Your startup just raised ₹50 lakh in pre-seed funding. The board wants growth, but you're bootstrapping user acquisition with ₹75,000 per month. Your founder asks: "Do we really need to spend ₹60,000 annually on an MMP when we're only spending ₹9 lakh on ads?"

You check AppsFlyer pricing: ₹2.8 lakh per year minimum. Branch: ₹3.2 lakh. Adjust: Similar pricing with seat limits. That's 30-40% of your entire annual marketing budget going to the measurement tool.

You search for alternatives. Blog posts recommend "just use Google Analytics and UTM parameters." Your developer says "we can build this ourselves in a weekend." Your growth lead says "we need proper attribution or we're flying blind."

Who's right?

This guide provides practical attribution strategies for apps spending under ₹2 lakh monthly, covering free tier approaches, DIY limitations, and the specific spend thresholds where proper attribution shifts from nice-to-have to essential.

The Low-Budget Attribution Dilemma: Do You Really Need an MMP Yet?

The honest answer: it depends on your acquisition complexity and decision-making needs.

You probably don't need an MMP yet if:

• You're spending under ₹25,000 monthly on 1-2 channels (Meta and Google)

• You're running broad awareness campaigns without optimisation

• You have no in-app monetisation yet (pre-revenue)

• Your team makes budget decisions monthly or quarterly, not weekly

• You're fine with approximate attribution ("roughly 60% from Meta, 40% from Google")

You probably DO need attribution if:

• You're spending ₹50,000+ monthly across 3+ channels

• You're running multiple campaigns per channel that need comparison

• You have in-app revenue events (purchases, subscriptions, transactions)

• Your team needs weekly campaign-level performance data

• You're testing creative variations or audience segments that require measurement

• You've already tried DIY solutions and they're breaking

The transition point for most apps: ₹50,000-₹75,000 monthly spend. Below this, you can likely manage with platform dashboards and basic tracking. Above this, the cost of wrong decisions exceeds the cost of proper attribution.

What Attribution Actually Costs: Breaking Down the Real Numbers

Legacy MMP Pricing (What You're Trying to Avoid)

AppsFlyer, Branch, Adjust pricing (2025 typical):

• Base platform fee: ₹2.5-3.5 lakh annually

• Seat-based add-ons: ₹25,000-50,000 per additional user

• Data export fees: ₹40,000-80,000 annually for API access

• Fraud protection: ₹60,000-1.2 lakh annually (often required add-on)

• Support tier upgrades: ₹40,000+ for dedicated support

Total cost for small team: ₹3-5 lakh first year, ₹2.5-4 lakh annually thereafter

For a startup spending ₹9 lakh annually on ads, that's 33-55% of marketing budget going to measurement tools.

Modern MMP Pricing (What's Actually Accessible)

Usage-based MMPs (Linkrunner, similar platforms):

• No base platform fee

• ₹0.80 per attributed install

• No seat limits

• No data export fees

• Fraud protection included

Example costs by monthly spend:

For apps spending under ₹2 lakh monthly, usage-based pricing costs ₹2,000-8,000 monthly versus ₹20,000-30,000 monthly for legacy MMPs (amortized annual costs).

Free Tier MMPs

Most modern MMPs offer free tiers:

• Linkrunner: 3,000 attributed installs/month free forever, full feature access

• Others: Variable free limits (often 1,000-5,000 installs with feature restrictions)

Free tiers work when:

• Your monthly install volume stays under limits (typically ₹25,000-40,000 monthly spend)

• You don't need advanced features locked behind paid tiers

• You're comfortable with platform dependency (if you scale beyond free tier, migration costs appear)

The DIY Attribution Trap: Why UTM + GA4 Eventually Breaks

Many lean teams start with DIY attribution: "We'll just use UTM parameters and Google Analytics." This works initially but breaks predictably at scale.

What Works in DIY Attribution

Week 1-4: You set up UTM parameters on Meta and Google campaigns:

• utm_source=meta

• utm_medium=cpc

• utm_campaign=launch_awareness

You check Google Analytics. You see sessions by source. You think "this is fine, we're tracking attribution."

Week 5-12: You add more campaigns. You see sessions converting to installs. You export CSVs weekly. Your spreadsheet has 47 rows. Someone asks "what's our true CAC by campaign?" You spend 3 hours reconciling Meta spend data with GA4 install data. The numbers don't match.

