How to Prove Attribution ROI to Finance Teams: A CFO-Ready Framework
Lakshith Dinesh
Updated on: Jan 7, 2026
You've just been asked to justify why your team needs ₹5,000 per month for a mobile measurement partner. Your CFO looks at the line item and asks: "Why are we paying for software to measure our marketing when we can see installs in the app stores for free?"
This conversation happens in hundreds of mobile-first companies every budget cycle. Marketing teams know attribution matters. Finance teams see it as an unexplained cost centre. The gap isn't about who's right—it's about translation. Finance speaks in prevented losses, efficiency gains, and measurable ROI. Marketing speaks in attribution windows, postback accuracy, and campaign optimisation.
Here's the problem: most marketers try to justify attribution tools by explaining what they do ("We track which ads drive installs"). Finance doesn't care about features. They care about financial outcomes. The question isn't "What does this tool do?" It's "What does this cost us if we don't have it?"
This guide translates attribution value into CFO-ready language. You'll get calculation frameworks, scenario-based ROI models, and a one-page business case template designed to win budget approval. If your finance team has ever questioned measurement spend, this is your framework.
The Finance Question: "Why Do We Need to Pay for This?"
When finance teams challenge attribution spend, they're not being difficult. They're doing their job: protecting capital efficiency. From their perspective, attribution tools look like overhead—a tax on marketing, not a driver of returns.
The typical marketing response doesn't help: "We need to know which channels work." Finance hears this as vague justification for discretionary spend. They've already approved the marketing budget. Now you're asking for an additional 3-8% on top to measure it. Without clear financial logic, that looks like cost creep.
Here's what finance actually wants to know:
What revenue or margin are we losing without this?
What's the quantifiable benefit versus the cost?
How do we validate the tool is working after we buy it?
Can we start smaller and prove value before scaling?
Notice these are investment questions, not marketing questions. Finance teams evaluate attribution the same way they evaluate warehouse management systems or fraud detection tools: as operational infrastructure that either prevents losses or creates measurable efficiency gains.
The shift in framing matters. Attribution isn't a "nice to have" dashboard. It's loss prevention software for your marketing budget. Every week without accurate attribution, you're flying blind on budget allocation decisions. Money flows to campaigns you can't verify. Underperforming channels stay funded because you lack proof they're underperforming. High-quality channels get underfunded because you can't isolate their true contribution.
This isn't theoretical. A consumer app spending ₹10 lakh per month on Meta and Google typically wastes 15-25% of budget on misattributed or underperforming campaigns when running without proper attribution. That's ₹1.5-2.5 lakh per month in preventable losses. If an attribution tool costs ₹60-80k per month and prevents even half that waste, the ROI is 150-200% before counting any efficiency gains.
Finance understands this math. What they need from you is the calculation, not the explanation of how attribution works. Your job isn't to educate them on postbacks and attribution windows. Your job is to quantify the cost of bad decisions caused by measurement gaps.
The Cost of Bad Attribution: Budget Waste Quantification Framework
Before you can prove ROI, you need to establish the baseline cost of operating without accurate attribution. This isn't about hypothetical scenarios. It's about quantifying the decision errors that happen when marketing teams lack reliable data.
Budget waste from poor attribution typically shows up in four measurable ways:
1. Continued spend on negative-ROAS campaigns
When you can't see campaign-level profitability, spend continues flowing to campaigns that lose money. A fintech app spending ₹3 lakh per month discovered 35% of their Meta budget (₹1.05 lakh) was allocated to ad sets with D7 ROAS below 0.4x—campaigns that would never break even. They'd been running these for 11 weeks because their dashboard showed "good CPI" but couldn't connect installs to revenue events.
Quantification method: Audit last 8 weeks of spend. Identify campaigns with ROAS below your break-even threshold (typically 0.6-0.8x for most apps). Calculate total spend on these campaigns. Multiply by percentage of time they ran before detection. This is your "negative ROAS leakage."
2. Underinvestment in high-performing channels
Poor attribution hides your best channels. A mobility app found that WhatsApp referral campaigns (tracked via dynamic links) had D30 ROAS of 3.2x, but organic attribution was claiming 60% of those conversions. The referral channel looked marginal (0.9x ROAS), so it stayed underfunded for 5 months while lower-quality paid channels got budget priority.
