What a 40% Ad Budget Reallocation Actually Does to ROAS: A Before/After Attribution Model

The Problem: A Gut Feeling Isn't a Framework

Most local business owners who've run paid ads for more than six months develop a nagging suspicion: one of these channels isn't pulling its weight. Maybe Facebook looks busy — lots of clicks, decent reach — but the phone isn't ringing. Maybe Google Search feels expensive but the leads that come in actually close.

The problem isn't the suspicion. The problem is the absence of a model to test it.

Without a before/after attribution framework, every budget decision is a guess dressed up as strategy. This article walks through a labeled hypothetical model — realistic inputs, real math, zero invented client data — so you can see exactly what a 40% reallocation does to ROAS, and then run the same logic on your own numbers.

Setting the Baseline: The 'Before' State

Let's define our hypothetical local service business. For this model we'll use:

• Total monthly ad budget: $5,000
• Channel A (Meta/Facebook): $3,000 (60% of budget)
• Channel B (Google Search): $2,000 (40% of budget)
• Average job/transaction value: $600 (illustrative — adjust to your own)
• Close rate on inbound leads: 30% (a rough mid-range estimate for local services; your number may differ)

Now we layer in channel-level conversion rates. This is where most business owners have a blind spot — they track total leads, not leads-by-channel.

Channel A (Meta) — Before:

• Cost-per-click: $1.50 (illustrative)
• Landing page conversion rate: 2% (for context on what 'normal' looks like, see our article Local Service Landing Page Conversion Rate: Benchmarks)
• Cost-per-lead (CPL): $75 (illustrative: $1.50 ÷ 0.02)
• Leads generated: ~40
• Booked calls/appointments (at 30% close): ~12
• Revenue attributed: ~$7,200
• ROAS: 2.4x ($7,200 ÷ $3,000)

Channel B (Google Search) — Before:

• Cost-per-click: $8.00 (illustrative; search CPCs for local services typically run higher than social)
• Landing page conversion rate: 5% (search traffic tends to convert better — higher intent)
• Cost-per-lead: $160 (illustrative: $8.00 ÷ 0.05)
• Leads generated: ~12
• Booked calls (at 30% close): ~4
• Revenue attributed: ~$2,400
• ROAS: 1.2x ($2,400 ÷ $2,000)

Blended baseline ROAS: ~1.96x ($9,600 total revenue ÷ $5,000 total spend)

At first glance, Meta looks like the winner. It's producing more leads and more revenue. But watch what happens when we look at cost per booked call rather than cost per lead — a distinction we dig into in our article Cost Per Booked Call vs Cost Per Lead: The Real Math.

• Meta cost per booked call: ~$250 ($3,000 ÷ 12)
• Google cost per booked call: ~$500 ($2,000 ÷ 4)

Google looks worse on both metrics so far. But that's because we haven't accounted for lead quality — and that's exactly what the reallocation will expose.

The Reallocation: Shifting 40% of Budget

Now we move 40% of the Meta budget ($1,200) into Google Search. The new split:

• Channel A (Meta): $1,800
• Channel B (Google Search): $3,200

We hold all per-click and conversion-rate inputs constant — no heroic assumptions. The only thing changing is where the dollars go.

The 'After' State: Running the New Numbers

Channel A (Meta) — After:

• Budget: $1,800
• CPL: $75 (unchanged, illustrative)
• Leads: ~24
• Booked calls (30% close): ~7
• Revenue: ~$4,200
• ROAS: 2.33x

Channel B (Google Search) — After:

• Budget: $3,200
• CPL: $160 (unchanged, illustrative)
• Leads: ~20
• Booked calls (30% close): ~6
• Revenue: ~$3,600
• ROAS: 1.125x

Blended ROAS After: ~1.56x ($7,800 ÷ $5,000)

Wait — that's lower. So the reallocation made things worse?

Yes, in this model — because we haven't changed the close-rate assumption yet. This is the critical insight: if Google and Meta leads close at the same rate, and Google's CPL is higher, you should be on Meta.

But here's what typically happens in real local service accounts: Google Search leads close at a meaningfully higher rate because the prospect was actively searching for a solution. Let's re-run with a revised close rate for Google of 50% (still illustrative, but representative of higher-intent traffic):

Channel B (Google Search) — After, Adjusted Close Rate:

• Leads: ~20
• Booked calls (at 50% close): ~10
• Revenue: ~$6,000
• ROAS: 1.875x

New Blended ROAS: ~2.04x ($4,200 + $6,000 = $10,200 ÷ $5,000)

That's a ~4% lift in blended ROAS from the same total budget — and more importantly, total revenue increases by ~$600/month despite spending the same amount. Annualized, that's roughly $7,200 in incremental revenue for a business already spending $5,000/month on ads.

The model also reveals something agencies often hide: when you report by impressions or total leads, the close-rate difference between channels stays invisible. This is exactly the dynamic we describe in Why Agencies Report Impressions Instead of Revenue.

The Three Inputs That Drive the Outcome

This model is only as useful as the inputs you feed it. The three levers that determine whether a reallocation helps or hurts:

1. Channel-level conversion rate (lead form or landing page). If you're sending traffic from different channels to the same page and not tagging UTM parameters, you cannot separate these. Fix tracking first.

2. Channel-level close rate. This requires your sales/CRM data, not just your ad platform data. A lead who fills out a form versus a lead who calls from a search ad are not the same prospect.

3. Average job/transaction value by channel. In some businesses, social traffic tends to attract price-shoppers while search traffic attracts buyers with urgency. If your average ticket differs by channel, your ROAS math must reflect that.

A rough rule of thumb: if two channels show a >15-point gap in close rate, that gap will almost always override the CPL difference when you calculate true ROAS.

How to Build This Model for Your Own Account

You don't need a data scientist. You need a spreadsheet and four weeks of clean data.

Step 1: Pull cost, clicks, and leads broken out by channel (Meta Ads Manager + Google Ads, side by side).

Step 2: Match those leads to your CRM or call log. Tag each lead's source. Count how many became booked appointments and how many closed.

Step 3: Calculate CPL, cost-per-booked-call, and cost-per-closed-job for each channel separately.

Step 4: Build the before/after model above using your actual numbers. Try a 20%, 40%, and 60% reallocation scenario.

Step 5: Run the winning scenario for 60–90 days before drawing conclusions. Channel performance shifts with seasonality, audience saturation, and bid competition.

If your tracking isn't set up to produce clean channel-level data at step 1, that's the real problem — and it's solvable before you move a single dollar.

The Bottom Line

The point of this model isn't to prove that Google beats Meta or vice versa. The point is that budget allocation decisions made without channel-level close-rate data are coin flips. A channel that looks expensive on CPL can be your highest-ROAS channel once you account for lead quality — and a channel that floods you with cheap leads can be silently destroying margin.

A 40% reallocation is not a small move. In the model above, it either hurts blended ROAS or helps it by ~$7,200/year depending on a single input: close rate by channel. That's the number most local businesses aren't tracking.

If you want to run this model against your actual account data — and see where your budget is leaking — book a call with the Nika Spark team. We'll pull your channel-level numbers, build the attribution model with your real inputs, and show you exactly what a reallocation would do to your ROAS before you move a dollar.