Your Google Ads dashboard credits 4,200 installs last month. Meta claims 6,800. TikTok shows 2,100. When you add them up, you get 13,100 installs, but your analytics platform only recorded 9,500 total. This isn't a data quality issue. It's an attribution model problem that's making you count the same user three times, and it's quietly inflating every performance metric you're tracking.

Most mobile marketers have been told that multi-touch attribution is "better" than single-touch models because it captures the full customer journey. That advice isn't wrong, but it's incomplete. The real question isn't which model is theoretically superior. It's which model actually helps your team make faster, more confident budget decisions with the data infrastructure and engineering resources you have right now.

After working with 50+ growth teams across gaming, fintech, D2C, and mobility apps, we've seen this pattern repeatedly: teams adopt multi-touch attribution because they think they should, spend 3-4 weeks configuring it, then revert to last-click reporting because the multi-touch data creates more confusion than clarity. This post explains why that happens, when each model genuinely outperforms the other, and how to choose based on your actual operating constraints, not theoretical best practices.

The Attribution Model Confusion: Why Every Platform Recommends Something Different

Here's what typically happens when you ask different platforms about attribution models:

Meta Ads Manager defaults to 7-day click, 1-day view attribution. They count an install as theirs if someone clicked a Meta ad within 7 days or viewed one within 24 hours before installing. If that same user also clicked a Google ad 5 days ago and a TikTok ad 3 days ago, Meta still claims 100% credit.

Google Ads uses last-click attribution by default but offers data-driven attribution (a multi-touch model) for accounts spending above certain thresholds. Their data-driven model redistributes credit across multiple touchpoints, but only for touchpoints Google can see, which creates its own blind spots.

TikTok Ads Manager uses last-click with configurable attribution windows (1-day, 7-day, or 28-day). Like Meta, they claim full credit even when other channels were involved in the journey.

Your MMP (Mobile Measurement Partner) typically defaults to last-click attribution across all channels, but offers multi-touch options including linear, time decay, U-shaped, W-shaped, and custom models. Each redistributes credit differently across the customer journey.

The result: you're not comparing apples to apples. You're comparing four different attribution models that each define "success" differently, and every one of them is technically correct within its own framework. This is why your channel performance reports never reconcile and why executive meetings devolve into debates about "which number is real".

The underlying problem: Attribution models aren't measuring reality. They're applying different accounting systems to the same underlying behaviour. A user who saw three ads before installing is a single install event, but depending on your attribution model, you might count it as one install (last-click), three installs (platform-specific), or 0.33 installs credited to each channel (linear multi-touch). None of these are "wrong", they're just different lenses for allocating credit.

What Multi-Touch Attribution Actually Measures (and What It Doesn't)

Multi-touch attribution attempts to distribute credit across multiple marketing touchpoints in a user's journey to installation or conversion. Instead of giving 100% credit to the last ad clicked, it recognises that earlier touchpoints may have influenced the decision.

Common multi-touch models include:

Linear attribution: Splits credit equally across all touchpoints. If a user saw three ads before installing (TikTok → Meta → Google), each gets 33.3% credit. Simple to understand, but ignores the reality that not all touches have equal influence.

Time decay attribution: Gives more credit to touchpoints closer to conversion. In the same three-touch journey, Google (last touch) might get 50%, Meta gets 33%, TikTok gets 17%. Reflects recency bias but can undervalue top-of-funnel awareness channels.

U-shaped (position-based) attribution: Credits 40% to first touch, 40% to last touch, and splits the remaining 20% across middle touches. Assumes first and last interactions matter most, which works well for longer consideration cycles but may not fit impulse-download apps.

W-shaped attribution: Similar to U-shaped but adds a third spike at a key middle action (typically first app open or signup). Credits 30% to first touch, 30% to middle milestone, 30% to last touch, with remaining 10% split across other touches.

Data-driven attribution: Uses machine learning to analyse thousands of conversion paths and algorithmically determine which touchpoints historically correlate with conversions. Sounds sophisticated, requires massive data volume (typically 15,000+ conversions per month minimum) and still struggles with new channels or creative variations.

What multi-touch actually measures: The relative influence of different touchpoints assuming your attribution model's assumptions about influence are correct. It doesn't measure causality. It measures correlation patterns and applies a predefined weighting scheme.