Quantification method: If you're running any channel where attribution seems "unclear" (influencer, affiliate, QR codes, offline), estimate conservative uplift if you had clear tracking. Multiply potential additional spend by estimated ROAS. The gap between current budget and optimal budget is opportunity cost.
3. Spreadsheet tax and delayed optimisation
Without clean dashboards, marketers spend 8-15 hours per week pulling data from multiple sources, reconciling discrepancies, and building reports. An e-commerce brand calculated their growth team spent 22% of total hours just on "dashboard work"—time that could have been spent on creative testing, audience experiments, or channel expansion.
Quantification method: Track hours spent per week on: pulling campaign data, reconciling platform discrepancies, building reports, explaining data gaps in meetings. Multiply by hourly cost (salary ÷ 2080 hours). Annualise. Add "delayed decision cost"—estimate how much faster you'd optimise with real-time dashboards (typically 3-7 days faster reaction time per month).
4. Multi-touch misattribution and organic inflation
Apps without proper attribution typically over-credit organic by 30-60% because they can't distinguish truly organic installs from paid-influenced ones. A gaming app's analytics showed 70% organic installs. After implementing attribution, actual organic dropped to 35%. The other 35% were misattributed paid installs, which meant their true paid CAC was 2x what they'd calculated, and their budget models were completely wrong.
Quantification method: If your "organic" percentage seems high (above 50% while running active paid campaigns), estimate misattribution rate at 20-40% of reported organic. Recalculate paid CAC with corrected attribution. The difference in budget allocation decisions is your misattribution cost.
Once you've quantified these four areas, you have your baseline. Add them up. This is the monthly cost of bad attribution—the financial leak you're trying to plug with measurement infrastructure.
For most apps spending ₹5-15 lakh per month on user acquisition, this calculation typically yields ₹80k-3 lakh per month in measurable waste or opportunity cost. That's your denominator for ROI calculation.
ROI Calculation Method: (Prevented Waste + Efficiency Gains) ÷ Attribution Cost
The ROI formula for attribution is straightforward once you've quantified the baseline cost of operating without it:
Attribution ROI = [(Prevented Waste) + (Efficiency Gains) - (Attribution Cost)] ÷ (Attribution Cost)
Let's break down each component:
Prevented Waste = Measurable budget leakage you'll eliminate with accurate attribution. This includes:
Spend on negative-ROAS campaigns caught and paused faster (typically 40-60% of the waste identified in your baseline audit)
Corrected multi-touch misattribution reducing inflated organic numbers (usually saves 8-15% of total budget through better CAC calculations)
Fraud detection preventing bot traffic and click spam (typically 3-8% of paid install volume for apps without fraud protection)
Conservative estimate: Use 50% of your quantified baseline waste as "prevented waste." You won't catch everything immediately, but any reasonable attribution setup will eliminate roughly half the major leaks within 60 days.
Efficiency Gains = Time savings and faster optimisation. This includes:
Reduction in manual reporting hours (8-15 hours per week × hourly cost)
Faster campaign optimisation cycles (moving from weekly to daily budget reallocation decisions typically improves ROAS by 12-20% within 90 days for performance-focused teams)
Better creative testing velocity (knowing which ad sets work within 48 hours instead of 14 days means faster iteration—estimate 15-25% improvement in creative hit rate)
Conservative estimate: Value efficiency gains at 50% of the calculated time savings, plus 10% improvement in overall ROAS from faster decisions (multiply your monthly ad spend by 0.10, then divide by 12 months to get a monthly gain value).
Attribution Cost = Actual monthly cost of your attribution platform. For most mobile measurement partners:
Legacy MMPs (AppsFlyer, Adjust, Branch): ₹2.5-6 lakh per month for teams with 50k-200k installs/month
Modern alternatives (Linkrunner): ₹40-80k per month at ₹0.80 per install for the same volume
DIY solutions: Engineering time + infrastructure costs, typically ₹80k-1.5 lakh per month when fully loaded
Use your actual quoted price or, if evaluating options, use the higher end of the range for conservative calculations.
