Compare/Marketing agencies
DataGlass vs a marketing agency for marketplace profit
A good agency brings creative, capacity, and platform-native expertise that an in-house team often cannot match alone. The structural limit is that agencies are paid on outputs the seller can see — campaigns, creatives, ad spend — not on the seller's contribution margin.
01/Sufficient
When an agency is the right hire
For brand, creative, content production, and the operational capacity of running campaigns at scale, an agency is often the cleanest answer. They have done it more times than the in-house team has.
- Creative production: video commerce assets, live-stream operations, photography
- Platform-native ad expertise across Shopee, Lazada, TikTok Shop
- Campaign-day operations during 11.11 / 12.12 / Mega Sale windows
- Capacity: running more campaigns than a small in-house team can sustain
02/Breakpoints
Where the agency model has blind spots
The structural issue is incentive alignment. Agencies are paid per campaign, per creative, or as a percentage of media spend. None of those metrics line up with the seller's contribution margin per SKU — and that is the metric that determines whether the campaign was profitable.
- Agencies usually do not have the seller's COGS, so true ROAS is computed against attributed revenue, not profit
- A campaign hitting its agency-side ROAS target can still be losing the seller money once full economics are applied
- Cross-platform tradeoffs (push this SKU on Lazada instead, where margin is healthier) are usually outside the agency's scope
- The agency's reporting cadence is weekly or campaign-end; the seller needs daily
03/Why
Why the model has these limits
An agency operates on the platform-side data — what they can see in the seller's ad accounts. The seller's real economics live outside that boundary: supplier cost, packaging cost, fulfillment cost, the strategic role of each SKU. Until those flow into the same view as the ad data, the agency is optimizing toward a target that is partial. Some agencies invest in this; most charge for the additional analytics work, and the analytics work itself is what DataGlass does.
04/DataGlass
How DataGlass differs
DataGlass and an agency are not substitutes — they sit on different parts of the operating chain. The agency runs the campaigns; DataGlass tells the seller (and, where useful, the agency) which campaigns are actually profitable per SKU.
- Per-SKU true ROAS the agency can see if the seller chooses to share it — closing the loop on which campaigns to scale
- Cross-platform comparison: which channel each SKU is most profitable on
- Stockout, margin, and pricing decisions that the agency typically does not handle
- A daily decision queue the seller can act on between agency review meetings
05/FAQ
Frequently asked
Probably not. The agency runs the campaigns; DataGlass tells you whether the campaigns are profitable. Many shops keep the agency for execution and use DataGlass for the profit-aware decision layer above the agency's work.
Yes — collaborator access is on the roadmap. Until then, exports and reports can be shared so the agency sees the same true-ROAS picture the seller does.
Some agencies do model COGS and full economics; that is great. The difference is that DataGlass is the system of record for the seller. Switching agencies, expanding to a new platform, or running a multi-agency setup leaves the data and the decision layer intact.
More comparisons