You're a growth lead at a mobile app doing 50,000 installs a month. Your team tracks Meta, Google, and maybe TikTok. You're using last-click attribution, and every marketing article you read says you're doing it wrong.

"Multi-touch attribution is the only way to understand your funnel." "Last-click over-credits bottom-funnel channels and ignores the awareness layer." "You need data-driven attribution to compete in 2025."

Here's what those articles won't tell you: most growing apps waste more money implementing complex attribution models than they lose to last-click's supposed inaccuracy. The measurement sophistication you're being sold often creates more problems than it solves.

If you're spending under ₹80 lakh ($100,000) per month on user acquisition, running 2-4 primary channels, and working with a lean marketing team, last-click attribution mobile apps use is probably the right choice. Not as a temporary compromise, but as the optimal model for your current stage.

This isn't about defending lazy measurement. It's about matching model complexity to decision-making reality.

The Attribution Model Anxiety: Why Everyone Says You're Doing It Wrong

The mobile marketing industry has developed a specific narrative around attribution models: simple models are for beginners, sophisticated models are for serious marketers, and if you're not using multi-touch or data-driven attribution, you're leaving money on the table.

This narrative benefits three groups: enterprise MMPs selling complex features, agencies justifying consulting fees, and content marketers writing comparison posts. It doesn't necessarily benefit you.

The pressure comes from multiple directions. MMP sales teams demo multi-touch dashboards showing "hidden value" in upper-funnel channels. Conference speakers present case studies where switching from last-click to position-based attribution "unlocked 30% more efficient spend." LinkedIn posts claim last-click attribution is bad practice that ignores the customer journey.

Here's the reality check: most of those case studies come from brands spending ₹4-8 crore ($500,000-$1,000,000) monthly across 8-12 channels with dedicated attribution analysts and data science teams. Their measurement needs are fundamentally different from yours.

When a fintech app running Meta and Google with a two-person growth team tries to implement the same attribution sophistication, they end up with dashboards they don't trust, budget allocation paralysis, and engineering sprints spent building reporting infrastructure instead of product features.

The anxiety around attribution models creates a predictable pattern. Teams adopt last-click because it's the default. They read that it's insufficient. They switch to multi-touch. They discover the data is messy and the insights are unclear. They either revert to last-click quietly or continue using multi-touch while making decisions based on platform-reported metrics anyway.

This cycle wastes time and erodes trust in measurement systems.

What Last-Click Actually Measures (and Why That's Often Enough)

Last-click attribution is direct: it credits the final touchpoint before an install or conversion. If someone clicks a TikTok ad at 2pm and installs your app at 2:05pm, TikTok gets full credit. If they saw a Meta ad yesterday, clicked a Google ad this morning, and installed from an organic search this afternoon, organic search gets the credit.

Critics argue this approach ignores the "upper funnel journey" and under-values awareness channels. That criticism is technically correct and practically irrelevant for most growing apps.

Here's why last-click works well in mobile app marketing specifically. Mobile conversion windows are short. Unlike B2B SaaS where someone might research for weeks, most app installs happen within hours of the decisive touchpoint. The median time from last click to install is under 30 minutes for performance campaigns.

This compressed timeline means the "journey" attribution evangelists describe often doesn't exist. A user sees your ad, decides whether they're interested, and either installs immediately or never thinks about your app again. There's no multi-week consideration process with 7 touchpoints across 4 channels.

The channel landscape in mobile is also naturally separated. Someone running Meta, Google, and organic ASO isn't creating overlapping awareness campaigns where attribution overlap is common. They're running distinct acquisition motions: Meta for cold audience prospecting, Google for search intent capture, ASO for brand and category searches.

When channels serve different functions, last-click attribution provides clear signal about which channel drove the final conversion decision. If Google UAC consistently shows lower cost-per-install but higher D7 retention than Meta, that's actionable intelligence regardless of whether someone saw a Meta ad two days ago.

Last-click also matches how most teams actually make budget decisions. You're not typically asking "what's the incrementality of our Meta spend when accounting for cross-channel interaction effects?" You're asking "should I spend more on Meta or Google next week based on which is delivering better unit economics?"

