You've built a mobile app, shipped version 1.0, and launched your first paid marketing campaigns. Your dashboard shows 50,000 downloads, 15,000 monthly actives, and a growing install curve. When investors ask how the business is performing, you share these numbers confidently. Then they ask the follow-up questions: "What's your Day 1 retention?" "What's the payback period?" "How many users completed your core action?" And suddenly, the conversation gets uncomfortable.

This pattern repeats across early-stage mobile startups. Teams track the metrics that are easy to measure (total installs, MAU, session counts) while ignoring the metrics that actually predict whether the business will scale profitably. The difference between vanity metrics and success predictors is the difference between raising a Series A and running out of runway with impressive-looking but meaningless KPIs.

The metrics that matter aren't always the ones that look best in pitch decks. They're the ones that tell you if users find value fast enough, whether your acquisition costs make sense relative to user lifetime value, and if you're building genuine product-market fit or just burning money on ads that bring one-time users who churn immediately.

Beyond Vanity Metrics: What VCs Actually Look At

Most mobile app pitch decks lead with total downloads, total users, and growth rates. These numbers feel impressive ("we've grown 300% quarter-over-quarter") until you realise that growing from 100 to 300 users doesn't prove anything about sustainable business models.

Venture investors look past surface-level metrics immediately. They've funded enough mobile apps to know that early traction is easy to manufacture with paid ads and limited-time promotions. What they're actually evaluating is whether your unit economics will allow profitable scale, whether users are retaining long enough to justify acquisition costs, and whether you've found repeatable channels that won't immediately saturate.

The shift from vanity metrics to predictive metrics typically happens after a startup burns through their first ₹50 lakh in marketing spend without clear ROI attribution. At that point, founders realise that tracking installs without retention is meaningless, that MAU without engagement tells you nothing about product value, and that ad platform dashboards showing "great performance" don't always match reality when you actually calculate customer payback.

Metric #1: D1 Retention Above 35% (Product-Market Fit Signal)

Day 1 retention (the percentage of users who return to your app the day after first install) is the earliest signal of whether you've built something people actually want. If 100 users install your app today and only 20 return tomorrow, you have a 20% D1 retention rate.

Strong D1 retention (above 35%) indicates users found immediate value and chose to return without prompting. Weak D1 retention (below 25%) means your onboarding failed, your core value proposition wasn't clear, or users installed out of curiosity but didn't discover a reason to stay.

D1 retention predicts long-term retention better than any other single metric. Apps with sub-20% D1 retention almost never achieve strong D7 or D30 retention. Conversely, apps with 40%+ D1 retention often see 25-30% D7 retention and 15-20% D30 retention, which creates viable unit economics.

Benchmark targets vary by vertical. Gaming apps often see 35-45% D1 retention when onboarding is effective. Social and communication apps can achieve 40-50% if the network effect is strong. Fintech apps that require account setup and verification often see 30-40% D1 retention after users complete initial friction.

If your D1 retention is below 25%, the problem is almost always onboarding or value communication. Users aren't discovering your core feature fast enough, permission requests are blocking them, or your first-run experience is too complex.

Metric #2: Organic Install Ratio Above 20% (Word-of-Mouth Validation)

Organic install ratio measures what percentage of your new installs come from non-paid sources: direct app store search, word-of-mouth referrals, social sharing, and brand search. If you're driving 1,000 installs monthly and 200 come from organic channels, you have a 20% organic ratio.

This metric validates product-market fit in a way paid installs never can. Paid installs prove you can buy users. Organic installs prove users are actively seeking you out or recommending you to others. When organic install ratios consistently exceed 30%, you've typically found genuine product resonance.

Low organic ratios (under 10%) indicate you're artificially propping up growth with paid spend and haven't built word-of-mouth momentum. Strong organic ratios (above 25%) create compounding advantages. Every paid install that turns into an advocate reduces your future acquisition costs.

Track this metric by source in your attribution platform. Your MMP should clearly segment paid versus organic attribution. One warning: organic ratio naturally varies by lifecycle stage. Brand-new apps in month one might see 90% paid installs as they build initial traction. By month six, that should shift toward 30-40% organic if product-market fit exists.

Metric #3: CAC Payback Under 90 Days (Unit Economics)

CAC payback period measures how long it takes for a new user to generate enough revenue to recover their acquisition cost. If you spend ₹200 to acquire a user and they generate ₹50 in revenue in month one and ₹50 in month two, your payback period is roughly 2 months.

