A/B Testing Tools for Mobile Deep Links: Comparison

Running A/B tests on deep links requires tools that can split traffic at the link level, track users across web and app, and handle the complexity of deferred deep linking. Not every A/B testing tool supports these requirements. This guide compares the approaches: using your deep linking platform's built-in testing, integrating a general-purpose A/B testing tool, or building custom testing infrastructure.

For A/B testing fundamentals, see A/B Testing Deep Links and Landing Pages. For platform options, see Deep Linking Platform Comparison Guide for 2026.

The A/B tests list page showing test names, status, types, and variant counts.

Three Approaches

1. Built-In Platform Testing

Your deep linking platform handles both link routing and A/B testing:

Feature	What It Means
Traffic splitting at the link level	Variants assigned before the user reaches the app
Cross-device attribution	Same experiment, tracked from click to install to conversion
No additional SDK	One integration handles everything
Deep link-aware metrics	CTR, install rate, in-app conversion tied to each variant

This is the simplest approach and works well for testing deep link destinations, smart banner variants, and landing page configurations.

2. General-Purpose A/B Testing Tool

Tools like LaunchDarkly, Statsig, GrowthBook, or Optimizely provide feature flagging and experimentation:

Feature	What It Means
Feature flags + experiments	Toggle features and run experiments in one tool
Server-side + client-side	Test on web, mobile, and backend
Statistical engine	Built-in significance calculations
Audience targeting	Target by user properties, segments

These tools are powerful but require you to wire up the deep link connection yourself.

3. Custom Built

Build your own experimentation system:

Feature	What It Means
Full control	Exactly the features you need
No vendor dependency	Own your data and logic
Custom metrics	Track anything you want
Engineering cost	Significant upfront and ongoing investment

Comparison Matrix

Feature Comparison

Capability	Built-In Platform	General-Purpose Tool	Custom Built
Deep link traffic splitting	Native	Requires integration	Build it
Smart banner testing	Native	Requires integration	Build it
Landing page testing	Native	Some support	Build it
In-app destination testing	Supported	Native	Build it
Cross-device tracking	Native	Partial	Complex to build
Deferred deep link attribution	Native	Not supported	Very complex
Statistical significance	Built-in	Built-in	Build it
Sample size calculator	Built-in	Built-in	Build it
Audience segmentation	Basic to moderate	Advanced	Build it
Feature flagging	Limited	Full support	Build it
Multi-platform (web + mobile)	Link-level	Full support	Build it

Cost Comparison

Approach	Upfront Cost	Ongoing Cost	Time to First Test
Built-in platform	Included in plan	None additional	Hours
General-purpose tool	$0-500/mo	$100-2000/mo	Days to weeks
Custom built	2-4 weeks engineering	Ongoing maintenance	Weeks to months

When to Choose Each

Scenario	Best Approach
Testing deep link destinations	Built-in platform
Testing smart banner variants	Built-in platform
Testing landing page layouts	Built-in platform or general-purpose
In-app feature experiments	General-purpose tool
Pricing experiments	General-purpose tool
Onboarding flow testing	General-purpose tool
Full experimentation platform	General-purpose tool or custom
Unique requirements, high traffic	Custom built

Integration Patterns

Pattern 1: Platform-Only Testing

Use your deep linking platform for all link-related experiments:

// Configuration happens in the platform dashboard
// The platform handles variant assignment at the link level

// In your app, just read the deep link parameters
function handleDeepLink(url) {
  const params = parseDeepLink(url);

  // The platform already assigned the variant and added it to params
  if (params.experimentId) {
    analytics.track('experiment_impression', {
      experimentId: params.experimentId,
      variantId: params.variantId,
    });
  }

  navigateTo(params.destination);
}

Pros: Zero additional setup. Cons: Limited to link-level experiments.

Pattern 2: General-Purpose Tool with Deep Link Integration

Use a general-purpose tool and connect it to your deep linking:

// Initialize your A/B testing tool
const experiment = abTestingTool.getExperiment('onboarding_flow_v3');
const variant = experiment.getVariant(userId);

// Use the variant to configure the deep link
const deepLink = buildDeepLink('/onboarding', {
  flow: variant.config.flow,
  experimentId: experiment.id,
  variantId: variant.id,
});

// Track conversion back to the experiment
abTestingTool.trackConversion(experiment.id, variant.id, {
  event: 'signup_completed',
  revenue: 0,
});

Pros: Full experimentation capabilities. Cons: Two systems to maintain, potential attribution gaps.

Pattern 3: Hybrid Approach

Use built-in platform testing for link experiments, general-purpose tool for in-app experiments:

// Link-level experiments (handled by platform)
// - Deep link destination testing
// - Smart banner A/B tests
// - Landing page variants
// -> Configured in platform dashboard

// In-app experiments (handled by A/B testing tool)
// - Onboarding flow
// - Feature rollouts
// - UI experiments
const inAppVariant = abTestingTool.getVariant(userId, 'checkout_redesign');

// Connect the two systems via user ID
// So you can analyze: "Users who saw banner variant A AND checkout variant B"
analytics.setUserProperties({
  bannerExperiment: platformParams.variantId,
  checkoutExperiment: inAppVariant.id,
});

This is the most common production setup: platform handles link experiments, a separate tool handles in-app experiments.

