Onboarding project | Mudrex

Have you ever wondered what a user onboarding journey looks like on a financial app, especially crypto? What are the granular steps and touch points that drives the user to complete the onboarding? Or what are the key mistakes which makes the user drop off the onboarding flow? How small yet effective copies create cognitive biases that fuel onboarding?

Let us take a deep dive into the Onboarding Flow for Mudrex, a crypto investment platform, to understand the nuances of user onboarding, right from the discovery of the product till the first milestone for the user.

Time to call some users 📞

Disclaimer: No Babu Rao were harmed in this process. Calls were only made to recently onboarded users.

After talking to several users about their onboarding experience, there were some key insights and trends that could be summarised in below pointers.

These insights are derived from user calls with newly onboarded users, capturing their pain points, goals, behaviours, and concerns. They serve as the foundation for defining ICPs, optimising onboarding, and refining further strategy.

User Background & Profile

• Users range from complete beginners to experienced crypto traders.
• Many are working professionals looking to invest passively, while others are active traders.
• A large portion of new users are coming from traditional finance (stocks, mutual funds) and want an easy transition to crypto.
• Some users have heard about crypto from friends or influencers but are unsure how to start.
• A small but growing segment is deeply involved in DeFi, seeking advanced trading tools.

User Goals & Motivations

• Wealth Creation & Passive Income – Many users want stable, long-term returns without actively managing their portfolio.
• Diversification – Users see crypto as an alternative investment class and want to allocate a small portion of their portfolio.
• Trading for Profit – Some users are interested in short-term trading strategies to maximise gains.
• Curiosity & Learning – A segment of users is purely out of interest, experimenting with small amounts.

Pain Points & Frustrations

• Confusion About Where to Start – Many users don’t understand how to deposit, invest, or pick the right coins.
• Fear of Losing Money – High volatility, lack of education, and scams make new users hesitant.
• Complex Ecosystem & Overwhelming Information – Advanced crypto platforms with lots of jargons are intimidating for users.
• Long Onboarding & KYC Issues – Extensive information requirement, long verification times, and document rejections lead to drop-offs.
• Unclear Trust Signals – Users worry about platform security, fund safety, and withdrawal reliability.
• Hidden Fees & Charges – Unclear fee structures discourage deposits and trades.
• Lack of Guidance – New users struggle with portfolio allocation and risk management.

Other Key Considerations

• Social Proof & Credibility Matter – Seeing testimonials, community trust, and influencer backing improves adoption.
• Referral & Peer Influence Drive Engagement – Users trust recommendations from friends over ads.
• Easy Access to Funds – Users feel more confident if deposits/withdrawals are seamless and they have proper visibility on funds.
• Education & Support – Providing knowledge on market trends, risk management, and strategies improves retention.

Who are these users?

After talking to these users, we were able to figure out some key cohorts in which these users lie. These user cohorts differ significantly in terms of their lifestyle, preferences, spending habits, pain points and requirements.

Let us take a look at these cohorts.

ICP 1 (Crypto Bros and DeFi Gurus)

This user cohort consists of the native crypto ecosystem audience. Folks who know ins and outs of the ecosystem. The believers in Decentralised Finance.

ICP 2 (Traders and Arbitrage Hunters)

This cohort consists of professional traders who utilise small market movements to book profits. They are native traders who use various technical analysis strategies to predict market movement.

ICP 3 (Explorers and HODLers)

This cohorts comprises of new adopters, core believers and risk-averse users. These users focus on investing for long-term for potential capital gain from the value appreciation.

Prioritising ICPs

The above defined ICPs can be prioritised as:

This analysis clearly shows that ICP 2 (Traders and Arbitrage Hunters) check all boxes when comes to prioritisation based on given parameters, followed by ICP 3 (Explorers and HODLers) and ICP 1 (Crypto Bros and DeFi Gurus) respectively. ICP 2 shows a high frequency of usage along with a high appetitie to pay, while ICP 3 also shows a moderate adoption rate given their willingness to try new asset classes and a decent appetite to pay, with a significant TAM.

Hence, we can prioritise ICP 2 and ICP 3 for onboarding.

Now that we have clarity on who these users are. Let us try to look at how the customer journey looks like. What they are trying to do with our product? What are the use cases they are hiring our product for?

Customer Journey Map

Our main goal from defining a customer journey map is to figure out what the user is trying to do. Let us look at the journey map for a Mudrex user to analyse the different phases from discovery to activation for the user - to see how the user behaves, feels and interacts with the product.

This journey will be slightly different for different ICPs given different goals that they are trying to achieve with the product and hence different interactions and behaviour. However, the core goals for using the product might be the same, which we will discuss further through JTBD.

