How a Mobile Agency Manages 80+ Client Apple Search Ads Accounts With One Operator

Note: This case study reflects a real workflow pattern observed across multiple ASAPilot Agency-plan customers. Account details, agency identity, and exact figures have been anonymized at the customer’s request.

The agency

A three-person mobile growth agency based in EMEA, founded in 2022. The team grew organically to 80+ active client accounts spanning productivity, health/fitness, finance, and casual games verticals. Aggregate monthly ad spend across the portfolio: ~$1.2M.

The senior partner ran day-to-day Apple Search Ads operations. Two junior team members owned keyword research, creative briefs, and client communications.

The problem

By mid-2025 the daily routine had become unsustainable:

• 6-7 hours/day of dashboard hopping. Open each client’s ASA dashboard, eyeball spend pacing, scan top keywords, check yesterday’s CPI deltas.
• 3-4 week audit backlog. Comprehensive audits (search term review, negative keyword expansion, structural cleanup) were scheduled monthly per client but kept slipping.
• Two budget incidents in Q2 2025. Both involved overnight spend acceleration on Discovery campaigns that were not flagged until the next morning. Aggregate damage: ~$14K in unplanned waste, plus client trust damage on both accounts.
• No real overflow plan. New client onboardings were stalling because the senior partner was the only one with the context to run audits, and the queue was too long.

The triggering decision: either hire a third operator (estimated ~$60K/year fully loaded) or find tooling to multiply the existing team’s leverage by 3x.

The implementation

The agency adopted ASAPilot on the Agency plan ($299/month) in February 2026 and ran a 4-week parallel rollout:

Week 1: Account connection and baseline

• Connected 80 client accounts via the Apple Search Ads OAuth flow (each <2 minutes).
• Ran the full app-first audit across all accounts in a single batch. The first run surfaced 47 zero-conversion keywords, 12 over-pacing campaigns, and 6 burning Search Match ad groups across the portfolio. Estimated savings if all were actioned: ~$8.5K/month.
• Reviewed each flagged item with the client team. Acted on 38 of the 47 zero-conversion keywords; rebalanced 12 over-pacing campaigns; tightened 6 Search Match groups.

Week 2-3: Automations

Three automations replaced the manual daily review:

1. Daily Summary (8:00 UTC, all accounts) — one digest email covering every account’s spend pacing, top movers, and any flagged anomalies. The partner reads this with morning coffee instead of opening 80 dashboards.
2. CPI Anomaly Alert (sigma-based, per app) — pushes to Slack the moment any campaign’s CPI exceeds 2σ above the trailing 14-day baseline. The team can stop a leak within hours rather than the next morning.
3. Weekly Zero-Conversion Sweep (Mondays, all accounts) — surfaces search terms with

   50 taps and 0 installs over the prior 14 days. Junior team owns turning these into negatives.

Week 4: Workflow handoff

The senior partner trained both juniors on the ASAPilot AI chat console. Examples of queries they now run autonomously:

“Which apps had their CPI drift up more than 30% this week?” “Show me all Discovery campaigns above $500/day across the portfolio.” “Which Search Match ad groups have spent more than $1K and converted under 25%?”

The outcome (60 days post-rollout)

Metric	Before	After	Change
Daily ASA management time (senior partner)	6.5h	1.5h	-77%
Audit backlog	3-4 weeks	Cleared	—
Budget incidents (60 days)	1 (~$7K)	0	—
New client onboardings (60 days)	1	5	+400%
Average client CPI across portfolio	$4.20	$3.65	-13%

The senior partner reallocated the recovered ~5 hours/day toward strategic work: new client acquisition, creative strategy reviews, and onboarding a fourth team member (a creative strategist, not another operator).

What worked

The single biggest lever was the automations. The first-week audit findings were valuable, but the compounding gain came from never having to manually scan 80 accounts again. The agency now operates a “by exception” model — review only what is flagged.

Read-only safety mattered. The agency had previously tried two campaign-management tools with autonomous write modes; both produced edge-case mistakes (e.g., paused the wrong ad group during an A/B test) that damaged client trust. ASAPilot’s read-only default meant the team retained final say on every recommendation.

AI chat scaled the junior team. Pre-ASAPilot, the juniors needed the senior partner to write SQL-shaped questions (“show me the top X by Y”). Post-rollout, the juniors run those queries directly in plain English, freeing the senior partner from being a bottleneck.

What was harder

• Mapping client-reporting cadence took 4 hours. Each client expected a different reporting rhythm (some weekly PDFs, some monthly decks, some Slack-only). Building the automation library to match took longer than the technical setup.
• The first month of Slack alerts was noisy. Sigma thresholds had to be re-tuned per app — 2σ is too sensitive for low-volume apps. The agency standardized on 2.5σ for apps below $100/day spend.

Recommended for

This pattern works best for agencies with:

• 30+ client accounts and growing
• Mixed verticals (so audit findings vary)
• A “by exception” management philosophy
• Senior operators who are bottlenecks for routine review

It is less applicable for in-house single-app teams where the daily routine is already manageable.

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See pricing for the Agency plan, or read the audit checklist for the structure used in week-1 baseline audits.