Original research · Dataset
Bangladesh Performance Marketing Benchmarks 2026
An open benchmark dataset for Bangladesh Meta Ads CPL, Google Ads ROAS, LinkedIn CPL, and GA4 attribution gap. Aggregated from 47 audited accounts and roughly USD 1.18M in media spend, 2023 to 2026. Ranges are inter-quartile; verticals with fewer than 3 accounts are excluded.
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The benchmark table
| Channel | Vertical | Metric | Median band | Top decile | Sample |
|---|---|---|---|---|---|
| Meta Ads | DTC Fashion (BD) | CPL (BDT) | 38 to 62 | 22 to 34 | 14 accounts |
| Meta Ads | Real Estate (BD) | CPL (BDT) | 180 to 320 | 95 to 140 | 6 accounts |
| Meta Ads | SaaS / B2B (BD) | CPL (BDT) | 420 to 780 | 220 to 340 | 5 accounts |
| Meta Ads | Development / NGO (BD) | CPL (BDT) | 35 to 55 | 18 to 28 | 3 accounts |
| Google Ads | DTC Ecommerce (BD) | ROAS | 3.4x to 5.2x | 6.8x to 11.4x | 9 accounts |
| Google Ads | Local Services (BD) | CPL (BDT) | 220 to 480 | 110 to 190 | 7 accounts |
| LinkedIn Ads | B2B Global (from BD) | CPL (USD) | 42 to 78 | 22 to 38 | 3 accounts |
| Attribution | GA4 vs Meta (BD DTC) | Data gap | 24% to 38% | 8% to 14% | 11 accounts |
Note: BDT ranges are before VAT. USD ranges use RBI 12-month median FX. Ranges represent inter-quartile bands; a single campaign outside a band is not evidence of failure, and a single campaign inside is not evidence of skill. Sample rules and exclusions are documented in the methodology section below.
Proprietary breakdown · USD 1.18M audited
Where the $1.18M went
The bands above sit on top of real spend distribution. This is the split by channel, vertical, and year across the 47 audited Bangladesh accounts. Verticals concentrate in DTC because that is where BD media budgets concentrate; B2B and LinkedIn are smaller pockets but carry the highest CPL and LTV.
By channel and vertical
| Channel | Vertical | Accounts | Audited spend | Share |
|---|---|---|---|---|
| Meta Ads | DTC Fashion (BD) | 14 | $410,000 | 35% |
| Google Ads | DTC Ecommerce (BD) | 9 | $220,000 | 19% |
| Meta Ads | Real Estate (BD) | 6 | $180,000 | 15% |
| Meta Ads | Development / NGO (BD) | 3 | $120,000 | 10% |
| Meta Ads | SaaS / B2B (BD) | 5 | $95,000 | 8% |
| Google Ads | Local Services (BD) | 7 | $85,000 | 7% |
| LinkedIn Ads | B2B Global (from BD) | 3 | $70,000 | 6% |
| Total | All BD verticals | 47 | $1,180,000 | 100% |
By year audited
| Year | Accounts | Audited spend |
|---|---|---|
| 2023 | 11 | $220,000 |
| 2024 | 15 | $380,000 |
| 2025 | 14 | $410,000 |
| 2026 | 7 | $170,000 |
Note: account counts by year exceed 47 because 8 accounts were audited across two consecutive years; each row is a distinct audit window, not a distinct brand. Spend is de-duplicated at the account level, so channel and year totals both reconcile to $1,180,000.
Methodology
The dataset combines direct account audits (Meta Ads Manager, Google Ads, LinkedIn Campaign Manager, GA4) performed between January 2023 and July 2026 across 47 Bangladesh brands. All accounts had at least 90 days of continuous spend at the time of measurement. Verticals were classified by the brand's primary revenue product, not campaign objective.
CPL medians use the trimmed mean of the middle 50 percent of accounts (25th to 75th percentile) to reduce single-account leverage. Top-decile is the 90th percentile of the same distribution. ROAS uses last-click GA4 revenue divided by platform-reported spend, matched to the same 90-day window. Attribution gap is defined as (Meta reported conversions minus GA4 attributed conversions) divided by Meta reported conversions.
All observed bands were cross-referenced against external anchors (Meta Global Business Benchmarks, Wordstream 2024, LinkedIn B2B Benchmarks, DataReportal Digital 2025 Bangladesh) to flag any BD-specific band that deviated more than 2x from the global anchor for the same vertical. No BD-specific band deviated more than 1.6x in the 2026 refresh.
Cited external sources
These sources were used as cross-check anchors. The dataset does not republish their proprietary numbers; it reports independent BD-specific bands and flags where BD deviates from global.
Frequently asked
Use this dataset
Journalists, analysts, and marketers can cite this dataset freely under CC BY 4.0 with a link back to this URL. For the underlying anonymised CSV, request access via the contact page.