Evaluating General-Purpose Tools

Key Criteria for Deep Link Use Cases

When choosing a general-purpose A/B testing tool for deep link experiments, evaluate:

const evaluationCriteria = {
  serverSideSupport: {
    why: 'Deep link variant assignment often happens server-side',
    required: true,
  },
  mobileSDK: {
    why: 'Track conversions in the app',
    required: true,
  },
  crossPlatformIdentity: {
    why: 'Same user across web landing page and app',
    required: true,
  },
  webhookSupport: {
    why: 'Integrate with deep link platform events',
    nice: true,
  },
  customMetrics: {
    why: 'Track deep-link-specific metrics (install, first open, deep link match)',
    required: true,
  },
  segmentation: {
    why: 'Different results by traffic source, device, OS',
    required: true,
  },
  statisticalEngine: {
    why: 'Automated significance calculations',
    nice: true,
  },
  bayesianOption: {
    why: 'Continuous monitoring without peeking problems',
    nice: true,
  },
};

Open Source Options

Tool	Language	Strengths	Deep Link Fit
GrowthBook	TypeScript	Feature flags + experiments, warehouse-native	Good (server-side SDKs)
Unleash	TypeScript	Feature toggles, self-hosted	Moderate (focused on flags)
Flagsmith	Python	Feature flags, remote config	Moderate
PostHog	TypeScript	Analytics + experiments	Good (event-based)

Managed Solutions

Tool	Strengths	Deep Link Fit	Starting Price
LaunchDarkly	Feature management, enterprise-grade	Good	$10/seat/mo
Statsig	Stats engine, warehouse-native	Good	Free tier available
Optimizely	Full stack experimentation	Good	Contact sales
Split	Feature flags + experiments	Moderate	Free tier available
Amplitude Experiment	Analytics integration	Good	Part of Amplitude plan

Building Custom A/B Testing

When Custom Makes Sense

Build custom when:

• You have unique requirements that no tool satisfies
• You need deep integration with your deep linking infrastructure
• You have engineering resources to maintain it
• You process millions of events and want to avoid per-event pricing

Minimum Viable Experimentation System

// 1. Experiment configuration
const experiments = {
  banner_cta_v1: {
    id: 'banner_cta_v1',
    variants: [
      { id: 'open', weight: 50, config: { ctaText: 'Open' } },
      { id: 'get', weight: 50, config: { ctaText: 'Get' } },
    ],
    status: 'running',
    startDate: '2026-05-01',
    primaryMetric: 'banner_tap',
  },
};

// 2. Deterministic assignment
function assignVariant(userId, experimentId) {
  const experiment = experiments[experimentId];
  if (!experiment || experiment.status !== 'running') return null;

  const hash = djb2Hash(`${userId}-${experimentId}`);
  const totalWeight = experiment.variants.reduce((s, v) => s + v.weight, 0);
  let bucket = hash % totalWeight;

  for (const variant of experiment.variants) {
    bucket -= variant.weight;
    if (bucket < 0) return variant;
  }
  return experiment.variants[0];
}

// 3. Event tracking
function trackExperimentEvent(experimentId, variantId, event, properties) {
  analytics.track('experiment_event', {
    experimentId,
    variantId,
    event,
    ...properties,
    timestamp: Date.now(),
  });
}

// 4. Results calculation
async function getResults(experimentId) {
  const experiment = experiments[experimentId];

  for (const variant of experiment.variants) {
    const impressions = await countEvents('experiment_event', {
      experimentId,
      variantId: variant.id,
      event: 'impression',
    });
    const conversions = await countEvents('experiment_event', {
      experimentId,
      variantId: variant.id,
      event: experiment.primaryMetric,
    });

    console.log(variant.id, {
      impressions,
      conversions,
      rate: (conversions / impressions * 100).toFixed(2) + '%',
    });
  }
}

This covers 80% of use cases. The remaining 20% (statistical engine, guardrail metrics, mutual exclusion, ramp-up) is where the engineering cost grows significantly.

Choosing the Right Setup

Decision Framework

function recommendSetup(requirements) {
  const { dailyTraffic, experiments, engineeringTeamSize, budget, deepLinkFocus } = requirements;

  if (deepLinkFocus && experiments <= 5) {
    return {
      recommendation: 'Built-in platform testing',
      reason: 'Simplest setup for link-level experiments',
      cost: 'Included',
    };
  }

  if (experiments > 5 && engineeringTeamSize < 5) {
    return {
      recommendation: 'General-purpose tool (managed)',
      reason: 'Full experimentation without build cost',
      cost: '$100-2000/mo',
    };
  }

  if (engineeringTeamSize >= 5 && dailyTraffic > 100000) {
    return {
      recommendation: 'Hybrid (platform + custom or open source)',
      reason: 'Scale and control without vendor lock-in',
      cost: 'Engineering time + hosting',
    };
  }

  return {
    recommendation: 'Built-in platform + managed tool',
    reason: 'Best of both worlds',
    cost: '$100-500/mo',
  };
}

Best Practices

1. Start with your deep linking platform: If it offers A/B testing, use it before adding another tool.
2. Don't over-engineer early: A simple variant assignment function covers most early-stage experiments.
3. Ensure cross-platform identity: Whatever tool you use, make sure you can track the same user from link click through app conversion.
4. Separate link experiments from in-app experiments: Use the right tool for each. Don't force a general-purpose tool to do link-level splitting, and don't force your deep linking platform to do in-app feature flags.
5. Track the full funnel: Any tool is only useful if you can track from impression through conversion. Verify end-to-end tracking before scaling experiments.

For Tolinku's built-in A/B testing, see Tolinku A/B testing features. For setup instructions, see the A/B testing docs.