Week 13-24: You have 8 active campaigns across 3 networks. GA4 shows "(not set)" for 30% of installs. Meta's dashboard shows different install counts than GA4. You hired a growth marketer who keeps asking "which creative is working?" You can't answer because creative-level attribution requires data you don't have.

Why DIY Attribution Breaks

Problem #1: Web-to-App Attribution Gaps

Users click your Instagram ad (web), get redirected to App Store (iOS), install your app (mobile). GA4 loses attribution connection at the App Store redirect. Your MMP shows the install came from "organic" or "(direct)" instead of your ₹15,000 Meta campaign.

Result: 30-50% of paid installs misattributed to organic, causing underinvestment in working campaigns.

Problem #2: Cross-Device Attribution

User clicks your Google ad on desktop at work, installs your app on mobile phone at home 4 hours later. GA4 can't connect these sessions without user login. Attribution fails.

Result: Desktop ads show zero conversions, causing premature campaign pauses despite driving 20% of installs.

Problem #3: Platform Dashboard Discrepancies

Meta reports 2,847 installs from your campaign. Google Analytics reports 2,104 installs with utm_source=meta. Which is correct? Neither? Both?

Without a single source of truth (SSOT), you have three conflicting datasets:

• Meta Events Manager: 2,847 installs

• Google Analytics: 2,104 installs

• Your finance spreadsheet: ₹180,000 spend ÷ 2,500 assumed installs = ₹72 CPI

Problem #4: No In-App Event Attribution

You track installs by source, but you need to know which campaigns drive purchases, not just app downloads. GA4 can track in-app events, but connecting those events back to ad campaigns requires manual UTM parameter passing through your entire onboarding flow.

Implementation time: 2-4 weeks engineering. Maintenance: Ongoing as you add features or change flows.

Problem #5: No Postback Configuration

Meta's algorithm wants to optimise toward your "purchase" event, not just installs. But Meta can't receive purchase events from GA4. You need to configure postbacks (automated event forwarding from your MMP to Meta) to enable value-based bidding.

DIY solution: Write custom server code to forward GA4 events to Meta's Conversions API. Complexity: High. Maintenance burden: Ongoing.

Across early-stage apps we've worked with, teams spending 80-120 hours over 6 months trying to make DIY attribution work before switching to proper MMPs. At ₹1,000/hour fully-loaded cost (developer time + opportunity cost), that's ₹80,000-1.2 lakh in hidden costs.

Attribution Approach #1: Free Tier MMPs (3,000 Installs/Month)

When This Works

Free tier MMPs are ideal when:

• Monthly attributed install volume: < 3,000 (roughly ₹25,000-40,000 monthly spend)

• You need proper attribution but have near-zero budget

• You want professional-grade tracking without payment commitment

• You may scale beyond free tier in 6-12 months

What You Get in Free Tiers

Linkrunner free tier (3,000 installs/month):

• Full attribution across all channels (Meta, Google, TikTok, organic, etc.)

• Dynamic and deferred deep links unlimited

• In-app event tracking with no parameter limits

• Campaign-level ROAS and retention dashboards

• Postback configuration to ad networks

• Fraud detection (click spam, bot filtering)

• All integrations (GA4, Mixpanel, CleverTap, etc.)

• No time limits, no feature lockouts

What you DON'T get:

• Installs beyond 3,000/month (pay-as-you-go pricing starts at ₹0.80 per additional install)

• Dedicated support (community support available)

• Custom SLAs or white-label options

Implementation Strategy

To maximize free tier value:

Week 1: Sign up and implement SDK (2-4 hours engineering time)

Week 2: Create attribution links for all active campaigns, configure postbacks to Meta/Google

Week 3: Track core in-app events (activation, purchase, subscription)

Week 4: Build weekly reporting dashboard showing:

• Installs by channel and campaign

• Cost per install (CPI) by source

• Event completion rates by acquisition source

• Campaign-level ROAS if revenue exists

Migration Path

If you grow beyond 3,000 installs monthly:

• Option 1: Stay on same platform, pay for overages at ₹0.80 per install

• Option 2: Migrate to usage-based billing (automated, no data loss)

• Option 3: Migrate to different platform (requires SDK swap, 2-4 week transition)

Attribution Approach #2: Manual UTM + Spreadsheet Tracking (What Works and What Breaks)

When This Approach Works

Viable scenarios:

• Spending under ₹20,000 monthly on 1-2 channels

• Running only branded search campaigns (high intent, short consideration)

