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Case study · Meta + TikTok + Google Ads

EGP 120.86M in store revenue. Judged at the till.

33 months of Meta, TikTok and Google Ads, measured against real store revenue instead of platform attribution. Monthly ad spend grew from EGP 308K to EGP 903K and the store kept agreeing.

Client
Egyptian casual apparel brand
Window
Oct 2023 to Jun 2026
Basis
Store revenue, GA4

5.78×

Blended return on all ad spend

Store revenue over total spend

Store revenue (GA4) over Meta, TikTok and Google Ads spend. Oct 2023 to Jun 2026.

EGP 120.86M

Store revenue

33 months

Store revenue, GA4. Oct 2023 to Jun 2026.

82,928

Store orders

Store orders, GA4. Oct 2023 to Jun 2026.

EGP 20.90M

Ad spend, three channels

Meta, TikTok and Google Ads spend. Oct 2023 to Jun 2026.

Q4 2023Q2 2025Q2 2026+38% on Q2 2025
Store revenue (GA4) by quarter, Q4 2023 to Q2 2026. Q2 2026 against Q2 2025, the same quarter a year earlier.

Monthly ad spend and store revenue

Oct 2023 to Jun 2026, all channels. Every line starts at 100 in Oct 2023, so the lines show the shape of the growth, not amounts.

  • Ad spend
  • Store revenue
01,0002,0003,000100Oct 2023Feb 2025Jun 2026

Basis: Store revenue, GA4.

Every point in this chart
MonthAd spendStore revenue
Oct 2023100100
Nov 2023129180
Dec 2023362219
Jan 202439886.6
Feb 202444195.6
Mar 2024557119
Apr 2024770167
May 2024782111
Jun 2024751142
Jul 20241,055128
Aug 20241,143115
Sep 20241,329153
Oct 20241,354125
Nov 20241,959399
Dec 20242,347271
Jan 20251,448176
Feb 20251,078124
Mar 20251,522218
Apr 20251,540177
May 20251,300146
Jun 20251,318138
Jul 20251,582158
Aug 20251,869168
Sep 20251,913179
Oct 20251,737195
Nov 20252,579507
Dec 20251,939287
Jan 20261,615170
Feb 20261,101109
Mar 20261,680218
Apr 20261,911178
May 20262,561272
Jun 20262,097185

A growing account with no honest scoreboard

Every return figure on the table was platform-attributed: each ad platform grading its own homework.

When several channels claim the same sale, adding up their reports overstates what the media earned, and budget decisions made on those numbers drift upward until the store stops agreeing.

The brand needed one number the bank account would recognise, and an account structure that could keep scaling against it.

Measure against the store, then scale

  • Store revenue became the scoreboard. GA4 store revenue divided by total ad spend replaced platform-reported ROAS as the number every budget call answered to.
  • Three channels, each with a job. TikTok and Google Ads opened beside Meta, and each kept the budget it could justify against the same store number.
  • A testing cadence fed scaling. Structured creative testing ran continuously, and winners graduated into a dedicated scaling campaign instead of being chased one at a time.
  • Strategy shared with the in-house team. We worked alongside the brand's own media buyer, owning structure, measurement and the testing rhythm.

Spend scaled and the store kept agreeing

Across 33 months the store booked EGP 120.86M against EGP 20.90M of ad spend on three channels: 5.78× blended, over 82,928 orders.

In the first twelve months ad spend averaged EGP 308K a month at 8.79× blended. From Oct 2024 to Sep 2025 it averaged EGP 757K at 5.04×, and from Oct 2025 to Jun 2026 EGP 903K at 5.24×.

The ratio came down as spend grew, which is what an honest denominator shows. What matters is that real revenue kept pace with real spend.

A note on measurement: the blended return here is store revenue divided by total ad spend on Meta, TikTok and Google Ads. It sidesteps platform over-counting. It also credits organic sales to the media, which is why the earliest months, when ad spend was small, show very high ratios, and why no growth multiple is quoted.

What carries over

  • Pick the denominator before the budget. Store revenue over total spend is the one return figure two channels cannot both claim.
  • Expect the ratio to fall as you scale. Early months flatter any account. The real question is whether revenue keeps pace with spend.
  • Give every channel a job. TikTok and Google Ads earned their budgets against the same store number as Meta.
  • Testing is the engine. A steady testing cadence is what made scaling repeatable rather than lucky.

Next case study · Egyptian fashion label

8.14× on Meta while spend grew 11.6×.

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