Example Calculation:
Mid-size fintech app spending ₹12 lakh per month on Meta + Google Ads:
Baseline waste identified: ₹1.8 lakh per month (negative ROAS campaigns + misattribution)
Prevented waste (50% of baseline): ₹90,000 per month
Efficiency gains (time savings + 10% ROAS improvement): ₹45,000 (time) + ₹1,20,000 (ROAS gain) = ₹1,65,000 per month
Total benefit: ₹90,000 + ₹1,65,000 = ₹2,55,000 per month
Attribution cost (Linkrunner pricing): ₹65,000 per month
Net benefit: ₹2,55,000 - ₹65,000 = ₹1,90,000 per month
ROI: ₹1,90,000 ÷ ₹65,000 = 2.9x or 192% ROI
Payback period: 15 days. By week three, the tool has paid for itself through prevented losses alone.
This is the language finance understands. Not "better dashboards" or "improved measurement"—prevented losses, efficiency gains, and rapid payback periods measured in weeks, not quarters.
When presenting this calculation, include confidence intervals. Finance teams trust conservative estimates more than optimistic projections. Show the ROI range using pessimistic assumptions (prevent only 30% of waste, gain only 5% ROAS improvement) through to realistic assumptions. Even the pessimistic scenario should show positive ROI within 60-90 days for any team spending above ₹3 lakh per month on user acquisition.
Example Scenario: Team Spending ₹1,50,000/Month on User Acquisition
This is where attribution shifts from "should we?" to "which one?" Finance teams at this budget level expect measurement infrastructure to be in place. The question isn't whether to invest in attribution—it's whether the current solution is cost-efficient or whether switching would improve ROI.
Budget Profile:
Monthly ad spend: ₹1,50,000
Typical install volume: 8,000-15,000 paid installs per month (varies by CPI)
Channels: Meta + Google + at least one experimental channel (TikTok, Apple Search Ads, influencer, affiliate, or offline)
Team size: 2-4 people managing UA, performance marketing, and growth
Attribution maturity: Often already using some measurement tool, but may be expensive, incomplete, or frustrating to operate
Baseline waste calculation:
Negative-ROAS leakage: With 3-5 active channels and 10-20 campaigns running simultaneously, misallocation risk increases. Teams at this scale often have 2-3 campaigns running with ROAS below break-even for 4-8 weeks before detection. Conservative estimate: ₹30,000-45,000 per month in preventable spend on underperforming campaigns.
Channel misattribution: Multi-channel setups without proper attribution often misallocate 15-25% of conversions. A common pattern: Meta gets over-credited, Google UAC gets under-credited, and experimental channels (influencer, affiliate, QR) appear to underperform because last-click attribution doesn't capture their assist value. Impact: ₹22,500-37,500 per month in suboptimal budget allocation.
Spreadsheet tax: At this scale, growth teams spend 12-18 hours per week reconciling data across platforms, debugging discrepancies, and building executive reports. At ₹2,000 per hour fully loaded cost: ₹24,000-36,000 per month.
Fraud and invalid traffic: Apps at this budget without fraud detection typically see 5-12% invalid install volume (bots, click spam, device farms). At ₹1,50,000 spend per month: ₹7,500-18,000 wasted on non-real users.
Total baseline waste: ₹84,000-1,36,500 per month
Attribution cost at this scale:
Legacy MMP pricing (AppsFlyer, Branch, Adjust): ₹1,50,000-3 lakh per month (seat-based pricing + data export fees + support charges)
Modern MMP pricing (Linkrunner): ₹64,000-96,000 per month at ₹0.80 per install for 8,000-12,000 installs
Cost differential: ₹54,000-2 lakh per month depending on current vendor
ROI Calculation (Modern MMP):
Prevented waste (50% of baseline, realistic with proper attribution): ₹42,000-68,250 per month
Efficiency gains (time savings + 12% ROAS improvement from faster optimisation): ₹24,000 (time) + ₹18,000 (ROAS gain) = ₹42,000 per month
Total benefit: ₹84,000-1,10,250 per month
Attribution cost: ₹64,000-96,000 per month
Net benefit: ₹20,000-46,250 per month
ROI: 1.3-1.7x or 31-70%
ROI Calculation (Legacy MMP vs Modern MMP):
If already using an expensive legacy MMP and evaluating a switch:
Current attribution cost: ₹2 lakh per month
Modern MMP cost: ₹80,000 per month
Cost savings: ₹1,20,000 per month
Implementation time: 2-4 weeks (one-time engineering cost)
Payback period for switching: ~2 weeks
Switching ROI is immediate and ongoing. Over 12 months, cost savings alone total ₹14.4 lakh—enough to fund an additional ₹1.2 lakh per month in user acquisition spend.