• Pre-revenue stage with no in-app conversion tracking needed

• Measuring at channel level only ("Meta vs Google"), not campaign level

What you can measure:

• Approximate install attribution by source (within 20-30% accuracy)

• Blended CPI across channels

• Top-level funnel metrics (installs, sessions, signups)

What you CAN'T measure:

• Campaign-level or creative-level performance

• Cross-device attribution (desktop click → mobile install)

• Deferred deep linking (install → route to specific content)

• Post-install events attributed to acquisition source

• Real-time attribution (data lags 24-48 hours)

• Fraud filtering (bots, click spam)

Implementation Guide

If you're committed to DIY attribution despite limitations:

Step 1: Standardize UTM naming (Day 1)

Create consistent UTM structure:

Step 2: Implement tracking (Day 2-3)

Add UTM parameters to all campaign links:

• Meta: Use dynamic parameters in URL builder

• Google: Use ValueTrack parameters in finals URLs

• TikTok: Manual UTM addition to destination URLs

Step 3: Set up GA4 conversion tracking (Day 4-7)

Configure GA4 to track:

• install: App first open event

• signup_complete: User registration

• purchase or subscribe: Revenue events

Connect GA4 to your mobile app via Firebase SDK.

Step 4: Build reporting spreadsheet (Day 7-10)

Weekly data pull template:

Step 5: Reconcile discrepancies (Weekly ongoing)

Compare numbers across:

• Meta Events Manager reported installs

• GA4 reported installs with utm_source=meta

• Your actual install count from app stores

Expect 15-35% variance. Use best judgment to reconcile.

Why This Eventually Breaks

At ₹40,000 monthly spend:

• You have 4-6 active campaigns

• Spreadsheet reconciliation takes 4-6 hours weekly

• Attribution accuracy drops to 60-75% (acceptable)

At ₹75,000 monthly spend:

• You have 8-12 active campaigns across 3 networks

• You need campaign-level optimization decisions twice weekly

• Reconciliation takes 8-12 hours weekly

• Attribution accuracy drops to 50-65% (problematic)

• Your growth lead threatens to quit because "we're flying blind"

At ₹1,50,000 monthly spend:

• DIY attribution is actively harming growth

• You're making ₹50,000-75,000 monthly budget decisions based on 50% accurate data

• Opportunity cost of wrong decisions exceeds MMP cost

Most teams switch to proper MMPs between ₹50K-₹1L monthly spend when spreadsheet complexity exceeds human capacity.

Attribution Approach #3: Platform-Native Attribution (Meta + Google + TikTok Dashboards)

When This Approach Works

Viable when:

• You completely trust each platform's self-reported metrics

• You're fine with inflated numbers (platforms over-report their own performance)

• You don't need cross-platform comparison

• You're not trying to prevent double-counting across channels

What Platform Dashboards Show

Meta Events Manager:

• Installs Meta attributes to Meta campaigns (including view-through, which inflates numbers)

• Cost per install from Meta's perspective

• In-app events if you configure the Meta SDK

Google Ads:

• Installs Google attributes to Google campaigns

• Cost per install from Google's perspective

• Conversion tracking via Firebase or Google Analytics

TikTok Ads Manager:

• Installs TikTok attributes to TikTok campaigns

• Cost per install from TikTok's perspective

• In-app event tracking via TikTok SDK

The Double-Counting Problem

User journey:

1. Sees your Meta ad (impression)

2. Searches your brand on Google (paid click)

3. Clicks your TikTok ad next day

4. Installs your app

What platforms report:

• Meta claims the install (view-through attribution)

• Google claims the install (last paid click)

• TikTok claims the install (last click overall)

Your spreadsheet:

• 3 installs reported

• 1 actual install occurred

• 200% over-counting

Without independent attribution (MMP as single source of truth), you can't deduplicate across platforms.

When Platform-Native Attribution Works

It's acceptable when:

• You're running awareness campaigns without ROI pressure

• You care about reach and impressions, not precise attribution

• You're spending under ₹20,000 monthly on a single channel

• You're OK with 30-50% over-reporting

When to Graduate from DIY: The ₹50,000/Month Inflection Point

Most apps hit an inflection point around ₹50,000 monthly spend where DIY attribution shifts from "good enough" to "actively harmful."

Signals You've Crossed the Threshold

Signal #1: Decision Paralysis

You have ₹50,000 budget and 6 campaigns. You need to shift ₹15,000 from weak campaigns to strong campaigns, but you can't confidently identify which is which because attribution data is ambiguous.