This metric determines whether your business model is fundamentally viable. Apps with payback periods under 90 days can aggressively scale acquisition because they recover investment quickly. Apps with payback periods over 180 days struggle to raise capital because investors know runway constraints make scaling difficult.

The formula is straightforward: CAC payback = (Blended CAC) / (Average Monthly Revenue per User). If your blended CAC is ₹300 and users generate ₹100 monthly on average, payback is 3 months.

Benchmark targets depend on monetisation model. Subscription apps should target sub-60 day payback because recurring revenue accelerates returns. Transaction-based apps (ecommerce, fintech, marketplaces) often have 60-120 day payback periods. Ad-monetised apps frequently face 120+ day payback, which limits growth velocity.

Many founders miscalculate payback by using only initial transaction value rather than cumulative revenue through payback window. If users spend ₹150 on first purchase but also generate ₹50 in month two and ₹50 in month three, payback should include all three months of revenue (₹250 total) not just first transaction.

Metric #4: Monthly Revenue per DAU Above ₹15 (Monetisation Efficiency)

Revenue per daily active user (ARPDAU) measures how effectively you're monetising your engaged user base. Calculate it by dividing total monthly revenue by average daily active users, then dividing by 30 days. If you generated ₹3 lakh in revenue last month with 20,000 average DAU, your ARPDAU is ₹0.50.

ARPDAU benchmarks vary dramatically by business model. Subscription apps typically see ₹15-50 ARPDAU. Transaction-based apps (ecommerce, fintech) often land between ₹8-25 ARPDAU. Ad-monetised apps usually achieve ₹1-5 ARPDAU.

This metric separates apps with real revenue models from those with user bases that don't convert to paying customers. An app with 50,000 DAU and ₹0.10 ARPDAU is generating ₹1.5 lakh monthly, which won't support a team or paid acquisition. The same 50,000 DAU at ₹20 ARPDAU generates ₹30 lakh monthly, which funds growth and sustains operations.

Track ARPDAU by cohort and channel. Users acquired from Meta might generate ₹12 ARPDAU while users from organic search generate ₹25 ARPDAU because intent levels differ. Understanding these variations helps you allocate acquisition budgets intelligently.

Metric #5: Cohort LTV Growth Month-Over-Month (Improving Product)

Cohort LTV growth measures whether later user cohorts generate more lifetime value than earlier cohorts. If users who installed in January 2025 generate ₹400 LTV on average and users who installed in March 2025 generate ₹500 LTV, you're improving LTV by 25% across cohorts.

This metric indicates whether product improvements, retention optimisations, and monetisation experiments are actually working. Flat or declining cohort LTVs suggest your product isn't getting better.

Growing cohort LTVs create compounding advantages. If you can consistently increase LTV by 5-10% monthly while keeping CAC stable, your unit economics improve exponentially. This creates room to scale acquisition aggressively because each new user is more valuable than previous cohorts.

The challenge with cohort LTV is measurement lag. True LTV takes 12-24 months to fully materialise. Instead, track proxy metrics that predict LTV: D7 and D30 retention, early purchase conversion rates, feature adoption depth, and engagement intensity in first 30 days.

Track cohort LTV through your attribution platform. Modern MMPs like Linkrunner automatically calculate cohort-level LTV and retention so you can compare monthly install cohorts without building custom dashboards or exporting data to spreadsheets.

Metric #6: Conversion to Core Action Above 40% (Onboarding Quality)

Conversion to core action measures what percentage of new users complete your app's primary value-delivering behaviour within their first session or first day. For a food delivery app, this might be placing an order. For a fitness app, completing a workout.

This metric exposes onboarding quality immediately. If only 15% of new users complete your core action, the other 85% churned before discovering why your app matters. Strong conversion rates (above 40%) indicate users understand value quickly and encounter minimal friction.

Define your core action carefully. It should be the behaviour that creates genuine value and predicts retention. For many apps, this isn't account creation or tutorial completion. Core action is the moment users receive tangible benefit: completing a purchase, booking a reservation, finishing a workout, or connecting with a friend.

Benchmark targets vary by friction levels. Low-friction core actions (playing a level, viewing content) should convert 50-70% of users. Medium-friction actions (making a purchase, booking a service) typically achieve 30-50% conversion. High-friction actions (linking bank accounts, completing verification) often see 20-35% conversion even when well-designed.