What multi-touch doesn't measure:

• Touchpoints your MMP can't see: Organic word-of-mouth, offline conversations, app store browsing, competitor research. Multi-touch only redistributes credit among tracked touchpoints.

• The counterfactual: Would the user have installed anyway without that middle touch? Multi-touch models don't answer this.

• View-through impact accurately: Most multi-touch models still struggle with view-through attribution (users who saw but didn't click an ad) because view tracking is probabilistic and privacy-limited post-iOS 14.5.

• Cross-device journeys: User sees Meta ad on desktop, clicks Google ad on mobile, installs app. Most MMPs track this as two separate users unless you have advanced cross-device graph capabilities.

This is critical context because many teams adopt multi-touch attribution expecting it to reveal hidden truths about their funnel, then discover it's redistributing uncertainty rather than eliminating it.

Single-Touch Models: Last-Click, First-Click, and Platform-Specific Attribution

Single-touch attribution gives 100% credit to one touchpoint in the customer journey. The three most common variants:

Last-click attribution credits the final touchpoint before installation or conversion. This is the default model for most MMPs and performance marketing platforms because it's simple, deterministic, and aligns with direct-response advertising principles. If a user's last interaction was clicking a Google ad, Google gets 100% credit, full stop.

Why teams use last-click:

• Clean, unambiguous data: every install maps to exactly one source

• Easy budget decisions: ROAS is calculated directly without weighted credit splits

• Fast analysis: no complex journey mapping required

• Works well for short consideration cycles (gaming, utility apps, impulse downloads)

Where last-click breaks down:

• Undervalues top-of-funnel awareness channels (display, video, influencer)

• Doesn't capture assisted conversions

• Can create perverse incentives (retargeting campaigns get over-credited because they're often the last touch)

First-click attribution credits the initial touchpoint that introduced the user to your app. Less common in mobile marketing but useful for understanding acquisition sources. If a user first discovered your app through an influencer post, then clicked Meta ads twice before installing, the influencer gets 100% credit.

Why teams use first-click:

• Reveals true acquisition sources, not just conversion triggers

• Useful for brand awareness and top-of-funnel budget allocation

• Helps identify which channels are effective at cold audience introduction

Where first-click breaks down:

• Ignores the nurturing required to convert awareness into action

• Difficult to operationalise (requires tracking first touch across long attribution windows)

• Doesn't help optimise conversion-focused campaigns

Platform-specific attribution is what happens when you let each ad platform use its own attribution window and model. Meta uses 7-day click/1-day view, Google uses last-click within their ecosystem, TikTok uses their own windows. This creates the overlap problem described earlier.

Why teams use platform-specific:

• No setup required, each platform handles it automatically

• Campaign optimisation happens natively within each platform

• Works acceptably when running only one major paid channel

Where platform-specific breaks down:

• Massive install inflation when running multiple channels

• Impossible to calculate true blended ROAS or CAC

• Budget allocation becomes guesswork

The Hidden Costs of Multi-Touch: Data Volume, Engineering Time, and Analysis Paralysis

Multi-touch attribution sounds sophisticated, but implementation reveals hidden costs that many teams underestimate:

Data volume requirements: Meaningful multi-touch attribution needs statistically significant sample sizes for each conversion path. For data-driven models, you typically need 15,000-20,000 conversions per month minimum. For gaming apps installing 500,000 users monthly, this works. For a B2C fintech app installing 8,000 users monthly across 6 channels, you don't have enough data volume for the model to learn reliable patterns.

Without sufficient volume, multi-touch models produce unstable results: one week Meta gets 45% credit, next week it's 31%, not because performance changed but because small sample fluctuations swing the algorithm. Teams then can't tell if they're seeing real performance shifts or model noise.

Engineering complexity: Setting up multi-touch properly requires:

• Comprehensive event tracking across all touchpoints (impressions, clicks, views)

• User identity resolution to stitch together multi-session journeys

• Custom conversion windows configured for each channel's typical path length

• Ongoing model tuning as channel mix and user behaviour evolves

For a lean growth team with 1-2 engineers supporting mobile, this represents 20-40 hours of initial setup plus ongoing maintenance. That's engineering time not spent on product features, A/B tests, or performance optimisations that might have more direct impact.