Signal #2: Revenue Disconnect

You're generating in-app revenue (purchases, subscriptions, transactions) but you can't attribute revenue back to acquisition sources. You know overall ROAS is 2.1× but you don't know if Meta is 3.5× and Google is 0.8×, or vice versa.

Signal #3: Team Friction

Your growth marketer and developer spend 6 hours weekly arguing about "what the real numbers are" instead of optimising campaigns.

Signal #4: Opportunity Cost

You're spending 10-15 hours weekly on manual attribution reconciliation. At ₹1,000/hour fully-loaded cost, that's ₹40,000-60,000 monthly in opportunity cost, which exceeds the ₹2,000-6,000 cost of proper attribution.

Signal #5: Creative Testing Needs

You want to test 4 creative variations across 3 audience segments. That's 12 combinations requiring performance tracking. DIY attribution can't provide creative-level data without massive manual effort.

Cost-Benefit Breakeven Analysis

Scenario: ₹75,000 monthly spend, 3,750 monthly installs

DIY attribution cost:

• 12 hours weekly reconciliation × ₹1,000/hour = ₹48,000 monthly

• Plus 20-30% attribution error causing suboptimal budget allocation

• Estimated cost of wrong decisions: ₹15,000-22,000 monthly

• Total DIY cost: ₹63,000-70,000 monthly

Proper MMP cost:

• Usage-based: 3,750 installs × ₹0.80 = ₹3,000 monthly

• Time saved: 12 hours weekly back to productive work

• Decision quality improvement: 15-25% better budget allocation

• Net benefit: ₹60,000-67,000 monthly

Breakeven occurs when MMP cost equals opportunity cost of DIY + cost of wrong decisions, typically around ₹50,000-75,000 monthly spend.

What You Actually Need at ₹50K, ₹1L, and ₹2L Monthly Spend

At ₹50,000 Monthly Spend (2,500 installs)

Minimum requirements:

• Accurate install attribution by campaign

• Basic in-app event tracking (signup, first action)

• Weekly reporting showing CPI and activation rate by source

• Postback configuration to ad networks

Nice to have:

• Fraud detection

• Cross-platform deduplication

• Real-time dashboards

Solution: Free tier MMP or basic usage-based MMP (₹2,000/month)

At ₹1,00,000 Monthly Spend (5,000 installs)

Minimum requirements:

• Everything from ₹50K tier

• Revenue event attribution (purchases, subscriptions)

• Campaign-level ROAS visibility

• Creative-level performance tracking

• Daily data refresh for optimization

Nice to have:

• Cohort analysis by channel

• Fraud prevention (click spam, bot detection)

• Deep linking for campaign-specific landing experiences

Solution: Usage-based MMP (₹4,000/month), potentially with some advanced features

At ₹2,00,000 Monthly Spend (10,000 installs)

Minimum requirements:

• Everything from ₹1L tier

• Advanced cohort analysis (retention by source)

• Fraud prevention (required, not optional)

• Multi-touch attribution or incrementality testing

• API access for custom reporting

• Deep linking with deferred routing

Nice to have:

• Predictive LTV modeling

• Custom conversion value configuration (SKAN)

• White-label reporting for stakeholders

Solution: Full-featured usage-based MMP (₹8,000/month) or entry-level legacy MMP if scaling beyond ₹3L/month

Cost-Benefit Analysis: Attribution Spend as Percentage of Marketing Budget

What percentage of marketing budget should attribution cost?

Industry benchmarks:

• 3-5% at scale: Apps spending ₹10L+ monthly typically spend 3-5% on measurement and analytics tools

• 8-12% when scaling: Apps spending ₹2-5L monthly often spend 8-12% as they build measurement infrastructure

• >15% is excessive: If attribution costs exceed 15% of marketing spend, you're overpaying

Your calculation:

If your attribution cost falls in the "reasonable range", you're probably optimizing correctly. If you're above the "red flag" threshold, you're either overpaying or using the wrong solution for your scale.

