Performance
The role of data in seller governance
branddi ·
You can even sell well on marketplaces, but without reliable data on price, sellers and Buy Box dominance, your brand may already be losing margin without realizing it.
Marketplaces concentrate around 80% of online sales in Brazil, and small variations in price or exposure quickly change results. But, when most of brands notices the changes, the margin has already dropped and the Buy Box has already changed hands.
If you want to get out of reactive mode and understand the role of data in seller governance, check out the guide below now!
What happens when a brand that sells through sellers doesn't have reliable data?
The lack of an integrated view on sellers on the marketplace leads to a scenario of imbalance: the brand cannot identify which seller dropped the price, who lost availability or why the competitive position changed.
In operations that work with marketplaces, structured data offers continuous and organized visibility over all sales actions.
Without real-time data, the operation operates in a model reactive. In other words, you and your team no longer govern the seller ecosystem and start responding to isolated events: processes, metrics and controls that allow the brand to monitor, classify and influence the performance of all sellers who sell their products on marketplaces.
Governance, in this case, is not measured by the ease of removing sellers indiscriminately. The objective is to create operational visibility and work under objective analysis criteria.
The brand needs to be able to respond, at any time:
- Who is active in the offer?
- Which sellers are above the official store?
- Which ones concentrate a greater share of sales?
- Which sellers only distort prices?
- Which ones have a known origin within the distribution policy and which ones have a presence that is not authorized?
What data really matters to govern sellers?
Marketplace governance depends on few objective indicators, capable of explaining loss of margin, Buy Box dominance and price distortions.
For To visualize how this happens in practice, see the essential data below:
Price vs. PMA
It is the percentage difference between the price announced by the seller and the Minimum Advertised Price (PMA) defined by the brand.
If the PMA is R$100 and a seller sells at R$92, there is an infraction of −8%. If three sellers repeat this behavior across 20 SKUs, price erosion starts to affect the perception of value of the entire product. This indicator shows where the margin starts to be lost.
Position in the Buy Box
It is the frequency with which each seller appears as a main offer over time. If a seller occupies the Buy Box in 70% of the day's measurements, while the official store appears in only 20%, he is capturing the majority of the conversion.
Even with a similar price, exposure dominance tends to concentrate revenue in this operator.
Volumetry of violations
It is the number of price or commercial policy violations, multiplied by the number of SKUs affected and the active time of the violation.
A seller that violates the PMA once in 1 SKU has a low impact. Another that maintains 15 SKUs below the PMA for 10 consecutive days creates structural price pressure.
Seller relevance
It is the level of seller participation in marketplace demand, observed by signs such as historical sales volume, reviews and operating time.
A seller with 5 thousand sales and reputation 4.8 tends to influence the result much more than one with 20 recent sales. This data guides where the intervention generates a concrete economic effect.
Signs of risk
It is the combination of low commercial relevance and high price aggressiveness. For example, a seller with few sales, but a constant discount of −15% on several SKUs, may not generate relevant revenue, but can pull the average price down and start a price war. Identifying this pattern early avoids loss of margin at scale.
How does the data define which sellers actually become a priority?
The difference between reacting late and acting correctly is knowing exactly who to prioritize. See below how the data shows this:
Crossing sales relevance and aggressiveness
The classification is based on two objective numbers: participation in Buy Box or revenue (relevance) and percentage difference to the PMA (aggressiveness).
Example: a seller with 30% Buy Box dominance selling 10% below the PMA can reduce the average price of the SKU and capture a large part of the demand daily. In a product that sells 100 units/day at R$100, this deviation can shift around R$300 of margin per day out of the brand's strategy.
A seller with −15% price, but only 1 unit sold per day, generates minimal financial impact.
Therefore, the operation needs to concentrate effort on sellers that are simultaneously relevant and aggressive, as they are the ones who change the economic result and condition the behavior of the marketplace algorithm.
Knowing who the seller is allows you to truly act
To correct the price, apply commercial policy or block irregular distribution, the brand needs to know which CPF or CNPJ is behind the operation.
This process usually follows three steps:
- Crossing on public bases and signs digital;
- Manual investigation when the data does not match;
- Purchase of the product to obtain the invoice and formally identify the seller.
Did you like the tips? Remember: on marketplaces, losing control over price, sellers and Buy Box almost never happens all at once, it is a process that erodes customer trust over time.
If governing sellers with data is your priority, shielding your brand is the next step. Talk to Branddi and find out how to protect your positioning before the loss becomes irreversible.