Improving this metric usually delivers the highest ROI of any optimisation work. Increasing conversion to core action from 25% to 45% effectively doubles your successful user acquisition without increasing acquisition spend.

Metric #7: Viral Coefficient Above 0.3 (Organic Growth Loop)

Viral coefficient measures how many additional users each existing user brings to your app through referrals, sharing, or network effects. A viral coefficient of 0.5 means every 10 users generate 5 additional users organically.

Calculate viral coefficient by tracking invites sent per user and conversion rate of those invites. If each user sends 3 invites on average and 20% convert to installs, your viral coefficient is 0.6 (3 invites × 20% conversion).

Most mobile apps achieve viral coefficients between 0.1 and 0.4. Social networks and multiplayer games can reach 0.8-1.5 when network effects are strong. Single-player apps and utility tools rarely exceed 0.3.

Viral coefficient below 0.2 indicates you haven't built sharing into the core product experience. Above 0.3 is strong (you're generating meaningful organic growth through existing users).

For funded startups, viral coefficient directly impacts capital efficiency. An app with 0.4 viral coefficient spends 40% less on paid acquisition to reach the same user count compared to an app with 0.1 viral coefficient.

Metric #8: Net Revenue Retention Above 100% (Expansion Revenue)

Net revenue retention (NRR) measures whether your existing user cohorts generate more revenue over time through upsells, cross-sells, and expanded usage. NRR above 100% means existing users are becoming more valuable, which creates growth even without new acquisition.

Calculate NRR by comparing revenue from a cohort today versus their revenue 12 months ago, accounting for churn. If users who installed in January 2024 generated ₹10 lakh monthly revenue back then, and those same users (minus churned users) generate ₹12 lakh monthly today, your NRR is 120%.

NRR above 110% is exceptional and indicates strong product-market fit plus expansion opportunities. NRR between 85-100% suggests you're retaining users but not expanding revenue. Below 85% indicates both retention and monetisation challenges.

For subscription apps, NRR growth comes from tier upgrades and add-on purchases. For transaction apps, it comes from increasing order frequency and expanding basket sizes. For ad-monetised apps, it comes from increasing session frequency and duration.

Metric #9: Time to First Value Under 5 Minutes (Activation Speed)

Time to first value measures how quickly new users experience your core benefit after installing. For a food delivery app, this might be viewing personalised restaurant recommendations. For a workout app, starting their first exercise.

This metric directly impacts retention. Users who don't find value within 5 minutes often abandon apps and never return. Strong products deliver value within 2-3 minutes, creating immediate "aha moments" that hook users.

Benchmark targets depend on product complexity. Simple utility apps should deliver value within 60-90 seconds. Content and commerce apps typically need 2-4 minutes. Apps requiring account creation or verification might take 5-8 minutes but should still minimise time to value.

Common friction points include account registration walls before users can explore, long tutorial sequences that don't allow skipping, empty state screens without sample content, and complex permission requests that block core features.

Improving time to first value often requires rethinking onboarding architecture. The best mobile products allow users to experience core value before requiring account creation and defer non-essential setup steps until after users are engaged.

Metric #10: Monthly Active to Daily Active Ratio Below 3 (Habit Formation)

MAU/DAU ratio measures how frequently active users return to your app. A ratio of 2.0 means users are active roughly every other day. A ratio of 5.0 means users are active once per week. Lower ratios indicate stronger engagement and habit formation.

Calculate this by dividing monthly active users by average daily active users. If you have 30,000 MAU and 10,000 average DAU, your ratio is 3.0, which means each monthly active user is active approximately 10 days per month.

Target ratios vary dramatically by app category. Messaging and social apps should achieve ratios below 2.5. Content apps (news, video, audio) typically land between 2.5-4.0. Utility apps used situationally might see 4.0-6.0 ratios. Transaction apps (shopping, food delivery) often exceed 10.0 because usage is episodic.

Low MAU/DAU ratios indicate habit formation and product stickiness. Users open your app multiple times weekly or daily because it's integrated into their routines. This predicts strong retention and high lifetime value.

How to Track These Without Complex Analytics Stack

You don't need expensive enterprise analytics platforms to track these 10 metrics. Modern attribution and analytics tools make startup-essential KPIs accessible even at early stages.