Analysis paralysis: Multi-touch attribution creates multiple "correct" answers to the same question. Your linear model says TikTok ROAS is 1.8x, but your time-decay model says 1.3x, and your U-shaped model says 2.1x. Which one guides budget reallocation?

We've seen teams spend 4-6 hours weekly in reporting debates trying to reconcile multi-touch outputs with platform-reported data, time that could have been spent running creative tests or launching new campaigns. The promise of better data accuracy becomes a distraction from the actual job: running profitable marketing.

Model selection complexity: Choosing between linear, time decay, U-shaped, W-shaped, or data-driven isn't obvious. Each makes different assumptions about user behaviour. Gaming apps with short paths favour time-decay. Enterprise apps with long nurture cycles favour U-shaped. But you won't know which fits your funnel until you've run them in parallel for 4-8 weeks and compared outcomes, by which time you've already invested significant effort.

Incrementality testing becomes harder: When running multi-touch attribution, isolating the true incremental impact of a channel requires complex holdout tests and causal inference frameworks. With single-touch last-click, you can pause a channel for 2 weeks and immediately see the impact on overall install volume. Multi-touch redistributes credit dynamically, making these tests less conclusive.

When Single-Touch Outperforms: Budget Under ₹75,000/Month, Simple Funnels, Clear Channel Separation

Single-touch attribution, specifically last-click, often delivers better decision velocity when:

Your monthly marketing budget is below ₹75,000 (~$1,000). At this scale, you're likely running 2-4 primary channels (typically Meta + Google, maybe TikTok or influencer). Multi-touch complexity outweighs the marginal insight gain. You need fast iteration cycles, not sophisticated attribution archaeology. Last-click tells you clearly: this campaign drove X installs at Y cost with Z ROAS. Move budget toward winners, pause losers, repeat weekly.

A D2C fashion app we worked with tried implementing U-shaped attribution with ₹50,000/month spend across Meta and Google. After six weeks, their Head of Growth reverted to last-click because the multi-touch model kept fluctuating wildly (sample size issues) and slowed their weekly optimisation cadence from 45 minutes to 3 hours. They wanted to know "which ad sets are working" not "how should we philosophically distribute credit across the funnel".

Your funnel is short and direct. Gaming apps, utility apps, and impulse-purchase categories often see install within hours or days of first awareness. Users aren't conducting lengthy research. They see an ad, click, install, done. In these scenarios, the last touchpoint genuinely is the dominant influence. Multi-touch adds complexity without adding insight.

Contrast this with B2B SaaS or high-consideration purchases (insurance apps, investment platforms, education apps) where users might research for weeks, see multiple ads, read reviews, and compare alternatives before converting. Here, multi-touch captures real behaviour patterns.

Your channels have clear separation. If you're running Meta for acquisition, Google for search intent, and influencers for awareness, with minimal overlap in audience targeting, last-click attribution is sufficient because users aren't seeing multiple touchpoints from different channels in the same journey. Meta users come through Meta, Google users come through Google.

This breaks down when you're running retargeting across platforms, broad audience overlaps, or omnichannel campaigns where the same user legitimately interacts with multiple channels before converting. Then single-touch under-represents the true complexity.

You optimise at the campaign/ad set level, not the journey level. If your weekly routine is: review campaign performance → pause poor performers → scale winners → test new creatives, last-click provides the clean signal you need. You're not trying to understand the holistic journey, you're trying to answer "should I spend more on this campaign or that campaign". Multi-touch doesn't help answer that question faster.

Your team is resource-constrained. Two-person growth teams don't have time for sophisticated attribution modelling. They need clear dashboards that load in under 10 seconds, show yesterday's ROAS by campaign, and support fast yes/no decisions on budget shifts. Single-touch delivers this. Multi-touch requires analysts to interpret model outputs, which you might not have.

