Launching a Wallet Connection Success Rate Tracker

A high wallet connection failure rate directly translates to lost users and revenue. If 30% of your users cannot connect their MetaMask, Rainbow, or Phantom wallet on the first attempt, they are likely to abandon your dApp. Tracking this metric allows you to quantify the onboarding friction and prioritize fixes. Common failure points include network mismatches, stale provider states, and mobile deep link issues, all of which are invisible without proper instrumentation.

Connection data helps you debug specific wallet and browser combinations. For instance, you might discover that connection success drops to 40% for users on Brave Browser with Coinbase Wallet, but is 95% for Chrome with MetaMask. This granularity is impossible with generic analytics. By implementing a tracker, you move from guessing to knowing which environments need attention, enabling targeted fixes like updating @web3-react logic or adding clearer error messages for unsupported chains.

Beyond troubleshooting, this data validates infrastructure decisions. When you upgrade your wallet connection library (e.g., from web3modal to wagmi v2) or change RPC providers, the connection success rate is your primary key performance indicator (KPI) for the change. A/B testing different connection flows becomes data-driven. You can answer questions like: does our new in-app wallet tutorial improve first-time connection success by 15%?

For developers, implementing a tracker involves capturing connection attempt events. A simple pattern logs attempts, successes, failures, and the error reason. For example, using the wagmi library, you can hook into the connect mutation to send events to your analytics backend. This creates a feedback loop where production data informs development priorities, ensuring resources are spent on fixes that impact the most users.

Ultimately, tracking wallet connection success is foundational for building a professional, user-centric dApp. It shifts the focus from simply making a feature work in development to ensuring it works reliably for thousands of users across diverse conditions. This metric is as vital as tracking page load speed or transaction completion rates in creating a seamless Web3 experience.

To track wallet connection events, you'll need a development environment capable of interacting with blockchain nodes. The core requirement is Node.js (version 18 or higher) and a package manager like npm or yarn. You will also need a code editor such as Visual Studio Code. This setup allows you to run a local server, install necessary dependencies, and execute scripts that listen for on-chain events. Ensure your system has a stable internet connection for reliable data fetching from remote RPC endpoints.

Your application will require several key libraries. The primary one is viem, a TypeScript interface for Ethereum, which provides the createPublicClient and createWalletClient functions to interact with chains. For tracking, you'll need @chainscore/sdk to access Chainscore's analytics APIs. Additionally, consider using ethers.js v6 as an alternative or for specific utilities. Use dotenv to manage environment variables securely, storing sensitive data like RPC URLs and API keys outside your codebase.

You must obtain API keys for the services your tracker will use. First, sign up for a Chainscore account at chainscore.dev to get your project API key, which authenticates requests to the Wallet Connect Success Rate endpoint. Second, you will need a reliable RPC provider. Services like Alchemy, Infura, or QuickNode offer free tiers. Obtain an RPC URL for your target chain (e.g., Ethereum Mainnet, Polygon). Store these keys in a .env file as CHAINSCORE_API_KEY and RPC_URL.

Initialize a new project directory and install the dependencies. Run npm init -y to create a package.json file. Then, install the required packages: npm install viem @chainscore/sdk dotenv. If you plan to build a frontend component for visualization, you might also add a framework like Next.js or a charting library such as Recharts. This project structure keeps your tracking logic separate from presentation, allowing you to focus on data collection and analysis first.

Configure your environment file and create a basic script. In your project root, create a .env file and add your keys: CHAINSCORE_API_KEY=your_key_here and RPC_URL=your_rpc_url_here. Then, create an index.js or index.ts file. Import viem, dotenv, and the Chainscore SDK. Use dotenv.config() to load variables. This script will be the foundation for creating a client to fetch blockchain data and make API calls to Chainscore to retrieve connection success metrics.

Finally, verify your setup by testing a simple connection. Write a script that uses viem's createPublicClient with your RPC URL to fetch the latest block number. Simultaneously, test the Chainscore SDK by making a basic call to the getWalletConnectSuccessRate endpoint with a dummy wallet address. Successful execution confirms that your environment variables are loaded correctly and that you have the necessary permissions and network access to begin building the full tracker.

A wallet connection success rate tracker is an observability tool that measures the reliability of user interactions with Web3 frontends. Its core function is to capture, aggregate, and analyze events from the wallet connection process—such as modal opens, provider selections, network switches, and signature requests—to calculate a success rate metric. This data is critical for developers to identify and diagnose friction points, like high failure rates for a specific wallet provider or on a particular blockchain network, directly impacting user onboarding and retention.