The strategic decision at this budget:
Teams spending ₹1,50,000 per month are past the "should we measure?" question. Attribution is non-negotiable. The finance conversation centres on vendor selection and cost efficiency.
If you're using a legacy MMP, the CFO-ready argument is simple: "We're spending ₹2 lakh per month to measure ₹1.5 lakh in ads. Modern alternatives provide the same accuracy and better dashboards at 60% lower cost. Switching saves ₹14.4 lakh annually with no reduction in measurement quality."
If you're not using attribution yet at this scale, the argument is operational necessity: "We're making ₹1.5 lakh per month budget allocation decisions based on incomplete data. Baseline audit shows ₹84,000-1.36 lakh per month in preventable waste. Attribution infrastructure pays for itself in 3-4 weeks and prevents ongoing losses."
Finance teams understand both arguments. What they don't accept is paying enterprise prices for mid-market budgets, or operating without measurement infrastructure at this scale. The ROI case is clear—it's just a matter of picking the right cost structure.
How to Present: One-Page Business Case Template
Finance teams don't want 15-slide decks explaining how attribution works. They want a single page that shows the numbers, the logic, and the decision. Here's the exact template that wins budget approvals:
[Header Section]
Business Case: Mobile Attribution Infrastructure Prepared by: [Your name, role] Date: [Current date] Decision required: Approve ₹[X] per month for attribution platform Payback period: [Y] days
[Section 1: Current State Problem]
Monthly ad spend: ₹[Amount] Channels active: [List: Meta, Google, TikTok, etc.] Current attribution status: [None / Legacy MMP / DIY solution]
Identified measurement gaps:
[Gap 1]: [Specific problem, e.g., "Cannot track ROAS by campaign, only by channel"]
[Gap 2]: [Specific problem, e.g., "30% of conversions show as 'organic' but likely paid-influenced"]
[Gap 3]: [Specific problem, e.g., "Team spends 15 hours/week building reports manually"]
Quantified impact:
Estimated monthly waste from poor attribution: ₹[Amount from baseline calculation]
Current attribution cost (if applicable): ₹[Amount]
Time spent on manual reporting: [X] hours per week
[Section 2: Proposed Solution]
Recommended platform: [Tool name] Monthly cost: ₹[Amount] Implementation timeline: [X] weeks
Why this option:
[Reason 1: e.g., "60% lower cost than legacy MMPs"]
[Reason 2: e.g., "Implementation takes 2 weeks vs 6-8 weeks for competitors"]
[Reason 3: e.g., "Includes fraud detection, unlimited data exports, no seat limits"]
[Section 3: Financial ROI]
Prevented waste (monthly):
Faster detection of negative-ROAS campaigns: ₹[Amount]
Fraud and invalid traffic blocked: ₹[Amount]
Corrected misattribution improving budget allocation: ₹[Amount]
Subtotal: ₹[Amount]
Efficiency gains (monthly):
Time savings (reporting automation): ₹[Amount]
Faster optimisation (daily vs weekly decisions): ₹[Amount]
Better creative testing velocity: ₹[Amount]
Subtotal: ₹[Amount]
Total monthly benefit: ₹[Amount] Monthly attribution cost: ₹[Amount] Net monthly benefit: ₹[Amount]
ROI: [X]x or [Y]% Payback period: [Z] days
Annual impact: ₹[Monthly net benefit × 12]
[Section 4: Risk Mitigation]
What if ROI doesn't materialise?