For that decision, last-click gives you the signal you need: which channel is delivering users who complete high-value actions at acceptable acquisition costs.

When Last-Click Outperforms: Clear Channel Separation, Short Sales Cycles, Budget Under ₹80 Lakh/Month

Last-click attribution becomes the practical choice under specific conditions. These aren't edge cases; they describe most mobile apps in growth stage.

Clear Channel Separation

When your channels target distinct user behaviours, overlap is minimal and multi-touch adds little value. A gaming app running Meta prospecting to cold audiences, Google App Campaigns for search intent, and Apple Search Ads for brand terms has naturally separated funnels.

Users discovering your game through Meta aren't later clicking Google UAC ads before installing. They're different user cohorts acquired through different mechanisms. Last-click correctly attributes each install to the channel that actually drove it.

The exception is retargeting, where someone sees a Meta prospecting ad, doesn't install, then later clicks a Meta retargeting ad and converts. But even here, last-click (crediting retargeting) tells you which campaign variant closed the deal, which is often the decision-relevant insight.

Short Sales Cycles

Mobile apps with immediate install decisions benefit from last-click's simplicity. If 80% of your installs happen within 2 hours of ad exposure, the "customer journey" is effectively a single touchpoint regardless of your attribution model.

D2C shopping apps, casual games, utility apps, and most consumer categories fit this pattern. Someone sees an ad, evaluates interest in 15 seconds, and either installs now or never. Multi-touch attribution looking for hidden influences across a 7-day window finds mostly noise.

Compare this to enterprise software where a prospect might read a blog post, attend a webinar, talk to sales, request a demo, and convert 45 days later. That journey benefits from multi-touch modeling. Your app install funnel doesn't have that complexity.

Budget Under ₹80 Lakh Monthly

Below ₹80 lakh ($100,000) monthly spend, the incremental value from multi-touch attribution rarely justifies the cost in engineering time, tool complexity, and analysis overhead.

At this budget level, you're typically running 2-4 primary channels. Your biggest optimization opportunities are creative testing, audience refinement, and bid strategy, not attribution model sophistication. Adding multi-touch capabilities means either paying more for your MMP, building custom attribution logic, or spending analyst time reconciling conflicting reports.

That investment delivers marginal returns. The difference between last-click and multi-touch might shift 5-10% of credit across channels. If that reallocation improves efficiency by 3-5%, you've gained ₹2-4 lakh in monthly value while spending ₹1-2 lakh extra in measurement costs and 20 hours of team time monthly.

The math doesn't work until you're operating at significantly larger scale where small efficiency gains compound into meaningful budget savings.

The Hidden Costs of Multi-Touch: Engineering Time, Analysis Paralysis, Marginal Accuracy Gains

Multi-touch attribution sounds sophisticated in theory. In practice, it introduces costs that advocates rarely quantify.

Engineering and Implementation Overhead

Most MMPs offer multi-touch attribution as a premium feature. Implementing it requires additional SDK configuration, event taxonomy standardisation, and often custom postback logic to send multi-touch data to ad networks (which mostly ignore it and optimise on last-click anyway).

For a lean team, this represents 1-2 weeks of engineering sprint capacity. That's development time not spent on product features, conversion optimisation, or other revenue-driving work. The opportunity cost is real.

Teams also discover that multi-touch models require more careful data hygiene. If your event naming isn't perfectly consistent, if postbacks occasionally fail, or if you have any attribution window mismatches, multi-touch reports become unreliable quickly. Last-click is more forgiving of minor implementation gaps.

Analysis Paralysis and Trust Erosion

When you switch to multi-touch attribution, you often get conflicting signals across different model types. Position-based shows Meta driving 40% of value. Time-decay shows Google at 45%. Data-driven (if your MMP offers it) shows yet another distribution.

Which model do you trust? The answer is usually "whichever confirms my existing hypothesis," which defeats the purpose of sophisticated measurement.

