Three weeks ago, you found it. The campaign. CPI at ₹140, D7 ROAS at 2.1x, and scaling beautifully at ₹8 lakh per week. You briefed your team, increased budget 40%, and started planning how to replicate the success across other channels. Then week four happened. CPI crept to ₹195. ROAS dropped to 1.4x. By week five, you're at ₹240 CPI with 0.9x ROAS, and the campaign that was carrying your monthly targets is now actively losing money.

This isn't bad luck. It's campaign decay, and it happens to every performance marketer running paid UA at scale. The pattern is remarkably consistent: campaigns perform well for 2-5 weeks, then gradually or suddenly decline until they're no longer profitable. Most teams respond by killing the campaign and starting fresh. That works, but it's expensive. You lose accumulated algorithmic learning, waste the production investment in creative, and restart the learning phase with every new campaign.

The better approach is understanding why campaigns decay and applying specific extension strategies matched to the decay mechanism. Not every campaign dies for the same reason, and the fix depends on an accurate diagnosis.

The 30-Day Performance Cliff: Why Great Campaigns Suddenly Fail

Campaign decay is predictable. Across hundreds of app campaign audits, the pattern follows a consistent timeline. Days 1-7 are the learning phase, where performance is volatile. Days 7-21 are typically peak performance, where the algorithm has optimised delivery and creative is still fresh. Days 21-35 is when decay begins, with gradual increases in CPI and decreases in conversion rates. Beyond day 35, most campaigns have degraded 30-60% from peak performance.

This pattern holds across Meta, Google, and TikTok (though TikTok's cycle is shorter, often peaking by day 14 and decaying by day 21). The timeline varies by audience size, budget, and vertical, but the shape is remarkably consistent.

The financial impact is significant. If a campaign's peak ROAS is 2.0x but its average ROAS over 60 days drops to 1.2x because of decay, you're leaving 40% of potential return on the table. For a team spending ₹20 lakh monthly, that's ₹8 lakh in preventable waste, every single month.

Understanding Campaign Decay: The Three Core Mechanisms

Campaign decay isn't one problem. It's three distinct problems that often overlap. Each has different symptoms, different causes, and different fixes. Diagnosing which mechanism is at play is the first step toward extending campaign life.

The three mechanisms are audience saturation (you've reached the profitable portion of your target audience), frequency exhaustion (the same users have seen your ad too many times), and algorithmic staleness (the platform's algorithm has stopped actively optimising your campaign). Most campaigns experience all three simultaneously, but one mechanism usually dominates.

Mechanism #1: Audience Saturation (You've Reached Everyone Who Will Convert)

Every audience has a finite number of users who will convert at your target CPI. When your campaign has shown ads to most of those users, it starts reaching people who are progressively less likely to convert. CPI rises because the algorithm is working harder to find converting users in a depleted pool.

Symptoms. CPI increases gradually (5-10% per week) while CTR remains stable or declines slightly. Frequency increases steadily. New user reach decreases week over week. If you check Meta's estimated audience saturation metric, it shows 60%+ delivery against your target audience.

Root cause. Your audience targeting is too narrow relative to your budget. A 2 million person lookalike audience with ₹5 lakh weekly spend will saturate in 3-4 weeks. A 10 million broad audience with the same budget lasts 8-12 weeks.

Validation check. Pull your campaign's weekly reach and frequency data. If reach is declining and frequency is increasing while budget is flat or growing, audience saturation is your primary decay driver.

Mechanism #2: Frequency Exhaustion (Same People, Too Many Times)

Even within a non-saturated audience, individual users can see your ad too many times. After 3-5 exposures, conversion probability drops sharply. After 7+ exposures, most users actively ignore or hide your ad, which sends negative signals to the platform's algorithm.

Symptoms. Frequency exceeds 3.0 for the campaign or specific ad sets. CTR drops 20-40% from week 1 to week 4. Negative feedback (hide ad, report ad) increases. CPI spikes sharply rather than gradually.

Root cause. Creative isn't rotating fast enough relative to audience size and budget. Campaigns with a single creative asset fatigue 2-3x faster than campaigns with 5+ rotating assets.

Validation check. Check frequency by ad and by ad set. If specific ads have frequency above 4.0 while the overall campaign average is 2.5, those individual ads are exhausted. If the entire campaign has high frequency, the audience needs expanding.

Mechanism #3: Algorithmic Staleness (Platforms Deprioritise Old Campaigns)

This is the least discussed but increasingly important decay mechanism. Ad platforms constantly test and optimise delivery. Newer campaigns receive more algorithmic attention, better auction priority, and access to fresh inventory. Older campaigns gradually lose priority as the platform redirects its optimisation resources.

Symptoms. Impressions decrease despite stable or increased budgets. Spend delivery slows (campaigns consistently underspend daily budgets). Performance degrades even when audience saturation and frequency metrics look healthy.