Pilot period: Run for 90 days, validate against projected savings
Exit clause: Monthly contracts (no annual lock-in)
Validation metrics: [Specify 2-3 KPIs you'll track to prove value]
Implementation risk:
Engineering time required: [X] hours over [Y] weeks
Business disruption: Minimal (platform migration happens in parallel)
Data continuity: Historical data remains accessible
[Section 5: Decision Request]
Approval requested: ₹[Amount] per month for [Tool name] Start date: [Target date] Review milestone: 90-day ROI validation on [Date]
Expected outcome: Prevent ₹[Amount] monthly waste, improve marketing efficiency by [X]%, and establish measurement infrastructure to support scaling from ₹[Current spend] to ₹[Target spend] over next 12 months.
Usage notes:
Keep this to one page. Finance teams value brevity. Use the second page only if you need to include a detailed cost breakdown or comparison table.
Include actual numbers from your baseline audit. Generic claims ("significant savings") get rejected. Specific calculations ("prevent ₹42,000 monthly waste from negative-ROAS campaigns") get approved.
Show your work. If you're projecting 60% waste prevention, explain why that's realistic. If you're estimating ROAS improvement, cite benchmarks or peer examples.
Include a validation mechanism. CFOs approve business cases that include accountability. Commit to a 90-day review where you'll present actual prevented waste vs projected.
Avoid technical jargon. Don't explain how SKAdNetwork postbacks work or what deterministic attribution means. Finance doesn't need to understand the mechanics—they need to understand the financial outcome.
Frame the decision as risk mitigation, not new investment. Attribution isn't a "growth initiative"—it's loss prevention for your existing marketing budget. That shifts the conversation from "should we spend more?" to "should we protect what we're already spending?"
End with a clear ask. "Approve ₹80,000 per month for Linkrunner" is better than "I think we should explore attribution options." Finance teams want decisive recommendations, not open-ended proposals.
This template has been used successfully across fintech apps, gaming companies, and consumer brands to justify attribution spend ranging from ₹60,000 to ₹4 lakh per month. The framework works because it speaks finance language: prevented losses, efficiency gains, payback periods, and validation metrics.
Implementation: Making Attribution ROI Tangible and Measurable
Finance doesn't just approve budget—they expect post-implementation validation that the investment delivered the projected ROI. Here's how to operationalise attribution measurement so you can prove value in your 90-day review.
Week 1: Baseline documentation
Before implementing attribution, document your current state with precision:
Pull 90 days of campaign data across all platforms (Meta, Google, TikTok, etc.)
Calculate current blended ROAS, CPI, and cost per paying user
Identify your top 10 campaigns by spend and document their performance metrics
Note total weekly hours spent on reporting and data reconciliation
If running attribution already, document current tool cost and limitations
Why this matters: Finance will ask "how do we know attribution improved performance?" Your pre-implementation baseline is the comparison point. Without it, you can't prove value.
Week 2-4: Implementation and validation
Once attribution is live, validate data quality immediately:
Verify that attributed installs match platform-reported installs within 10-15%
Check that postbacks are firing correctly (use platform debugging tools to confirm)
Test deep linking flows (dynamic links, deferred deep links, universal links)
Configure fraud detection rules and confirm invalid traffic is being flagged
Set up automated alerts for ROAS drops, CPI spikes, and attribution discrepancies
Common mistakes to avoid: Teams often implement attribution but don't validate postbacks, leading to 30-60 days of incomplete data before someone notices the tracking isn't working. Validation in week one prevents this waste.