When Multi-Touch Matters: Complex Journeys, Assisted Conversions, Attribution Credits

Multi-touch attribution becomes genuinely valuable when:

Your conversion journey is measurably complex. Run a path analysis in your MMP. If 40%+ of conversions involve 3 or more touchpoints across different channels before installing, you have journey complexity that multi-touch can help illuminate. This is common in:

• Fintech apps (users research, compare, return multiple times)

• Education apps (long consideration, parent involvement, seasonal patterns)

• High-ticket marketplace apps (travel, real estate, luxury goods)

• Enterprise/B2B apps (multi-stakeholder decisions)

A fintech app we worked with discovered through multi-touch analysis that 60% of their high-LTV users (those who completed KYC and made first transaction) had interacted with both paid social (awareness) and paid search (intent) before installing. Last-click attribution was giving 100% credit to search, leading them to underfund social, which was actually the critical awareness driver. Multi-touch revealed that social + search together drove their best cohorts, prompting them to increase social spend by 40%, which improved overall CAC efficiency.

You're running orchestrated multi-channel campaigns. If your strategy deliberately uses different channels for different funnel stages (TikTok for awareness → Meta for consideration → Google for conversion), multi-touch helps you understand whether the orchestration is working. You need to know: are top-of-funnel awareness investments assisting bottom-of-funnel conversions, or are they wasted reach?

This requires setting up UTM parameters and tracking codes carefully so your MMP can stitch together journeys across channels, plus enough conversion volume to make the patterns statistically meaningful.

You need to defend brand/awareness budget to finance or exec teams. CFOs and non-marketing executives often ask "why are we spending ₹30,000/month on YouTube if it's only driving 400 installs". Last-click attribution can't answer that question well if YouTube is actually creating awareness that leads to Google search conversions days later. Multi-touch (specifically U-shaped or W-shaped models) can quantify assisted conversions and help justify top-of-funnel spend.

For teams that need to produce quarterly board reports showing "full-funnel impact", multi-touch provides the narrative structure to tell that story convincingly.

You have sufficient data volume and analytical capacity. If you're installing 100,000+ users monthly, running 8+ active channels, and have a dedicated analytics function (not just performance marketers wearing analyst hats), you can extract real value from multi-touch. You have the sample size for models to learn stable patterns, and you have people whose job is interpreting the attribution outputs and translating them into strategy.

You're optimising creative strategy, not just budget allocation. Multi-touch can reveal which creative approaches work at different funnel stages. Top-of-funnel brand video might not drive direct conversions but measurably improves conversion rates when users later see performance creative. This insight is invisible in last-click but becomes actionable in multi-touch when you analyse assisted conversion patterns.

A Practical Decision Framework: Choosing Based on Your Team and Budget Reality

Rather than asking "which attribution model is better", ask these diagnostic questions:

Question 1: What decision are you trying to make faster?

If your answer is "which campaigns should I pause and which should I scale this week", you need simple, deterministic data. Last-click delivers this. If your answer is "how should I rebalance my annual budget across awareness, consideration, and conversion channels", multi-touch helps inform that strategic view.

Match your attribution model to your decision cadence. Weekly tactical decisions benefit from single-touch clarity. Quarterly strategic planning benefits from multi-touch journey insights.

Question 2: How many conversions per month are you tracking?

• Under 5,000/month: Single-touch only. Multi-touch won't have statistical stability.

• 5,000 to 20,000/month: Single-touch as default, consider simple multi-touch (linear or time-decay) if you're running 4+ channels and seeing clear journey overlap.

• 20,000 to 100,000/month: Multi-touch becomes viable, especially for understanding channel interaction effects.

• Over 100,000/month: Multi-touch and data-driven models can add real value.

Question 3: How many active acquisition channels are you running?

• 1-2 channels: Single-touch is sufficient. You don't have journey complexity to model.

• 3-4 channels: Evaluate whether they have meaningful audience overlap. If yes, consider multi-touch. If no, single-touch is fine.

• 5+ channels: Multi-touch helps understand interaction effects and assisted conversions.

Question 4: What's your team's analytical bandwidth?

If you have dedicated analysts: Multi-touch is worth the setup investment.

If your performance marketers are also your analysts: Start with single-touch and add complexity only when it's clearly limiting decisions. Don't optimise for theoretical sophistication; optimise for decision velocity.

Question 5: How long is your typical conversion window?

• 0-7 days (gaming, utilities, impulse categories): Last-click captures most of the story.

• 7-30 days (most consumer apps): Multi-touch reveals meaningful patterns if you have volume.

