Debugging Analytics: Ensuring Accurate Event Tracking

Analytics bugs are the most dangerous bugs in your app because they are silent. A broken button shows up immediately in user complaints. A broken analytics event shows up weeks later when a product manager notices that conversion data stopped making sense after a release. By that time, you have lost weeks of data and cannot recover it. Debugging analytics requires specialized tools and techniques that go beyond traditional app debugging.

This article covers practical strategies for debugging analytics implementations in iOS apps with KlivvrAnalyticsKit, from development-time inspection to production monitoring.

Real-Time Event Inspector

The most effective debugging tool for analytics is a real-time event inspector that shows every event as it fires. KlivvrAnalyticsKit includes a debug overlay that can be enabled during development.

// Debug event inspector
final class AnalyticsDebugger {
    static let shared = AnalyticsDebugger()
 
    private var isEnabled = false
    private var eventLog: [DebugEvent] = []
    private let maxLogSize = 500
 
    struct DebugEvent {
        let event: AnalyticsEvent
        let timestamp: Date
        let callStack: [String]
        let validationResult: ValidationResult
 
        enum ValidationResult {
            case passed
            case warnings([String])
            case failed([String])
        }
    }
 
    func enable() {
        isEnabled = true
        Logger.info("Analytics debugger enabled")
    }
 
    func disable() {
        isEnabled = false
    }
 
    func logEvent(_ event: AnalyticsEvent, validation: DebugEvent.ValidationResult) {
        guard isEnabled else { return }
 
        let debugEvent = DebugEvent(
            event: event,
            timestamp: Date(),
            callStack: Thread.callStackSymbols,
            validationResult: validation
        )
 
        eventLog.append(debugEvent)
        if eventLog.count > maxLogSize {
            eventLog.removeFirst(eventLog.count - maxLogSize)
        }
 
        printEventToConsole(debugEvent)
    }
 
    private func printEventToConsole(_ debugEvent: DebugEvent) {
        let event = debugEvent.event
        var output = """
        ==============================
        ANALYTICS EVENT: \(event.name)
        Time: \(debugEvent.timestamp)
        Properties:
        """
 
        for (key, value) in event.properties.sorted(by: { $0.key < $1.key }) {
            output += "\n  \(key): \(value) (\(type(of: value)))"
        }
 
        switch debugEvent.validationResult {
        case .passed:
            output += "\nValidation: PASSED"
        case .warnings(let warnings):
            output += "\nValidation: WARNINGS"
            for warning in warnings {
                output += "\n  - \(warning)"
            }
        case .failed(let errors):
            output += "\nValidation: FAILED"
            for error in errors {
                output += "\n  - \(error)"
            }
        }
 
        output += "\n=============================="
        print(output)
    }
 
    // Export event log for sharing with team
    func exportLog() -> Data? {
        let exportable = eventLog.map { debugEvent -> [String: Any] in
            [
                "event_name": debugEvent.event.name,
                "properties": debugEvent.event.properties,
                "timestamp": ISO8601DateFormatter().string(from: debugEvent.timestamp),
                "validation": String(describing: debugEvent.validationResult)
            ]
        }
        return try? JSONSerialization.data(withJSONObject: exportable, options: .prettyPrinted)
    }
}

During development, enable the debugger at app launch and watch the console for every analytics event. This immediate feedback loop catches most tracking issues before code review.

In-App Debug Dashboard

A console log is useful for developers, but sometimes you need a visual inspector that non-technical team members can use to verify tracking. An in-app debug dashboard provides this.

// SwiftUI debug dashboard
import SwiftUI
 
struct AnalyticsDebugView: View {
    @StateObject private var viewModel = AnalyticsDebugViewModel()
 
    var body: some View {
        NavigationView {
            List {
                Section("Summary") {
                    LabeledContent("Total Events", value: "\(viewModel.totalEvents)")
                    LabeledContent("Unique Event Names", value: "\(viewModel.uniqueEventNames)")
                    LabeledContent("Validation Errors", value: "\(viewModel.errorCount)")
                }
 
                Section("Recent Events") {
                    ForEach(viewModel.recentEvents, id: \.event.id) { debugEvent in
                        NavigationLink(destination: EventDetailView(debugEvent: debugEvent)) {
                            EventRow(debugEvent: debugEvent)
                        }
                    }
                }
            }
            .navigationTitle("Analytics Debug")
            .toolbar {
                Button("Clear") { viewModel.clearLog() }
                Button("Export") { viewModel.exportLog() }
            }
        }
    }
}
 
struct EventRow: View {
    let debugEvent: AnalyticsDebugger.DebugEvent
 
    var body: some View {
        VStack(alignment: .leading, spacing: 4) {
            HStack {
                Text(debugEvent.event.name)
                    .font(.headline)
                Spacer()
                validationBadge
            }
            Text(debugEvent.timestamp, style: .time)
                .font(.caption)
                .foregroundColor(.secondary)
            Text("\(debugEvent.event.properties.count) properties")
                .font(.caption)
                .foregroundColor(.secondary)
        }
        .padding(.vertical, 4)
    }
 