The architecture is event-driven and typically consists of three main layers. The client-side SDK is embedded in your dApp to capture raw connection events and errors using browser APIs and wallet provider listeners. A backend ingestion service receives these events via HTTP or WebSocket, validates them, and writes them to a time-series database like TimescaleDB or ClickHouse for efficient aggregation. Finally, an analytics and alerting layer queries this database to compute metrics, populate dashboards, and trigger alerts if success rates fall below defined thresholds.

Implementing the tracker starts with instrumenting your application frontend. You need to hook into the connection lifecycle, which varies by library. For ethers.js v6, listen to events from the BrowserProvider. For viem and Wagmi, use the useConnect hook status and error states. For web3.js, monitor the provider's event listeners. The key is to capture both successful connections and all failure modes—user rejection, network mismatch, RPC errors, and timeouts—with contextual metadata like wallet type, chain ID, and error codes.

Data modeling is essential for useful analysis. Each connection attempt should be logged as an event with fields for: session_id, wallet_type (e.g., MetaMask, WalletConnect), chain_id, event_type (e.g., connect_request, sign_success, network_switch_failed), error_code, and a timestamp. This schema allows you to slice metrics by any dimension. Success Rate is calculated as (Successful Events / Total Events) * 100 over a defined time window, but you should also track Time to Connect and Fallback Rate (users trying multiple wallets) for deeper insights.

For production deployment, consider scalability and privacy. Use a queuing system like Apache Kafka or Amazon SQS between your SDK and ingestion service to handle traffic spikes. Anonymize or hash personal data like wallet addresses before storage. The system should be deployed as a standalone microservice or integrated into existing monitoring stacks like Grafana for visualization and Prometheus for alerting, enabling teams to set SLOs (Service Level Objectives) for wallet connection reliability and respond to degradations proactively.

Instrumentation is the process of adding code to your application to capture and report specific user interactions. For tracking wallet connection success, you need to log events at key moments in the connection flow. The primary events to instrument are connection_initiated, connection_success, and connection_failed. Each event should include a unique session ID to correlate attempts with outcomes, the wallet provider (e.g., MetaMask, WalletConnect), the timestamp, and any relevant error codes.

Implementing this requires intercepting your dApp's existing connection logic. For a typical window.ethereum provider, you would wrap the request method call. Here's a basic JavaScript example:

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async function connectWallet() {
  const sessionId = generateSessionId();
  logEvent('connection_initiated', { sessionId, wallet: 'MetaMask' });

  try {
    const accounts = await window.ethereum.request({ 
      method: 'eth_requestAccounts' 
    });
    logEvent('connection_success', { sessionId, wallet: 'MetaMask' });
    return accounts;
  } catch (error) {
    logEvent('connection_failed', { 
      sessionId, 
      wallet: 'MetaMask', 
      error: error.code // e.g., 4001 for user rejection
    });
    throw error;
  }
}

The logEvent function would send this data to your analytics backend or a service like Chainscore.

For frameworks like wagmi or ethers.js, you would instrument the hooks or provider methods similarly. The critical detail is capturing the failure reason. Common error codes include 4001 (user rejected request), -32002 (request already pending), and network-specific errors. Without this granularity, you cannot distinguish between user abandonment and technical failures. Your instrumentation should be passive and non-blocking, using fetch with keepalive or navigator.sendBeacon to avoid impacting the user experience.

Once instrumented, these raw events form the basis of your key metrics. You can calculate the Connection Success Rate (CSR) as (Successful Connections / Total Initiated Connections) * 100. Segmenting this data by wallet provider, browser, or time of day will reveal where failures are concentrated. For instance, a low success rate specifically for WalletConnect sessions could indicate configuration issues with your project ID or relay provider.

Finally, ensure your instrumentation is robust. Test it across different wallets and failure scenarios. Consider adding a heartbeat event for long-running modal sessions to track user engagement time before abandonment. The data schema should be consistent and documented, allowing for easy integration with downstream dashboards. This foundational step turns subjective user frustration into quantifiable, actionable data for improving your dApp's onboarding.

To track wallet connection success, you must first capture the relevant events emitted by your chosen wallet provider. The following useWalletConnectionTracker hook is designed for Ethereum dApps using ethers.js v6 and the MetaMask provider. It listens for the 'accountsChanged' and 'chainChanged' events, which are the primary signals for a successful connection and network switch. The hook manages internal state for the current account and chain ID, and it exposes a callback function (onConnectionEvent) that your analytics logic will call.

The hook's logic is straightforward but critical. When the accountsChanged event fires with a non-empty array, it indicates a successful connection or account switch. The chainChanged event provides the new chain's ID. The hook uses useEffect to set up these listeners when the component mounts and cleans them up on unmount to prevent memory leaks. The provider is expected to be the window.ethereum object or another EIP-1193 compliant provider injected into the browser.