This creates a specific dysfunction. Your team looks at multi-touch dashboards, sees confusing results, and reverts to checking last-click numbers or platform-reported metrics because those feel more concrete. You're paying for complexity you don't use.

Budget allocation discussions become harder, not easier. Instead of "Google delivered 12,000 installs at ₹95 CPI with 18% D1 retention," you're debating "well, the position-based model gives Google 32% credit but time-decay gives 41%, and we need to decide whether early-funnel touches should count as 20% or 30%."

These conversations waste time and rarely lead to better decisions.

Marginal Accuracy Gains

The dirty secret of attribution modeling is that all models are wrong. Last-click is wrong by over-crediting final touchpoints. Multi-touch is wrong by making untestable assumptions about how earlier exposures influenced later conversion decisions.

The question isn't "which model is accurate?" but "which model is useful for the decisions we need to make?"

For most growing apps, multi-touch attribution improves accuracy by perhaps 5-15% in specific scenarios (heavy retargeting overlap, long consideration windows, many channels) while adding 40-60% more complexity to your measurement stack.

That trade-off makes sense for sophisticated teams with dedicated analysts. It's value-destructive for lean teams trying to move fast.

Real-World Evidence: Apps Scaling to ₹8 Crore ARR with Last-Click Attribution

Theory suggests multi-touch is superior. Practice shows many successful apps scale efficiently on last-click.

A casual gaming app in India reached 300,000 DAU using last-click attribution across Meta, Google, and Unity Ads. Their growth team was three people: a performance marketer, a creative designer, and a part-time analyst. They tracked CPI, D1 retention, D7 ROAS, and made weekly budget allocation decisions based on which channels delivered users who monetised fastest.

Their MMP offered multi-touch attribution. They tested it for two months and found it told them to shift 8% more budget to Meta for "upper funnel influence." They ran the test, saw no improvement in blended unit economics, and reverted to last-click. The gaming app crossed ₹6 crore ($750,000) annual revenue still using single-touch attribution.

A fintech app running Meta and Google with ₹25 lakh monthly budget tried implementing position-based attribution after reading that last-click attribution is bad. They spent three weeks configuring it, discovered their event taxonomy wasn't clean enough for reliable multi-touch reporting, fixed the taxonomy, re-ran analysis, and found the new model suggested 5% budget reallocation toward Google.

They made the shift. ROAS stayed flat. Two months later, they realised they'd spent 40 hours of combined team time to make a marginal budget change that delivered no measurable improvement. They kept last-click and redirected that analysis time toward creative testing, which improved CTR by 18%.

These patterns repeat. Apps using last-click attribution aren't leaving massive value on the table. They're making practical trade-offs between measurement sophistication and operational simplicity.

The companies that benefit most from multi-touch tend to share specific characteristics: monthly budgets above ₹1.6 crore ($200,000), 6+ active channels, dedicated attribution analysts, long consideration windows (7+ days from first touch to install), and heavy investment in brand awareness campaigns where cross-channel influence is genuinely complex.

Most mobile apps don't fit that profile.

When You Actually Need Multi-Touch (and How to Know You're Ready)

Last-click isn't optimal forever. Specific conditions signal it's time to consider attribution model complexity.

Monthly Budget Exceeds ₹1.6 Crore

At ₹1.6 crore+ monthly spend, small efficiency improvements compound into significant value. If multi-touch attribution helps you reallocate 8% of budget toward higher-efficiency channels, that's ₹12 lakh monthly savings. The engineering and analysis costs become justified.

Budget scale also usually means more channels, which increases attribution overlap and makes multi-touch modeling more valuable.

Running 6+ Active Channels

When you're running Meta, Google, TikTok, Apple Search Ads, programmatic display, influencer campaigns, and affiliate networks simultaneously, channel overlap becomes common enough that multi-touch provides genuine insight.

Users might see a TikTok ad, click a Meta retargeting ad, search your brand on Google, and install via an influencer link. Last-click credits the influencer. Multi-touch shows the contribution chain. That visibility becomes decision-relevant when you're optimising a complex channel mix.