• 30+ days (fintech, education, enterprise): Multi-touch is valuable for understanding nurture sequences.

Recommended implementation path:

Stage 1 (Months 0-3): Start with last-click attribution across all channels via your MMP. Get clean, unified reporting working. Build confidence that your tracking is accurate and your team trusts the numbers.

Stage 2 (Months 3-6): Run path analysis reports monthly to understand what percentage of conversions involve multi-touch journeys. If it's under 25%, stick with last-click. If it's over 40%, consider adding multi-touch.

Stage 3 (Months 6-12): If introducing multi-touch, start with one simple model (linear or time-decay), run it in parallel with last-click for 8 weeks, and compare whether the insights change your budget decisions meaningfully. If yes, adopt it. If no, revert to last-click.

Stage 4 (Year 2+): With mature tracking and sufficient volume, experiment with data-driven attribution if your MMP supports it. But never abandon last-click reporting entirely because it remains the clearest diagnostic for campaign-level performance.

How to Validate Your Attribution Model in Your MMP

Most MMPs let you switch attribution models in reporting views without changing underlying data collection. Here's a practical validation process:

Step 1: Pull last 30 days of install data using last-click attribution. Note your top 3 channels by volume and ROAS.

Step 2: Switch to a multi-touch model (try linear first) and pull the same date range. Compare:

• Did the rank order of channels change?

• Did ROAS calculations shift by more than 15% for any major channel?

• Are the total attributed installs roughly the same (they should be; you're redistributing credit, not creating new conversions)?

Step 3: If multi-touch creates significant rank changes, drill into specific conversion paths to understand why. Are you seeing genuine multi-channel journeys, or is the model amplifying noise?

Step 4: Run this comparison monthly for three months. If the multi-touch insights consistently change your budget allocation decisions in ways that improve overall ROAS, keep it. If it's creating confusion without improving outcomes, revert to last-click.

Platforms like Linkrunner support both single and multi-touch models within the same dashboard, letting teams start with last-click and layer in multi-touch views when their funnel complexity and data volume justify it, without rebuilding their entire measurement infrastructure or reconfiguring SDK integrations.

Common Mistakes Teams Make with Attribution Models

Mistake 1: Adopting multi-touch because it sounds more sophisticated

Multi-touch isn't inherently "better". It's more complex, which is only valuable if that complexity solves a real problem. Teams with simple funnels and small budgets often adopt multi-touch to seem data-driven, then discover it slowed their decision-making without improving outcomes.

Mistake 2: Trusting platform-specific attribution as a source of truth

If you're adding up installs from Meta, Google, and TikTok dashboards and wondering why they exceed your actual total installs by 40%, you're experiencing platform attribution overlap. Each platform uses its own model and claims credit independently. This isn't useful for budget allocation because you can't spend more than 100% of your budget.

The fix: use your MMP as the single source of truth with one consistent attribution model (whether single or multi-touch), and use platform dashboards only for creative performance diagnostics within that platform.

Mistake 3: Changing attribution models mid-quarter

If you switch from last-click to time-decay attribution in week 6 of the quarter, your campaign performance will suddenly shift, not because actual performance changed but because you changed the accounting system. This creates false signals that lead to poor budget decisions.

Stick with one model for at least 60-90 days before evaluating whether to change it. If you want to test a new model, run it in parallel reporting, don't switch your primary decision dashboard.

Mistake 4: Ignoring view-through attribution entirely

Most single-touch last-click setups don't capture users who viewed an ad but didn't click, then later installed organically or through another channel. For brand campaigns and video advertising, this can significantly undervalue their contribution.

The fix: even if using last-click as primary model, run monthly view-through reports to understand if your display/video campaigns are driving awareness that assists other channels.

Mistake 5: Over-engineering attribution when you have measurement gaps elsewhere

Teams sometimes obsess over choosing the perfect attribution model while having broken event tracking, misconfigured postbacks, or unvalidated revenue data. Attribution modelling only redistributes the credit across touchpoints you're already tracking accurately. If your underlying event instrumentation is flawed, sophisticated attribution models just redistribute garbage.

Fix your measurement hygiene first (accurate install tracking, reliable revenue events, proper UTM parameters, validated postbacks), then optimise attribution logic.