    @ViewBuilder
    var validationBadge: some View {
        switch debugEvent.validationResult {
        case .passed:
            Text("OK")
                .font(.caption)
                .padding(.horizontal, 8)
                .background(Color.green.opacity(0.2))
                .cornerRadius(4)
        case .warnings:
            Text("WARN")
                .font(.caption)
                .padding(.horizontal, 8)
                .background(Color.yellow.opacity(0.2))
                .cornerRadius(4)
        case .failed:
            Text("FAIL")
                .font(.caption)
                .padding(.horizontal, 8)
                .background(Color.red.opacity(0.2))
                .cornerRadius(4)
        }
    }
}
 
struct EventDetailView: View {
    let debugEvent: AnalyticsDebugger.DebugEvent
 
    var body: some View {
        List {
            Section("Event") {
                LabeledContent("Name", value: debugEvent.event.name)
                LabeledContent("ID", value: debugEvent.event.id)
                LabeledContent("Time", value: debugEvent.timestamp.formatted())
            }
 
            Section("Properties") {
                ForEach(
                    debugEvent.event.properties.sorted(by: { $0.key < $1.key }),
                    id: \.key
                ) { key, value in
                    LabeledContent(key, value: "\(value)")
                }
            }
        }
        .navigationTitle(debugEvent.event.name)
    }
}

Proxy-Based Network Debugging

Sometimes you need to verify what actually gets sent over the network. A proxy interceptor can capture and display the raw HTTP payloads.

// Network debugging interceptor
final class AnalyticsNetworkInterceptor: URLProtocol {
    static var capturedRequests: [CapturedRequest] = []
 
    struct CapturedRequest {
        let url: URL
        let method: String
        let headers: [String: String]
        let body: Data?
        let responseStatus: Int?
        let timestamp: Date
 
        var decodedBody: [String: Any]? {
            guard let body else { return nil }
 
            // Try decompressing first
            let decompressed = (try? (body as NSData).decompressed(using: .zlib) as Data) ?? body
 
            return try? JSONSerialization.jsonObject(with: decompressed) as? [String: Any]
        }
    }
 
    override class func canInit(with request: URLRequest) -> Bool {
        guard let url = request.url?.absoluteString else { return false }
        return url.contains("analytics") || url.contains("events")
    }
 
    override class func canonicalRequest(for request: URLRequest) -> URLRequest {
        request
    }
 
    override func startLoading() {
        let captured = CapturedRequest(
            url: request.url!,
            method: request.httpMethod ?? "GET",
            headers: request.allHTTPHeaderFields ?? [:],
            body: request.httpBody,
            responseStatus: nil,
            timestamp: Date()
        )
 
        Self.capturedRequests.append(captured)
 
        // Log the captured request
        if let body = captured.decodedBody {
            print("ANALYTICS NETWORK: \(captured.method) \(captured.url)")
            if let events = body["events"] as? [[String: Any]] {
                print("  Events in batch: \(events.count)")
                for event in events {
                    print("  - \(event["en"] ?? event["event_name"] ?? "unknown")")
                }
            }
        }
 
        // Forward to actual network
        let session = URLSession(configuration: .default)
        let task = session.dataTask(with: request) { data, response, error in
            if let response = response {
                self.client?.urlProtocol(self, didReceive: response, cacheStoragePolicy: .notAllowed)
            }
            if let data = data {
                self.client?.urlProtocol(self, didLoad: data)
            }
            if let error = error {
                self.client?.urlProtocol(self, didFailWithError: error)
            }
            self.client?.urlProtocolDidFinishLoading(self)
        }
        task.resume()
    }
 
    override func stopLoading() {}
}
 
// Register the interceptor during development
#if DEBUG
URLProtocol.registerClass(AnalyticsNetworkInterceptor.self)
#endif

Automated Regression Detection

The most dangerous analytics bugs are regressions where a previously working event stops firing or loses properties after a code change. Automated regression detection catches these.

// Analytics snapshot testing
class AnalyticsSnapshotTests: XCTestCase {
    var analyticsHelper: AnalyticsTestHelper!
 
    override func setUp() {
        super.setUp()
        analyticsHelper = AnalyticsTestHelper()
        KlivvrAnalytics.shared.setTestDestination(analyticsHelper)
    }
 
    // Snapshot test: capture all events from a user flow and compare against baseline
    func testOnboardingFlowEventSnapshot() {
        // Execute the full onboarding flow
        let onboarding = OnboardingCoordinator()
        onboarding.simulateFullFlow()
 
        // Capture the event sequence
        let eventSequence = analyticsHelper.trackedEvents.map { event -> [String: Any] in
            [
                "name": event.name,
                "property_keys": Array(event.properties.keys).sorted()
            ]
        }
 
        // Compare against stored snapshot
        let snapshot = try! JSONSerialization.data(
            withJSONObject: eventSequence,
            options: [.prettyPrinted, .sortedKeys]
        )
        let snapshotString = String(data: snapshot, encoding: .utf8)!
 