Heavy Brand/Awareness Investment

Apps running significant brand campaigns (TV, out-of-home, content marketing, PR) alongside performance channels benefit from multi-touch attribution that can connect brand exposure to eventual performance-driven conversions.

If you're spending ₹40 lakh monthly on brand awareness with the hypothesis it improves performance channel efficiency, you need multi-touch data to validate (or disprove) that hypothesis.

You Have Dedicated Attribution Analysts

Multi-touch attribution requires ongoing analysis to be useful. If you have team members whose job is attribution modeling, data validation, and insight generation, they can extract value from sophisticated models.

If your performance marketer is also your attribution analyst, creative strategist, and campaign manager, multi-touch just adds noise to their workflow.

Platform-Reported Metrics Conflict Significantly

When Meta claims 15,000 installs but Google claims 12,000 for the same period and your MMP shows 20,000 total installs with last-click, you have an attribution overlap problem. Multi-touch modeling helps reconcile these conflicts by distributing credit across touchpoints.

If platform numbers roughly align with your last-click MMP data (within 10-15% variance), overlap isn't significant enough to justify model complexity.

A Practical Decision Framework: Model Complexity vs Team Capability

Choosing an attribution model isn't about finding the "correct" answer. It's about matching measurement sophistication to team capability and decision-making needs.

The Attribution Complexity Matrix

Map your situation across two dimensions: budget scale and team sophistication.

Low budget (under ₹80 lakh/month) + small team (1-3 people) = Last-click attribution. Your optimization opportunities are creative, audience, and bid strategy, not attribution modeling. Keep measurement simple so you can move fast on high-impact levers.

Medium budget (₹80 lakh to ₹1.6 crore/month) + growing team (3-6 people) = Last-click with selective multi-touch testing. Start experimenting with position-based or time-decay models for specific high-overlap scenarios (retargeting campaigns, brand + performance mix) while keeping last-click as your primary model.

High budget (₹1.6 crore+/month) + sophisticated team (6+ with dedicated analysts) = Multi-touch or data-driven attribution. At this scale, model sophistication pays for itself through better budget allocation across complex channel mixes.

The Weekly Decision Test

Ask what decisions you make weekly based on attribution data. If your answer is "reallocate budget between 2-3 channels based on CPI and retention cohorts," last-click provides that signal clearly.

If your answer is "optimise a complex funnel with 8 channels including brand awareness, evaluate cross-channel influence, and model incrementality of upper-funnel spend," you need multi-touch.

Most teams fall into the first category.

The Trust and Usability Filter

Choose the model your team actually trusts and uses. A sophisticated model that generates reports nobody understands is worse than a simple model that drives confident decisions.

If you implement multi-touch and find your team still checking last-click numbers or reverting to platform dashboards for "ground truth," the sophisticated model has failed regardless of its theoretical superiority.

The Incremental Value Calculation

Estimate what you'd gain from better attribution. If moving from last-click to multi-touch might reallocate 5-10% of budget toward more efficient channels, and your current efficiency delta between channels is 15-20%, the potential gain is roughly 1-2% of total spend.

On ₹40 lakh monthly budget, that's ₹40,000-₹80,000 monthly value. Does it justify 30-40 hours of initial setup, 5-10 hours monthly analysis time, and potential MMP cost increases? Probably not.

On ₹2 crore monthly budget, that same improvement is ₹2-4 lakh monthly value. Now the math works.

Why Single-Touch Attribution Effectiveness Often Beats Multi-Touch Confusion

The question "is last-click attribution bad?" misframes the issue. Last-click isn't inherently bad. It's contextually appropriate or inappropriate based on your specific situation.

For most growing mobile apps, last-click attribution is not a limitation you need to overcome. It's a practical choice that lets you focus on higher-impact optimisation work.

The apps that succeed aren't necessarily the ones with the most sophisticated measurement. They're the ones that match measurement complexity to team capability, trust their data enough to make fast decisions, and optimise the levers that actually move outcomes: creative quality, audience targeting, bid strategy, and product-market fit.

