Maintaining 1.5x–3.5x ROAS at $30K Monthly Spend For an Aftermarket Battery Brand

with Google Ads, Google Shopping, Remarketing

Executive Summary

An automotive aftermarket battery brand operating in a competitive replacement market required stable, predictable paid media performance at a consistent five-figure spend level.

Over a 30-month period, Google Ads, Google Shopping, and remarketing programs maintained steady budget levels while ROAS fluctuated within expected bounds. Strong months exceeded 3x. Compressed months fell below 2x. The system recovered without destabilizing spend or requiring reactive restructuring.

This was not a growth spike. It was controlled performance over time.

The account behaved like a mature system rather than a fragile one dependent on ideal conditions.

The Challenge

Aftermarket battery demand is cyclical and influenced by seasonality, pricing pressure, and shifting competitive bids.

In this environment, volatility is normal. Instability is not.

The objective was not to chase aggressive ROAS targets. It was to:

  • Maintain consistent five-figure monthly spend
  • Absorb natural performance compression
  • Preserve contribution during weaker periods
  • Allow efficiency to normalize without budget whiplash

Accounts that rely on constant budget changes to manage performance typically signal structural weakness. This brand needed durability, not dramatic swings.

SCUBE’s Approach

The account was structured to prioritize stability over short-term optimization.

Budget Discipline

Monthly ad spend was maintained consistently in the $28,000–$30,000 range. Budget was not aggressively pulled during compressed ROAS months, preventing signal disruption and auction instability.

This preserved platform learning and allowed recovery periods to occur naturally.

Channel Balance

Google Search, Google Shopping, and remarketing each served defined roles:

  • Search captured high-intent demand
  • Shopping surfaced SKU-level visibility
  • Remarketing preserved return traffic and improved blended efficiency

Channels complemented each other rather than competed for attribution credit.

Signal Monitoring

Performance was evaluated within ranges rather than against single-month targets.

Instead of reacting to individual dips, trend stability was prioritized. Compression was tolerated if it remained within expected bounds and did not compound over multiple periods.

This reduced overcorrection and preserved system integrity.

Results

Across 30 months:

  • Monthly ad spend remained consistently around $28K–$30K
  • Average ROAS held just above 2x across the full period
  • Strong months reached 3x–3.5x
  • Compressed months occurred but did not persist
  • Budget stability remained intact despite performance fluctuation

Cost remained steady even as ROAS moved within a normal operating range.

Efficiency recovered without forced budget cuts. The account did not require structural resets to regain performance.

This is what controlled paid media performance looks like at scale.

Operator Perspective

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What Changed Structurally

  • Budget was treated as a stabilizing force, not a lever for panic response
  • Channels were assigned roles instead of blended into a single efficiency metric
  • ROAS was evaluated over trend windows rather than isolated months
  • Recovery behavior was built into expectations

The outcome was not peak performance. It was durability. And durability compounds.

Lets Talk

See what your demand capture is actually doing

The SCUBE Game Plan is a focused review of how complex, spec-driven catalogs behave inside paid channels. It’s designed to surface what’s contributing to performance, what’s masking underlying issues, and where structure is quietly working against you. If there’s a fit, we walk through the findings in a ~60 minute conversation, looking at:

  • which parts of the catalog are contributing profit versus absorbing spend
  • campaign insights
  • which constraints are shaping results over the next 90 days

The goal is a clearer picture of how the system is behaving, so decisions stop relying on averages or assumptions.

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