Exploring Balancer V2

Case Study: Analyse Balancer's user acquisition, retention and behaviour.

This use case demonstrates how Orus’ on-chain analytics dashboard can be leveraged to gain deep insights into user acquisition, behavior, and operational metrics for Balancer V2.

🎯 Objective

What are you trying to achieve?

To optimize user acquisition, behavior, and operational efficiency on Balancer V2 by understanding how users interact with the platform, tracking key operational metrics, and identifying areas for growth and improvement.

💪 User Profile

Who would this be ideal for?

• Defi Project Managers

• Growth Marketers

• Product Managers

• Data Analysts/Researchers

• Crypto VCs/Investors

🚀 Business Impact

How can Orus solve your problem?

• Design more effective user engagement strategies, reducing churn and increasing platform stickiness.

• Identify the most effective acquisition channels and understand user behavior, thereby learning to allocate resources more efficiently and optimize marketing campaigns for better ROI.

• Use data to guide product development, helping to prioritize features that enhance user experience and drive higher engagement.

• Understand platform dynamics, enabling more accurate predictions and better strategic recommendations.

🤔 Questions

Questions you might have

"How effectively are we converting new users into active users, and at what stages do we see the most drop-off?"

"Are there any patterns in user retention, and how can we improve retention rates over time?"

"How does user transaction behavior (e.g., swap, deposit, withdrawal) change over time, and what factors influence these changes?"

"What is the average swap activity, and how does this correlate with market conditions or specific events?"

"How is the pool count evolving, and what impact does it have on the platform’s liquidity and user engagement?"

"How do average gas fees affect user behavior and platform usage, and what can be done to mitigate high fees?"

"What trends do we see in TVL (Total Value Locked) across various pools, and how can we use this data to optimize liquidity incentives?"

🔍 Exploration

How can you use Orus to explore these answers?

Analyzing User Acquisition

User retention (proxy): Active users (90D) / Active users (180D)

[ETH]C [BALANCERV2]APP ACTIV_90D / [ETH]C [BALANCERV2]APP ACTIV_180D G 180D

User retention (proxy): Active users (30D) / Active users (90D)

[ETH]C [BALANCERV2]APP ACTIV_30D / [ETH]C [BALANCERV2]APP ACTIV_90D G 90D

User retention (proxy): Active users (7D) / Active users (30D)

[ETH]C [BALANCERV2]APP ACTIV_7D / [ETH]C [BALANCERV2]APP ACTIV_30D G 180D

New users: New user count by day

[ETH]C [BALANCERV2]APP NEW G 180D

Analyzing User Behaviour

TX count: TX count

[ETH]C [BALANCERV2]APP TXC G 180D

Swap volume

[ETH]C [BALANCERV2]APP SV G 180D

Withdraw volume

[ETH]C [BALANCERV2]APP WV G 180D

Average swap activity: Swap count / unique swapper count

[ETH]C [BALANCERV2]APP SC / [ETH]C [BALANCERV2]APP USC G 180D

Deposit/withdrawal count: Deposit count, withdrawal count

[ETH]C [BALANCERV2]APP DC / [ETH]C [BALANCERV2]APP WC G 180D

Analyzing Balancer Operations

Pool count

[ETH]C [BALANCERV2]APP PC G 180D

New pools

[ETH]C [BALANCERV2]APP NPC G 180D

TVL: Liquidity

[ETH]C [BALANCERV2]APP L AGG[APP] G 180D