching between screens.
UX Validation Results
1. Discover helped users find
tokens for trading faster
Users confirmed that the new Discover helped them find tokens
to buy faster and no longer felt like a static token list.
2. Lite Mode reduced friction
for casual users
Tests showed that casual users completed buy and sell flows with fewer
questions and mistakes when advanced market data was removed from
the main trading flow.
3. Degen Mode helped
experienced traders act faster
Tests confirmed that market context, key metrics, a chart, an order form,
open positions, and fast execution in one place helped traders analyze
tokens and place trades faster.
4. Ask AI was useful, but still
too high-level in early tests
Users liked natural-language search, but early tests showed that some
AI results were too generic or missed the trading intent behind the request.
Ask AI needed more precise token signals and clearer boundaries around
financial recommendations.
Results
I designed Spot Trading from the ground up, helping Blum move its tap-to-earn audience
toward real trading activity. The new Discover and two-mode trading UX architecture created
a clearer path from token discovery to trade and supported growth in activation, retention,
trading volume, and trade value.
50.8M MAU
At its peak
20.6M DAU
At its peak
+13.2%
Conversion to first trade
+11.6%
Growth in average trade size
28.4% → 36.7%
7-day retention after first trade
72% / 28%
Lite / Degen mode usage
Key Learning
One universal trading interface could not serve users with different trading behaviors equally
well. The stronger solution was to design separate experiences around each group's tasks,
context, and need for control.