Here is the complete code for the hook. Import ethers and React, then implement it in a file like hooks/useWalletConnectionTracker.js.

javascriptCopy
import { useEffect, useState } from 'react';
import { BrowserProvider } from 'ethers';

export const useWalletConnectionTracker = (provider, onConnectionEvent) => {
  const [account, setAccount] = useState('');
  const [chainId, setChainId] = useState('');

  useEffect(() => {
    if (!provider || !onConnectionEvent) return;

    const handleAccountsChanged = (accounts) => {
      const newAccount = accounts[0] || '';
      setAccount(newAccount);
      // Fire analytics callback on successful connection
      if (newAccount) {
        onConnectionEvent({ type: 'CONNECT', account: newAccount, chainId });
      } else {
        onConnectionEvent({ type: 'DISCONNECT' });
      }
    };

    const handleChainChanged = (newChainId) => {
      // MetaMask returns a hex string; convert to decimal
      const decimalChainId = parseInt(newChainId, 16).toString();
      setChainId(decimalChainId);
      if (account) {
        onConnectionEvent({ type: 'CHAIN_CHANGED', account, chainId: decimalChainId });
      }
    };

    provider.on('accountsChanged', handleAccountsChanged);
    provider.on('chainChanged', handleChainChanged);

    // Fetch initial state
    provider.request({ method: 'eth_accounts' }).then(handleAccountsChanged);
    provider.request({ method: 'eth_chainId' }).then(handleChainChanged);

    return () => {
      provider.removeListener('accountsChanged', handleAccountsChanged);
      provider.removeListener('chainChanged', handleChainChanged);
    };
  }, [provider, onConnectionEvent, account, chainId]);

  return { account, chainId };
};

To use this hook in a component, such as your main App.js or a connection button component, you need to pass the provider and a callback function. The callback (onConnectionEvent) is where you will implement the logic to send data to your backend or analytics service. In this example, we simply log the event, but you would replace console.log with an API call to your tracking endpoint.

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import { useWalletConnectionTracker } from './hooks/useWalletConnectionTracker';

function App() {
  const provider = window.ethereum;

  const handleConnectionEvent = (event) => {
    console.log('Wallet Event:', event);
    // TODO: Send event to your analytics backend
    // Example: fetch('/api/track', { method: 'POST', body: JSON.stringify(event) })
  };

  const { account } = useWalletConnectionTracker(provider, handleConnectionEvent);

  return (
    <div>
      <p>Connected Account: {account || 'Not Connected'}</p>
    </div>
  );
}

This implementation captures the essential connection lifecycle. For production, consider adding error handling for the provider.request() calls and supporting additional wallet providers like WalletConnect or Coinbase Wallet, which may have slightly different event signatures or initialization sequences. The event payload structure ({ type, account, chainId }) is designed to be easily parsed by your backend service, which we will set up in the next step to log these events into a database for analysis.

With your data pipeline operational, the next step is to calculate the core metrics that define wallet connection performance. The primary metric is Connection Success Rate, calculated as (Successful Connections / Total Connection Attempts) * 100. A successful connection is typically defined by a connected event from the wallet provider's SDK. You should also track failure reasons by parsing error messages from connection_error events, categorizing them into types like User Rejected, Network Switch Failed, or RPC Error. Segmenting data by wallet_type (e.g., MetaMask, WalletConnect) and chain_id is crucial for identifying platform-specific issues.

Implement these calculations in your backend service. For a time-series database like TimescaleDB, you can use SQL window functions to compute rolling averages (e.g., 1-hour, 24-hour success rates). For more complex aggregations, consider using a dedicated analytics engine. Here's a simplified SQL query example for an hourly success rate:

sqlCopy
SELECT 
  time_bucket('1 hour', timestamp) as hour,
  wallet_type,
  COUNT(*) as total_attempts,
  SUM(CASE WHEN event_type = 'connected' THEN 1 ELSE 0 END) as successes,
  (SUM(CASE WHEN event_type = 'connected' THEN 1.0 ELSE 0 END) / COUNT(*)) * 100 as success_rate_pct
FROM connection_events
GROUP BY hour, wallet_type
ORDER BY hour DESC;

Visualization turns data into insight. Build a dashboard using a library like Grafana (which connects directly to PostgreSQL/TimescaleDB) or a frontend charting library like Recharts or Chart.js for a custom UI. Essential widgets include: a time-series graph of the overall success rate, a breakdown of success rate by wallet provider, a bar chart of top failure reasons, and a real-time counter of active connection attempts. Set up alerts in Grafana or via your application to notify your team via Slack or PagerDuty if the success rate for a critical wallet (like MetaMask on Ethereum Mainnet) drops below a defined threshold, such as 95%.