Attribution modeling is a means to an end. The end is confident budget allocation that improves unit economics. If last-click gets you there with less overhead and faster decision cycles, it's the right choice.

You can always add model sophistication later when budget scale and team capability justify it. Starting simple and adding complexity as you grow is a better path than starting complex, getting overwhelmed, and reverting to simple anyway.

Frequently Asked Questions

Doesn't last-click ignore the customer journey?

For most mobile apps, the "customer journey" is much shorter than multi-touch advocates assume. Median time from last ad click to install is under 30 minutes for performance campaigns. In that timeframe, the final touchpoint is genuinely the decisive factor. Multi-touch attribution looking for influences across 7-day windows often finds noise, not signal.

What about retargeting campaigns?

Retargeting creates legitimate multi-touch scenarios: someone sees a prospecting ad, doesn't install, sees a retargeting ad, converts. Last-click credits retargeting, which is often the correct insight since retargeting closed the deal. If you want to value prospecting's contribution, segment your analysis by "new users" vs "retargeted users" rather than implementing full multi-touch attribution.

Don't ad networks optimise better with multi-touch data?

Most ad networks (Meta, Google, TikTok) optimise on last-click conversions regardless of what attribution model your MMP uses. They receive postback signals when conversions happen and don't meaningfully factor multi-touch credit into their algorithms. Sending them multi-touch data rarely improves campaign performance because their internal systems still optimise on last-touch.

How do I know if I'm ready for multi-touch?

You're ready when you meet most of these criteria: monthly budget above ₹1.6 crore, running 6+ channels, have dedicated attribution analysts, see significant discrepancy between platform-reported numbers, and make complex decisions about brand + performance mix. If you're running 2-4 channels with a lean team and budget under ₹80 lakh, stick with last-click.

What's the difference between last-click and last-touch attribution?

The terms are often used interchangeably. "Last-click" specifically refers to crediting the final ad click before conversion. "Last-touch" is slightly broader and can include non-click interactions (impressions, email opens) depending on implementation. For mobile app attribution, both typically mean "credit the final measured interaction before install," which is usually an ad click.

Can I use last-click for user acquisition and multi-touch for LTV analysis?

Yes, and this is often the practical compromise. Use last-click attribution to decide which channels drive installs at acceptable CPI. Then layer on cohort analysis showing how users from each last-click source perform over time (retention, revenue, LTV). This gives you clearer signal than full multi-touch attribution while avoiding the complexity.

Isn't data-driven attribution better than both?

Data-driven attribution uses machine learning to determine credit distribution across touchpoints based on observed conversion patterns. It sounds sophisticated but requires massive data volume to work reliably (typically 15,000+ conversions monthly across all channels). Below that threshold, it's often less accurate than simple rule-based models. It's also a black box, which makes it hard to audit or trust.

The Path Forward: Simple First, Sophisticated Later

The mobile marketing industry's default advice is to adopt the most sophisticated attribution model you can afford. That's backwards.

Start with last-click attribution. Make it work reliably: clean event taxonomy, consistent postbacks, trusted dashboards. Use that foundation to make confident budget decisions for 6-12 months. Learn which channels drive quality users at acceptable costs.

When you hit ₹1.6 crore monthly budget, or when you're running 6+ channels with meaningful overlap, or when you have dedicated attribution analysts, consider adding multi-touch capabilities. Test them against last-click. If they generate insights that change decisions and improve outcomes, adopt them. If they just create confusion, stick with last-click.

Attribution sophistication should follow business sophistication, not lead it. Teams that scale efficiently often do so because they kept measurement simple enough to move fast on creative testing, audience refinement, and product optimisation.

Those are the levers that compound. Attribution model choice is a second-order concern that matters far less than most measurement vendors would have you believe.

If you need an attribution platform that lets you start with last-click and add complexity as you grow without migrating tools, platforms like Linkrunner let you make that transition smoothly. The model should match your team's capability, and your measurement stack shouldn't force sophistication before you're ready to use it.

Request a demo from Linkrunner to see how straightforward attribution can work for growing apps without sacrificing the option to scale into more sophisticated models later.