Free vs Paid Attribution: Feature Comparison and Decision Framework

Feature Comparison

*Free tier up to 3,000, then ₹0.80 per additional install

Decision Framework

Use this decision tree:

If monthly spend < ₹25,000:

→ Start with platform-native dashboards (Meta, Google)

→ Acceptable to have rough attribution

If monthly spend ₹25,000-50,000:

→ Implement free tier MMP

→ Get professional attribution without cost

If monthly spend ₹50,000-1,00,000:

→ Usage-based MMP (₹2,000-5,000/month)

→ Attribution quality justifies cost

If monthly spend ₹1,00,000-3,00,000:

→ Full-featured usage-based MMP (₹5,000-15,000/month)

→ Advanced features (fraud, cohorts, API) become essential

If monthly spend >₹3,00,000:

→ Compare usage-based vs enterprise MMPs

→ Negotiate pricing based on volume

Implementation Strategy: Starting with Free Tier and Scaling Strategically

Phase 1: Free Tier Start (₹0-50,000 monthly spend)

Month 1-3:

• Implement free tier MMP

• Migrate existing campaigns to attributed links

• Set up core in-app events

• Configure postbacks to ad networks

• Build reporting dashboards

Success criteria:

• All campaigns properly attributed

• Weekly campaign performance reports

• Decision confidence increases

Phase 2: Light Usage (₹50,000-1,00,000 monthly spend)

Month 4-9:

• Scale spend gradually

• Install volume exceeds free tier limits

• Begin paying for overages (₹1,000-4,000/month)

• Add fraud detection and deep linking

Success criteria:

• Attribution accuracy >90%

• Clear ROAS visibility by campaign

• Fraud blocked before affecting spend

Phase 3: Full Scale (₹1,00,000-2,00,000 monthly spend)

Month 10+:

• Full attribution infrastructure

• Advanced features (cohorts, API, custom reporting)

• Monthly cost ₹5,000-10,000

• Attribution cost 4-6% of marketing spend

Success criteria:

• Data-driven budget reallocation weekly

• Attribution directly driving ROAS improvements

• Team confident in performance metrics

Common Mistakes: Where Bootstrapped Teams Waste Money on Attribution

Mistake #1: Paying for Enterprise Features Too Early

Many teams sign annual contracts for legacy MMPs with features they don't need (white-label reporting, dedicated account managers, custom SLAs) when spending ₹1-2L monthly.

Fix: Start with usage-based pricing, upgrade only when features justify cost.

Mistake #2: Building Custom Attribution Systems

Some teams spend ₹1.5-3 lakh in developer time building custom attribution using BigQuery, Airflow, and event pipelines, then spend ₹50,000-1 lakh annually maintaining it.

Fix: Use off-the-shelf MMPs unless you're spending ₹20L+ monthly and have specific needs no MMP addresses.

Mistake #3: Ignoring Fraud Until It's Too Late

Teams without fraud detection often discover 15-30% of attributed installs are fraudulent (click spam, bot farms, device farms) after spending ₹2-5 lakh.

Fix: Implement fraud detection from day one, even on free tiers.

Mistake #4: No Attribution Until Revenue Exists

Some founders say "we'll add attribution once we monetise." By the time revenue exists, they've already wasted ₹1-3 lakh on unattributed campaigns.

Fix: Implement basic attribution at ₹50,000 monthly spend, even pre-revenue.

How Linkrunner Supports Low-Budget Teams

Linkrunner was built specifically for growing apps that can't justify ₹2-5 lakh annual attribution costs:

Free tier (3,000 installs/month):

• Full feature access (no premium lockouts)

• Unlimited dynamic and deferred deep links

• Complete attribution across all channels

• In-app event tracking unlimited

• Fraud detection included

• All integrations available

Usage-based pricing beyond free tier:

• ₹0.80 per attributed install

• No seat limits, no base fees

• No data export charges

• Fraud protection included

Example costs:

For apps spending ₹50,000-2,00,000 monthly, Linkrunner costs ₹0-13,600 monthly versus ₹20,000-30,000 monthly for legacy MMPs.

Key Takeaways

Bootstrapped and lean teams need attribution strategies that match their budget constraints:

Under ₹25,000 monthly spend:

• Platform-native dashboards sufficient

• Rough attribution acceptable

• No MMP needed yet

₹25,000-50,000 monthly spend:

• Free tier MMPs ideal

• Professional attribution without cost

• Avoid DIY spreadsheet hell

₹50,000-1,00,000 monthly spend:

• Usage-based MMPs justified

• Cost: ₹2,000-5,000 monthly

• Attribution quality drives better decisions

₹1,00,000-2,00,000 monthly spend:

• Full-featured attribution essential

• Cost: ₹5,000-10,000 monthly

• 4-6% of marketing spend

For low-budget teams ready to implement professional attribution without enterprise pricing, request a demo from Linkrunner to see how free tier access and usage-based pricing can deliver attribution quality that scales with your growth.