Key Takeaways

Multi-touch attribution isn't universally better than single-touch. It's more complex, which creates value only when you have complex conversion journeys, sufficient data volume, and analytical resources to extract insights from the model outputs.

Single-touch last-click remains the most practical model for teams spending under ₹75,000/month, running 2-4 channels, optimising weekly at campaign level, or operating with lean analytical capacity. It provides decision clarity and fast iteration cycles.

Multi-touch becomes valuable when you're spending over ₹150,000/month across 5+ channels, see measurable multi-touch journeys (40%+ of conversions involve 3+ touchpoints), and have dedicated analysts who can translate attribution insights into strategy changes.

The right attribution model is the one that helps your team make faster, more confident budget decisions with the resources you actually have, not the one that sounds most sophisticated in strategy documents.

Start simple, add complexity deliberately. Begin with last-click attribution, validate that your measurement infrastructure is reliable, then introduce multi-touch only when you have clear evidence (from path analysis) that journey complexity is limiting your understanding of channel performance.

Your MMP should make model switching easy. The ability to toggle between attribution models in reporting views, without changing SDK setup or data pipelines, lets you experiment with different lenses on the same underlying user behaviour and choose what works for your team's decision cadence.

Frequently Asked Questions

Do I need multi-touch attribution if I'm only running Meta and Google?

Not necessarily. If your Meta and Google audiences have minimal overlap (for example, Meta for cold acquisition, Google for search intent), last-click is sufficient. But if you're running prospecting and retargeting across both platforms, multi-touch can reveal which platform is better at initial awareness vs final conversion.

How do I know if my conversion journeys are complex enough for multi-touch?

Run a conversion path report in your MMP for the last 30 days. What percentage of installs involved interactions with 3+ different campaigns or channels before converting? If it's under 25%, stick with single-touch. If it's over 40%, multi-touch will add insight.

Can I use different attribution models for different reporting needs?

Yes, and many teams do this. Use last-click for weekly campaign optimisation (fast tactical decisions), and run monthly multi-touch reports for strategic budget allocation across channels. Just don't switch your primary decision dashboard mid-quarter.

Does multi-touch attribution work post-iOS 14.5 with limited tracking?

Multi-touch becomes harder with probabilistic attribution and SKAN data limitations, but not impossible. You need larger sample sizes and should expect less precision. Many teams have shifted toward simplified multi-touch models (linear or time-decay) rather than complex data-driven models because the signal quality doesn't support sophisticated machine learning.

What's the difference between multi-touch attribution and incrementality testing?

Multi-touch redistributes credit across touchpoints you're already tracking. Incrementality testing measures the true causal impact of a channel by running controlled experiments (geo holdouts, audience splits, on/off tests). Multi-touch is observational (what happened), incrementality is experimental (what caused it). You need both for complete understanding, but if you can only do one, incrementality testing reveals more about true channel value.

Will switching to multi-touch attribution improve my ROAS?

No. Attribution models are accounting systems, not performance optimisations. They change how you measure and allocate credit, but they don't change underlying campaign performance. Better attribution can lead to better budget decisions, which might improve ROAS, but the model itself doesn't make ads perform better.

Making Attribution Work for Your Team

The attribution model debate often misses the point. The goal isn't to pick the "best" model in abstract terms. It's to choose the model that makes your specific growth team faster and more confident in budget decisions, given your actual data volume, channel mix, and analytical resources.

For most mobile app marketers, especially those scaling from ₹30,000 to ₹150,000 monthly spend, last-click attribution provides the clarity needed to optimise campaigns weekly without getting lost in attribution philosophy. Multi-touch becomes valuable at higher scale and complexity, but only if you have the data volume and analytical bandwidth to extract actionable insights from journey-level patterns.

The key is choosing deliberately based on diagnostic questions about your conversion volume, channel count, journey complexity, and team structure, rather than defaulting to what sounds most sophisticated or copying what larger competitors use at different scale.

If you're evaluating attribution options and want a platform that lets you start with single-touch simplicity but add multi-touch views when your funnel data proves it's needed, request a demo from Linkrunner. Our unified attribution dashboard supports both models without requiring infrastructure changes, giving you flexibility to match attribution logic to your team's decision cadence as you scale from early growth to multi-channel optimisation.