Root cause. Platform algorithms prioritise campaigns with recent structural changes (new creative, new audiences, bid adjustments) over static campaigns. A campaign that hasn't been meaningfully updated in 3-4 weeks sends a signal that it's been "set and forget," and the algorithm treats it accordingly.

Validation check. If your campaign is underspending its daily budget by 15%+ while CPI has increased, algorithmic deprioritisation is likely contributing. Check whether competing advertisers in your vertical have recently launched fresh campaigns, which shifts auction dynamics against older campaigns.

Diagnosing Which Mechanism Is Killing Your Campaign

Before applying any fix, diagnose the primary decay mechanism. This prevents you from applying the wrong solution, which often accelerates the problem.

Pull these data points for the last 4 weeks: weekly CPI trend, weekly CTR trend, weekly reach (unique users), weekly frequency, daily spend vs daily budget (delivery ratio), and number of creative assets currently active.

If CPI is rising gradually and frequency is increasing, audience saturation is dominant. If CTR is dropping sharply and frequency on individual ads exceeds 4.0, frequency exhaustion is the primary issue. If spend delivery is declining while budgets are stable, algorithmic staleness is at play.

In most cases, you'll see signs of 2-3 mechanisms simultaneously. Prioritise the fix for the dominant mechanism first. For guidance on building a regular monitoring routine that catches these signals early, see daily, weekly, monthly KPIs: what to track and when for mobile marketers.

Extension Strategy #1: Audience Layering and Expansion

Fixes: Audience saturation

Instead of running a single audience until it depletes, layer audiences progressively. Start with your highest-intent audience (1% lookalike of purchasers). When saturation signals appear (usually week 3-4), add a second layer: 3% lookalike of the same seed. The algorithm already has conversion data from layer one, so it optimises layer two faster.

Continue expanding: 3% lookalike, then 5%, then broad targeting with interest modifiers. Each layer adds fresh reach while maintaining the algorithmic learning from previous layers.

The key is expanding before peak performance ends, not after. If you wait until CPI has already spiked 40%, you're adding fresh audience to a campaign that's already struggling. Add the next audience layer when you see the first signs of saturation (frequency increasing above 2.0, reach declining 10-15% week over week).

Expected impact. Audience layering typically extends campaign viability by 3-6 weeks beyond the initial saturation point.

Extension Strategy #2: Creative Rotation Without Losing Learning

Fixes: Frequency exhaustion + Algorithmic staleness

The most common extension mistake is launching entirely new creative when existing creative fatigues. That resets algorithmic learning and puts you back in the learning phase. Instead, use iterative rotation: modify 20-30% of the ad while keeping 70-80% identical.

Practical rotation approaches: swap the first 3 seconds (hook) while keeping the body and CTA. Change the background music or voiceover talent. Adjust visual elements (colours, text overlays, thumbnail). Use the same script but reshoot with a different creator or setting.

Each rotation is different enough that the platform treats it as a new ad (avoiding frequency exhaustion) but similar enough that the algorithm can transfer learning from the previous version (avoiding full learning phase reset).

Rotate creative every 14-21 days on Meta, every 10-14 days on TikTok, and every 21-30 days on Google UAC. Keep at least 4-5 active creative assets per ad set at all times. For a deeper framework on managing creative lifecycles, see the complete ad creative optimisation guide for modern marketers.

Extension Strategy #3: Bid Strategy Evolution (From CPA to ROAS)

Fixes: Algorithmic staleness + Audience saturation

As campaigns age, shifting your bidding strategy can refresh algorithmic optimisation. If you started with target CPA bidding, switching to target ROAS bidding changes what the algorithm optimises for, effectively restarting its learning process without losing the audience data it has accumulated.

This works because different bidding strategies access different parts of the algorithm. CPA bidding optimises for conversion volume. ROAS bidding optimises for conversion value. When you switch, the platform re-evaluates every user in your audience based on predicted value rather than just conversion probability.

Time this transition at the first signs of algorithmic staleness (underspending budget, declining impressions). The bid strategy switch signals to the platform that your campaign is actively being optimised, which restores priority.

Implementation note. When switching bid strategies, expect 3-5 days of volatile performance as the algorithm recalibrates. Set your target ROAS 10-15% below your actual goal to give the algorithm room to learn. Adjust upward after the new learning phase stabilises.

Extension Strategy #4: Geographic and Demographic Expansion

Fixes: Audience saturation

If you've been running campaigns in tier-1 cities only, expanding to tier-2 cities adds significant fresh audience at typically 20-40% lower CPIs. The tradeoff is usually lower LTV per user, but the expanded reach can sustain campaigns for an additional 4-8 weeks.

Expand geographically in stages. Add one tier-2 cluster at a time and measure D7 ROAS separately for each expansion. Some tier-2 markets deliver comparable LTV to tier-1 at lower CPI. Others deliver volume without value. You need the data to tell them apart.