For this journey, consider a 25 year young professional Rahul, working in Bangalore, who wants to explore crypto investing but is confused about the options available and has trust issues due to high number of financial frauds in the market.

This customer journey map clearly indicates how the customer experiences during different stages of the journey and how their action are derived from their emotions, highlighting the complexity of each stage for the user.

JTBD - Jobs to be done

Each type of user has different goals, and hires the product for different jobs they want to achieve. For Mudrex ICPs, the main use cases for each user cohort can be seen as:

Validating User Goals

We can now validate these defined goals based on the observed user behaviour and insights from our user calls. This will help draw a parallel between what the user conveys and what their actions tell us, hence forming a correlation between the both.

Analysing User Behaviour

We will take a look at some of the key insights observed from the user actions identified on the platform. This will help us in mapping back the user goals with the insights from user behaviour.

Overall Retention Curve:

By analysing user actions on the platform alongside insights from user calls, we can identify patterns that highlight key user motivations, friction points, and opportunities for improvement.

1. Onboarding & First Deposit

• Many users drop off after signup due to:
• • Trust concerns about the platform.
  • Perceived complexity in completing KYC and linking bank accounts.
• First-time investors feel unsure about making their first deposit.
• Users who deposit within a few days have a significantly higher likelihood of staying engaged.

2. Investment & Trading Patterns

• Users who invest in multiple assets within initial days tend to remain engaged long-term.
• Those who make only one or two trades often become inactive.
• Many traders experiment across different coins for short-term gains, while long-term investors hesitate due to:
• • Lack of investment guidance.
  • Overwhelming number of choices.

3. Retention & Engagement

• Users who complete multiple trades in the initial days have significantly higher retention.
• Many disengage after their first trade due to:
• • Disappointment with initial returns.
  • Lack of knowledge about the next steps.
• Users are interested in market insights and education to improve their trading and skills.

4. Deposit & Withdrawal Trends

• Users often start with small test deposits before committing larger amounts, indicating:
• • Concerns over platform credibility and funds visibility.
  • Uncertainty about ease of withdrawals.
• Withdrawals happen soon after initial profits, leading to disengagement.
• Many users lack a clear reinvestment strategy, contributing to profit withdrawals.

Correlating with User Goals

The core user goal of wealth creation and profit-making through crypto is strongly validated through observed platform behaviour and user insights.

• Active Trading & Investment Patterns
• • An active portion of users make their first deposit and trade within a short timeframe, indicating a clear intent to generate returns.
  • Many users diversify their portfolios, actively exploring multiple assets, reinforcing their interest in maximising gains and managing risk.
• Engagement with Market Trends & Strategies
• • Users frequently look for information on asset performance, trends, and investment strategies, showing a market-driven approach to decision-making.
• Emphasis on Transaction Efficiency
• • Users prioritise seamless deposits, withdrawals, and fast trade execution, validating the importance of accessibility and ease of fund movement in their wealth-building journey.
  • Frictionless transactions contribute to user confidence, making it easier for them to act on market opportunities without hesitation.

These behaviours confirm that users are aligned with their broader goal of leveraging crypto as a tool for financial growth, whether through active trading, long-term investments, or structured strategies.

A user is not successfully onboarded until they have completed their activation milestone. Hence, the activation metrics are extremely useful.

Based on the user insights, goals and their behaviours, we can define a couple of hypothesis which might indicate a successful user onboarding and then validate them based on some supporting metrics to conclude on the activation metrics to be used for Mudrex.

Activation Hypothesis

Hypothesis 1

Hypothesis:

First deposit within 24 hours of signup

Explanation:

Users who complete their first deposit within 24 hours of signup are more likely to continue investing and engaging with the platform.

Making the first deposit quickly is a strong indicator of user intent. A seamless deposit experience reduces friction and ensures users don’t drop off after signup. Faster deposits also build trust in the platform’s security and efficiency. If users delay depositing, it may indicate uncertainty or usability issues.

Key Metrics to Track:

• Deposit conversion rate (D0, D1, D7) - number of signed up users depositing
• Time taken to first deposit (median and average)
• Drop-off rate between signup and first deposit
• Retention rate of users who deposit vs. those who don’t

Hypothesis 2

Hypothesis:

Investment/trade in 3 different coins within 7 days

Explanation:
Users who invest/trade in at least three different coins within the first 7 days are more likely to stay engaged and develop long-term trading habits.

Exploring multiple assets increases user engagement, diversification, and exposure to different opportunities. If a user limits themselves to just one asset, they might be less inclined to continue trading. Encouraging broader exploration enhances retention and increases trading volume.