        // This uses a snapshot testing library
        assertSnapshot(matching: snapshotString, as: .lines)
    }
 
    // Property coverage test: ensure critical events have all required properties
    func testCriticalEventPropertyCoverage() {
        let criticalEvents: [String: Set<String>] = [
            "purchase_completed": ["product_id", "price", "currency", "order_id"],
            "sign_up_completed": ["method", "user_id"],
            "screen_viewed": ["screen_name", "screen_class"]
        ]
 
        // Trigger all critical user flows
        simulateAllCriticalFlows()
 
        // Verify each critical event has all required properties
        for (eventName, requiredProperties) in criticalEvents {
            let events = analyticsHelper.events(named: eventName)
            XCTAssertFalse(events.isEmpty, "Critical event '\(eventName)' was never tracked")
 
            for event in events {
                let eventProperties = Set(event.properties.keys)
                let missing = requiredProperties.subtracting(eventProperties)
                XCTAssertTrue(
                    missing.isEmpty,
                    "Event '\(eventName)' missing properties: \(missing)"
                )
            }
        }
    }
 
    private func simulateAllCriticalFlows() {
        // Simulate purchase flow, sign up flow, navigation, etc.
    }
}

Production Debugging with Debug Events

When analytics issues surface in production, you need debugging capabilities that do not require a new app release.

// Remote debug mode activation
final class RemoteDebugController {
    func checkDebugMode() async {
        guard let config = try? await fetchRemoteConfig() else { return }
 
        if config.analyticsDebugEnabled {
            // Enable verbose logging for this device
            AnalyticsDebugger.shared.enable()
 
            // Send debug events to a separate endpoint
            KlivvrAnalytics.shared.addDestination(DebugDestination(
                endpoint: config.debugEndpoint
            ))
        }
 
        if let sampleRate = config.eventSamplingRate {
            // Temporarily increase event detail for diagnosis
            KlivvrAnalytics.shared.setDetailLevel(.verbose)
            KlivvrAnalytics.shared.setSampleRate(sampleRate)
        }
    }
}
 
// Debug destination sends enriched events for investigation
final class DebugDestination: AnalyticsDestination {
    let name = "Debug"
    let endpoint: URL
 
    func track(event: AnalyticsEvent) {
        var debugProperties = event.properties
        debugProperties["_debug_call_stack"] = Thread.callStackSymbols.prefix(10).joined(separator: "\n")
        debugProperties["_debug_memory_usage"] = ProcessInfo.processInfo.physicalMemory
        debugProperties["_debug_thread"] = Thread.current.description
 
        // Send to debug endpoint
        Task {
            try? await sendDebugEvent(event.name, properties: debugProperties)
        }
    }
 
    func initialize(config: [String: Any]) {}
    func identify(user: AnalyticsUser) {}
    func flush() {}
 
    private func sendDebugEvent(_ name: String, properties: [String: Any]) async throws {
        // HTTP send to debug endpoint
    }
}

Practical Tips

Always enable the analytics debugger during development and watch the console as you navigate the app. Add analytics assertions to your UI tests for critical flows. Set up a Slack channel that receives alerts when production event volumes deviate from expected patterns. Build a pre-release checklist that includes verifying analytics for any screens or flows that changed. Use Charles Proxy or mitmproxy to inspect actual network payloads during QA. Document known analytics edge cases so new team members do not reintroduce fixed bugs.

Conclusion

Debugging analytics requires a different mindset and different tools than debugging features. The silent nature of analytics bugs makes proactive debugging essential. By combining real-time event inspection, in-app debug dashboards, network interception, automated snapshot testing, and production debugging capabilities, KlivvrAnalyticsKit provides the visibility you need to maintain accurate event tracking across releases. The investment in analytics debugging infrastructure pays for itself the first time it catches a tracking regression before it reaches production.

Debugging Analytics: Ensuring Accurate Event Tracking

Real-Time Event Inspector

In-App Debug Dashboard

Proxy-Based Network Debugging

Automated Regression Detection

Production Debugging with Debug Events

Practical Tips

Conclusion

Related Articles

Ensuring Data Quality in Mobile Analytics

Turning Product Analytics into Actionable Insights

Unified Analytics Strategy Across Platforms

Related Articles

Ensuring Data Quality in Mobile Analytics
September 18, 20257 min read

Turning Product Analytics into Actionable Insights
September 16, 20257 min read

Unified Analytics Strategy Across Platforms
September 14, 20257 min read