For advanced analysis, implement cohort analysis to see if success rates differ for new vs. returning users, or calculate the Mean Time To Connect (MTTC) by measuring the delta between connection_started and connected/connection_error timestamps. Correlate connection failures with on-chain metrics from Chainscore, such as high gas prices or network congestion, to determine if external factors are impacting user experience. This layered approach isolates problems to your dApp's frontend, the wallet provider, or the underlying blockchain network.

Finally, ensure your metrics are actionable. A dashboard is only useful if it drives decisions. Use the visualized data to prioritize bug fixes (e.g., if WalletConnect v2 has a 40% failure rate), inform user support documentation, and guide infrastructure choices. Share key dashboards with your product and engineering teams to create a data-informed feedback loop for continuously improving the wallet connection experience, which is the first and most critical step in any user's Web3 journey.

Proactive alerts transform your monitoring from a passive log into an active defense system. Instead of discovering a problem after user complaints, you can be notified the moment your wallet connection success rate drops below a defined threshold. This allows for immediate investigation and remediation, often before a significant portion of your user base is affected. Setting up these alerts is a critical step in maintaining a reliable Web3 frontend.

To configure an alert, you first define a metric and a condition. Using the Chainscore API, you would typically alert on the success_rate metric for your specific app_id. A common condition is triggering an alert when the 5-minute average success rate falls below 95% or 99%, depending on your application's requirements. You can also set alerts for a sudden spike in specific error codes, like USER_REJECTED or CHAIN_DISCONNECTED, which can indicate UI or RPC issues.

The alert needs a destination. This is where the notification will be sent. Common destinations include Slack channels, Discord webhooks, PagerDuty, or email. For critical production applications, integrating with an incident management platform ensures alerts are never missed. The configuration specifies who gets notified and the urgency of the message, allowing your engineering team to triage issues effectively.

Here is a conceptual example of a cURL request to create an alert using a monitoring service's API (replace placeholders with your actual values). This alert would fire when the success rate for app_id: your_app_123 drops below 98%.

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curl -X POST https://api.chainscore.io/v1/alerts \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Wallet Connect Success Rate Drop",
    "metric": "success_rate",
    "app_id": "your_app_123",
    "condition": "lt 98",
    "window": "5m",
    "destination": {
      "type": "webhook",
      "url": "https://hooks.slack.com/services/YOUR/SLACK/WEBHOOK"
    }
  }'

After setting up alerts, establish a response protocol. Determine what steps your team should take when an alert fires. This might involve checking the Chainscore dashboard for detailed error breakdowns, verifying RPC provider status, reviewing recent frontend deployments, or testing the connection flow manually. Documenting this process ensures a swift and effective response, minimizing downtime and user frustration.

Finally, review and iterate on your alert rules. You may need to adjust thresholds to avoid alert fatigue from non-critical fluctuations or add new alerts for emerging failure patterns. Effective alerting is not a one-time setup but an evolving practice that improves alongside your application's stability and your team's operational maturity.

To launch a production-ready wallet connection tracker, you must move beyond a local dashboard. Deploy your data pipeline and analytics backend to a cloud service like AWS, Google Cloud, or a decentralized alternative like Akash Network. Use infrastructure-as-code tools such as Terraform or Pulumi to manage your deployment. Ensure your data collection endpoint is publicly accessible via a secure, rate-limited API. For the frontend, deploy your dashboard using Vercel, Netlify, or a similar service, and configure a custom domain. Set up automated alerts using PagerDuty, Opsgenie, or a webhook to a Discord/Slack channel to notify your team of critical failures or significant drops in success rates below a defined threshold (e.g., below 95%).

Automation is key for actionable insights. Schedule daily or weekly reports using cron jobs or cloud scheduler services to analyze trends. Implement automated root cause analysis by correlating walletConnect events with other on-chain and off-chain data sources. For example, cross-reference connection failures with RPC provider status pages, recent smart contract deployments, or updates to popular wallet browser extensions. Use this data to automatically tag incidents with probable causes, such as RPC-Timeout, Chain-Mismatch, or Wallet-Update. This transforms raw metrics into structured, actionable intelligence that your development and support teams can use immediately.

Establish a continuous improvement framework by integrating your tracker's findings directly into your development lifecycle. Create a dedicated channel or dashboard for live connection health that is visible to the entire engineering team. Use the historical data from your tracker to set Service Level Objectives (SLOs) for wallet connection success, such as "99.5% successful connections over a 30-day rolling window." Run regular blameless post-mortems for any SLO violations to identify systemic issues. Furthermore, use A/B testing for connection flows; deploy different wallet connection libraries (like Wagmi v2, Ethers v6, or Web3Modal) or UI patterns to different user segments and measure their impact on your success rate metrics to drive data-driven optimizations.