Your attribution platform should automatically calculate retention (D1, D7, D30), segment organic versus paid installs, track cohort-level LTV, and measure conversion to key events. Platforms like Linkrunner provide these metrics out of the box at ₹0.80 per install with no additional analytics fees, eliminating the need to stitch together multiple tools or build custom dashboards.

For product analytics beyond attribution, integrate a lightweight SDK like Mixpanel, Amplitude, or PostHog. These platforms offer free tiers for early-stage startups (up to 20 million events monthly).

The critical capability is unified attribution data. If you're tracking installs in one platform, events in another, and revenue in a third, you'll waste weeks reconciling data in spreadsheets. Modern MMPs that offer both attribution and deep linking in one platform (like Linkrunner) let you track the full funnel from ad click to install to core action to revenue without managing multiple integrations.

Benchmark Ranges by Vertical

Gaming Apps:

D1 Retention: 35-45% | Organic Ratio: 15-30% | CAC Payback: 60-120 days | ARPDAU: ₹2-8 (ad-based) or ₹20-40 (IAP) | MAU/DAU: 2.0-3.5

Social & Communication Apps:

D1 Retention: 40-55% | Organic Ratio: 40-70% | CAC Payback: 90-180 days | ARPDAU: ₹0.50-3 (ad-based) or ₹15-30 (subscription) | MAU/DAU: 1.5-2.5

Fintech Apps:

D1 Retention: 30-45% | Organic Ratio: 20-35% | CAC Payback: 30-90 days | ARPDAU: ₹10-25 | MAU/DAU: 3.0-6.0

eCommerce Apps:

D1 Retention: 25-35% | Organic Ratio: 25-40% | CAC Payback: 60-120 days | ARPDAU: ₹15-35 | MAU/DAU: 5.0-10.0

Content & Media Apps:

D1 Retention: 30-40% | Organic Ratio: 30-50% | CAC Payback: 90-180 days | ARPDAU: ₹1-5 (ad-based) or ₹20-50 (subscription) | MAU/DAU: 2.5-4.0

Frequently Asked Questions

How do I know which metrics to prioritise first?

Start with retention (D1, D7) and conversion to core action. These indicate product-market fit most directly. If users aren't returning or completing your core action, improving other metrics won't matter. Once retention exceeds 30% D1 and 20% D7, layer in monetisation metrics (ARPDAU, payback) and growth efficiency metrics (organic ratio, viral coefficient).

What if my metrics are below benchmark but we're still growing?

Growth without strong unit economics is often unsustainable. You might be succeeding in paid acquisition but building a user base that won't retain or monetise well long-term. This creates a dangerous scenario where pausing spend causes immediate growth collapse. Focus on improving core metrics before scaling spend further.

How often should I review these metrics?

Retention and activation metrics (D1 retention, conversion to core action, time to first value) should be reviewed weekly. Unit economics metrics (CAC payback, ARPDAU, cohort LTV) should be reviewed monthly because they require enough data volume to be meaningful. Growth loop metrics (organic ratio, viral coefficient) can be reviewed bi-weekly.

What's the minimum viable metrics dashboard for a pre-seed startup?

Track these five metrics from day one: D1 retention, conversion to core action, organic vs paid install split, blended CAC, and ARPDAU (or proxy revenue metric). These five metrics tell you if you're building a product people want (retention), if onboarding works (conversion), if you have word-of-mouth (organic ratio), if acquisition is efficient (CAC), and if monetisation exists (ARPDAU).

How do attribution platforms help with tracking these metrics?

Modern MMPs provide most of these metrics automatically through their dashboards. Retention tracking, cohort analysis, install source attribution, and revenue tracking are core features. Platforms like Linkrunner specifically designed for growing apps include cohort-level LTV views, retention curves by channel, organic vs paid breakdowns, and automated ROAS tracking without requiring custom integrations or data exports. Request a demo from Linkrunner to see how attribution platforms surface these metrics natively.

What if my app is pre-monetisation and I can't track revenue metrics yet?

Focus on engagement and retention proxies that predict future monetisation potential. Track session frequency, feature adoption depth, and user-perceived value through NPS surveys. Users who engage daily and use core features extensively will likely convert to paid plans when you introduce monetisation. Establish strong retention and engagement baselines first, then layer in monetisation and measure conversion rates.