Week 4-12: Operational measurement
Track these metrics weekly and present them to finance in your 90-day review:
Prevented waste (measurable immediately):
Campaigns paused due to negative ROAS: [List with spend amounts]
Fraud blocked: [Invalid install count × average CPI]
Budget reallocated from underperforming to high-performing campaigns: [Amount moved]
Efficiency gains (measurable within 60 days):
Hours per week spent on reporting: [Before vs After]
Time from campaign launch to optimisation decision: [Before vs After]
Number of campaigns tested per month: [Before vs After]
Performance improvement (measurable within 90 days):
Blended ROAS: [Before vs After]
Cost per paying user: [Before vs After]
Fraud rate: [Invalid traffic percentage]
Cost validation:
Actual monthly attribution cost: [Amount]
Attribution cost as % of ad spend: [Percentage, target 3-6%]
The 90-day finance presentation:
Your review presentation should follow this structure:
Slide 1: Business case recap
Projected ROI: [X]x
Projected payback period: [Y] days
Investment: ₹[Amount] per month
Slide 2: Actual results
Prevented waste (documented): ₹[Amount] per month
Efficiency gains (documented): ₹[Amount] per month
Performance improvement: [X]% ROAS increase
Actual ROI: [X]x
Slide 3: Specific examples
Campaign 1: Paused at ₹[Amount] spend due to 0.2x ROAS, prevented ₹[Amount] additional waste
Campaign 2: Reallocated ₹[Amount] budget from negative ROAS to 2.8x ROAS campaign
Fraud detection: Blocked [X] invalid installs, saved ₹[Amount]
Slide 4: Validation
Platform discrepancies: [Within 10-15% target? Yes/No]
Data completeness: [98%+ attributed install coverage]
Team adoption: [Reporting time reduced from X to Y hours per week]
Slide 5: Next 90 days
Continue current measurement approach
Add [specific capability: e.g., "creative-level attribution" or "cohort-based LTV tracking"]
Target [X]% further ROAS improvement through faster testing velocity
Finance teams respond well to this format because it's transparent: you committed to specific ROI, you're showing actual results against that commitment, and you're acknowledging where results differed from projections (if they did).
What if ROI didn't hit projections?
Be honest about gaps. If you projected 2x ROI but achieved 1.3x, explain why:
"Implementation took 4 weeks instead of 2 due to SDK debugging, delaying value capture"
"Fraud rate was lower than estimated (5% vs 10%), reducing prevented waste"
"Platform discrepancies required troubleshooting, limiting data completeness in weeks 2-5"
Then show how you're course-correcting: "We've resolved platform discrepancies, achieved 98% data completeness, and expect to hit projected ROI by month four based on current trajectory."
Finance teams appreciate accountability more than perfect outcomes. Showing that you're tracking against projections and adjusting when results differ builds trust for future budget requests.
Making attribution a standing finance agenda item:
Once attribution is operational, add a monthly slide to finance reviews showing:
Total ad spend this month
Attribution cost this month
Attribution cost as % of ad spend (target 3-6%)
Documented prevented waste (campaigns paused, fraud blocked)
Month-over-month ROAS trend
This keeps attribution visible and validates ongoing value. It also prevents the "Why are we still paying for this?" question six months later, because finance has seen continuous evidence of value in every review.
Conclusion: Translating Marketing Measurement Into Financial Outcomes
The attribution ROI conversation isn't about convincing finance teams that marketing measurement matters. They already believe measurement matters—they just don't see why it should cost ₹60,000-6 lakh per month when free tools exist.
The gap is translation. Marketing teams talk about attribution windows, postback accuracy, and multi-touch models. Finance teams talk about prevented losses, efficiency gains, and payback periods. Neither is wrong. You're just speaking different languages.
This framework gives you the translation:
Baseline waste quantification (what you're losing now)
ROI calculation (what you'll gain with attribution)
Scenario modeling (proof it works at your scale)
One-page business case (finance-friendly format)
Objection responses (data-driven answers)
Implementation validation (proof you delivered value)
At ₹50,000 per month in ad spend, attribution ROI is 6-8x with the right tool. At ₹1.5 lakh per month, ROI is 1.3-1.7x. At ₹5 lakh per month, ROI is 1.6-2.2x. These aren't theoretical projections. They're based on documented patterns across mobile apps operating with and without proper measurement infrastructure.
The cost of bad attribution isn't dashboards that look confusing. It's ₹80,000-6 lakh per month flowing to campaigns you can't validate, channels you're over-crediting, and users who aren't real. That's the financial outcome finance teams care about. That's the case you need to make.