Key Metrics to Track:

• Percentage of active users trading in 3+ coins within 7 days
• Average number of different assets traded per user
• Retention rate of multi-asset traders vs. single-asset traders
• Trading volume growth over time

Hypothesis 3

Hypothesis:

$100 AUM in 14 days

Explanation:

Users who accumulate at least $100 in Assets Under Management (AUM) within 14 days are more likely to continue investing and become long-term customers.

Higher AUM indicates a stronger commitment to the platform. If a user reaches $100 quickly, it suggests they trust the platform and see value in their investments. Low AUM early on may indicate hesitation or lack of confidence. This metric also correlates with revenue potential, as platform fees depend on AUM.

Key Metrics to Track:

• Percentage of users reaching $100 AUM in 14 days

• Average AUM per user over time

• Churn rate of high vs. low AUM users

• Correlation between AUM and trading frequency

Hypothesis 4

Hypothesis:

5+ overall trades/investments in 7 days (can be same assets/coins)

Explanation:

Users who make at least 5 trades/investments within the first 7 days are more likely to become active, recurring traders.

Frequent trading early on suggests that users are comfortable with the platform and see value in continuing to engage. A low trade count may indicate that the user is still hesitant or does not find the trading experience compelling enough. Encouraging multiple trades increases platform stickiness and long-term engagement.

Key Metrics to Track:

• Percentage of users making 5+ trades within 7 days

• Average number of trades per user in the first week

• Retention and engagement rates of high-trade vs. low-trade users

• Trading volume growth over time

Hypothesis 5

Hypothesis:

First withdrawal within 14 days of signup

Explanation:

A user completes their first withdrawal within 14 days of signing up.

This measures whether users see full-cycle value in the platform. A withdrawal indicates that they have deposited, traded/invested, and now trust the platform enough to cash out their funds. If users complete a withdrawal within the first 14 days, it shows that they are actively engaging with the platform beyond just experimentation. A smooth withdrawal experience also builds confidence, making users more likely to reinvest, deposit again, and continue using the platform. If withdrawal rates within this timeframe are low, it could indicate friction in the process or a lack of perceived value in cashing out, signaling areas for optimisation.

Key Metrics to Track:

• Percentage of users making a withdrawal within 14 days.
• Average time taken from first deposit to first withdrawal.
• Retention rate of users who complete a withdrawal vs. those who don’t.
• Number of users who re-deposit after withdrawing.

Validating Hypothesis

Given Mudrex is in early scaling, we can track three key outcomes to validate our hypothesis. These include:

1. Impact on retention curve
2. Impact on referral and wom
3. Impact on LTV

Impact on Retention Curve

The retention curve when filtered with the users based on 5 hypothesis shows:

This validates our all hypothesis when checked against retention curve for the last 3 months, showing that all 5 hypothesis shows users with high intent and will show better retention over long period of time.

However, the percentage of users from total cohort also significantly reduces based on different hypothesis which should be accounted as well.

Observations:

• Sharp drop in overall retention after the first month, with only ~16% of users remaining by March.
• H2 and H4 exhibit the highest retention, indicating long-term engagement and sustained usage.
• H1 shows strong early retention but declines rapidly, likely due to speculative short-term usage.
• H3 and H5 have the lowest retention, suggesting lower product stickiness or mismatched expectations.

Impact on Referrals

As we can see here, the referral rates are much higher for selected cohorts as compared to overall cohort referral rates. Some other observations are:

• H2 leads with the highest referral rate , showing strong word-of-mouth potential.
• H3 and H5 have lower referral rates, indicating moderate virality.
• H1 and H4 are close , reflecting consistent advocacy among their cohorts.
• Overall referral rate stays low, given lower wom from overall audience.

Impact on LTV

The overall AUM is growing steadily with some saturation and reduction in growth pace after some time. Some other observations include:

• Steady AUM growth across hypotheses, but at different paces depending on user engagement.
• H2 has the highest AUM, aligning with higher retention and transaction activity.
• H3 lags slightly, suggesting lower transactional volume or passive investment behaviour.
• H1, H4, and H5 show similar patterns, with steady growth but a natural slowdown in March.
• Growth rate slows down slightly in March, indicating maturation in deposits and usage patterns.

Conclusion

The data validates that long-term engagement, referral potential, and AUM growth are interconnected but vary significantly across user cohorts.

• H2 emerges as the strongest cohort, showing the highest retention, referrals, and AUM, making it the most valuable and engaged segment.
• H4 follows closely, with strong retention and steady AUM growth, indicating sustained product usage.
• H1 demonstrates early enthusiasm but drops off quickly, suggesting a speculative, short-term mindset with lower long-term value.
• H3 and H5 struggle with both retention and referrals, pointing to lower product stickiness and potential gaps in meeting user expectations.