Demographic expansion works similarly. If you've been targeting 25-34 year olds exclusively, testing 18-24 or 35-44 adds fresh audience. Again, measure downstream performance separately. The goal is extending reach without diluting overall campaign economics.

The Campaign Refresh vs Rebuild Decision Framework

Sometimes extension strategies work. Sometimes a campaign is genuinely exhausted and needs to be rebuilt. Here's how to decide.

Refresh (apply extension strategies) when: The campaign was profitable for 3+ weeks at peak, CPI has increased less than 50% from peak, at least one extension strategy hasn't been tried yet, and the core creative concept still resonates (CTR hasn't dropped below 50% of peak).

Rebuild (new campaign) when: CPI has more than doubled from peak, all three extension strategies have been attempted, CTR has dropped below 40% of peak performance, and the campaign has been running for 60+ days.

Rebuilding doesn't mean starting from zero. Transfer your learnings: use the same seed audiences for lookalikes, keep creative elements that tested well, and apply the same event/postback structure. What you reset is the campaign shell, giving the algorithm a fresh start.

Implementation Playbook: 7-Day Campaign Health Assessment

Before applying any extension strategy, run this assessment to understand your campaign's current state and decide which approach fits.

Day 1: Pull historical data. Export weekly performance for the campaign's entire lifetime. Plot CPI, CTR, frequency, and reach on the same timeline. Identify when peak performance occurred and when decay began.

Day 2: Diagnose decay mechanisms. Using the diagnostic framework above, determine whether audience saturation, frequency exhaustion, or algorithmic staleness is the primary driver.

Day 3: Audit creative assets. Check frequency for individual ads. Identify which specific assets are exhausted (frequency above 4.0) versus still viable. Count how many active creative assets you have.

Day 4: Assess audience headroom. Check estimated audience size vs delivery. Calculate what percentage of your addressable audience the campaign has reached. Determine whether audience expansion is viable.

Day 5: Select extension strategy. Based on Days 1-4, choose the primary extension approach. Prepare the implementation plan (new audiences, creative variants, or bid strategy changes).

Day 6: Implement. Launch the extension. Make one change at a time. If you're layering audiences AND rotating creative simultaneously, you can't isolate which change drove results.

Day 7: Set monitoring cadence. Define what metrics you'll check daily for the next 2 weeks and what thresholds trigger additional intervention. For teams managing this across multiple campaigns and channels, having unified attribution data accelerates these decisions. See 10 smart tactics to boost ROAS while keeping CPI low for related optimisation approaches.

FAQ: Campaign Lifecycle Questions Answered

Is 30 days a hard rule for campaign decay?

No. 30 days is the typical timeline for mid-sized audiences (₹5-15 lakh monthly budgets, 2-10 million addressable users). Smaller audiences decay faster (14-21 days). Larger audiences can sustain 45-60 days. TikTok campaigns typically decay faster than Meta campaigns due to higher creative consumption speed.

Should I keep decaying campaigns running at lower budgets?

Only if they remain profitable. If a decayed campaign still delivers 1.0x+ ROAS, reducing budget to sustainable levels and running it alongside a fresh campaign can maintain volume while the new campaign exits learning.

How many extension strategies can I apply simultaneously?

One at a time, with 5-7 days between changes. Stacking multiple extensions simultaneously makes it impossible to diagnose which intervention worked, and if performance doesn't improve, you won't know which strategy failed.

Does campaign decay affect retargeting campaigns too?

Yes, but differently. Retargeting audiences are naturally smaller and refresh constantly (new app installs create new retargeting prospects). Retargeting campaigns typically sustain longer but are more sensitive to frequency exhaustion. Cap frequency at 2-3 exposures per 7-day window for retargeting.

Can I prevent campaign decay entirely?

Not entirely, but you can delay it significantly. Teams that proactively rotate creative every 14-21 days, progressively expand audiences, and refresh bid strategies monthly see campaign lifecycles 2-3x longer than teams that launch and leave.

Extending Campaign Life Without the Spreadsheet Overhead

Campaign lifecycle management sounds straightforward, but it requires consistent monitoring of multiple metrics across multiple campaigns and channels. The teams that extend campaigns successfully are the ones that catch decay signals early, often within the first week of decline, not after CPI has already doubled.

That early detection depends on clean, daily attribution data that connects spend to installs to revenue. If you're reconciling campaign performance in weekly spreadsheets, you're seeing decay signals 5-7 days after they appear. By then, you've already wasted significant budget on a declining campaign.

Platforms like Linkrunner surface campaign-level performance shifts daily, showing CPI trends, ROAS changes, and creative-level metrics in real-time dashboards. That visibility turns campaign decay from a monthly crisis into a routine optimisation cadence.

Want to catch campaign decay before it drains your budget? Request a demo from Linkrunner to see how real-time campaign monitoring works in practice.