At Linkrunner's ₹0.80 per install pricing, a team spending ₹5 lakh per month on ads typically pays ₹40,000-60,000 for attribution. If the platform prevents just 5-8% budget misallocation (₹25,000-40,000 per month), ROI is positive before counting efficiency gains, fraud prevention, or ROAS improvement. When you add those benefits, payback happens in 15-25 days.
That's not a marketing claim. That's a CFO-ready financial outcome.
If your finance team has questioned measurement spend, use this framework. Quantify your baseline waste. Calculate expected ROI. Present it in their language. And show them that attribution isn't overhead—it's loss prevention for your marketing budget.
Request a demo from Linkrunner to see how attribution infrastructure translates into measurable financial outcomes, or explore our resources on the true cost of mobile attribution, when to adopt an MMP, and the hidden cost of inaccurate attribution.
Frequently Asked Questions
How do I calculate baseline waste if I don't have attribution data yet?
Use platform-level data you already have. Pull 90 days of campaign spend from Meta and Google. Identify campaigns with CPI above your target (likely negative ROAS). Estimate time your team spends on reporting. Calculate suspected organic inflation (if "organic" is 60%+ while running paid campaigns, estimate 25-35% is likely misattributed paid). Add these up. This won't be precise, but it gives you a conservative baseline for your business case.
What if my CFO says attribution should cost less than 1% of ad spend?
Show them the actual market rates. For teams spending ₹1.5-5 lakh per month, attribution typically costs 4-8% of ad spend with modern MMPs (3-6% is optimal). Legacy MMPs can exceed 10-15%. The percentage decreases at scale: teams spending ₹50 lakh per month might pay 1-2%. At smaller budgets, the fixed cost of measurement infrastructure means higher percentages are normal. What matters is absolute ROI, not percentage of spend.
How long should I pilot attribution before committing long-term?
90 days is standard. Week 1-2: implementation and validation. Week 3-8: operational measurement and baseline comparison. Week 9-12: performance improvement should be measurable. If you're not seeing value by day 90, either the tool isn't working correctly or your initial baseline calculation was wrong. Either way, 90 days gives you enough data to decide.
Can I justify attribution if our app is pre-revenue?
Yes, but the calculation shifts. If you're not tracking revenue events yet, focus on prevented waste (campaigns paused due to poor retention or engagement metrics), fraud prevention, and efficiency gains (time savings). Pre-revenue apps still waste budget on underperforming campaigns—you just can't use ROAS as the metric. Use D1/D7 retention, engagement rate, or signup completion as your quality signal instead.
What if attribution data doesn't match platform reporting?
Discrepancies within 10-15% are normal and expected due to different attribution windows and methodologies. Discrepancies above 20% signal a problem: missing postback configurations, SDK implementation errors, or attribution window mismatches. Use your MMP's validation tools to diagnose why the gap exists. Don't accept "all MMPs have discrepancies" as an excuse for poor data quality—if the gap is above 20%, something is broken and needs fixing.
Should I present ROI as a multiple (2.5x) or a percentage (150%)?
Finance teams understand both, but multiples are clearer for quick evaluation. "2.5x ROI" immediately signals "for every rupee spent, we get 2.5 rupees back." Percentages work too but require translation ("150% ROI means 2.5x"). Use whichever your CFO prefers, but default to multiples for simplicity.
How do I validate that prevented waste is real and not just projected?
Document specific campaigns. "Campaign ID 12345 spent ₹45,000 over 6 weeks with 0.3x ROAS. We paused it on day 3 with attribution data, preventing an additional ₹38,000 spend." This is documented prevented waste, not a projection. Track these examples weekly. By your 90-day review, you should have 5-10 specific instances showing "campaign paused at ₹X spend, would have continued to ₹Y spend based on previous run pattern."
What if my finance team wants ROI proof before approving any spend?
Offer a pilot structure: "Approve ₹[Amount] for 90 days. If we don't document ₹[Amount] in prevented waste plus efficiency gains within that period, we'll discontinue and revert to current approach." This shifts the decision from "approve ongoing spend" to "approve a 90-day test with clear success criteria." Most CFOs will approve pilots even if they're skeptical of ongoing commitments.
