DeepBI does not simply assign a fixed set of keyword campaigns to every SKU. Instead, it evaluates product relevance, keyword conversion performance, ACOS, budget, inventory, and other advertising signals. Keywords may be shared across related SKUs when they perform well for each product, while differences in relevance or performance can lead to separate targeting, bidding, budget, or exposure controls. Through a four-layer funnel, DeepBI can explore search terms, evaluate their results, and adjust management according to each SKU’s actual performance. This approach supports differentiated keyword management rather than a rigid one-SKU-to-one-keyword structure.

7. Does DeepBI Split Keyword Ads by SKU?

DeepBI does not manage keyword advertising through a simple fixed rule such as “one SKU has one set of keywords” or “each keyword can advertise only one SKU.” Instead, it evaluates product relevance, advertising performance, and operating objectives for different ASINs or SKUs. Keywords are then screened and categorized, with differentiated controls for bids, budgets, and exposure applied when necessary. As a result, whether keywords are split, and how extensively they are split, depends on the differences between SKUs and the actual performance of each keyword for each product.

The Relationship Between Keywords and SKUs Is Not a Rigid One-to-One Structure

Multiple SKUs in the same store may share some core keywords, while other keywords may correspond separately to product specifications, features, use cases, or long-tail searches. If several SKUs are highly relevant to a search term and can each generate meaningful clicks and conversions, there is no need to separate the traffic completely simply because the SKUs are different.

The opposite is also true. If a keyword is suitable for only one SKU, or if its click-through rate, conversion rate, order value, and ACOS differ substantially across products, more detailed keyword assignment and advertising controls may be appropriate.

DeepBI’s advertising strategy includes mechanisms such as automatic keyword addition, ASIN addition, and search-term feedback. These mechanisms can collect real user search terms during the exploration stage and then use product advertising data to evaluate keyword quality. This process is closer to differentiated management based on SKU performance than to an advertising structure created in advance through static rules.

The Four-Layer Funnel Gradually Screens Keywords for Different SKUs

At the exploration layer, DeepBI can use AUTO campaigns, different match types, and competitor ASIN advertising to expand traffic coverage. It also records the original search terms that generate clicks and orders. At this stage, the main objective is to discover opportunities, so keywords may not yet be strictly assigned to a particular SKU.

After entering the initial screening layer, the system considers whether a keyword has generated orders recently and whether it meets the established ACOS standard. At the precision layer, keywords and competitor ASINs are tested multiple times to evaluate their longer-term conversion performance. This helps reduce incorrect judgments caused by an isolated order or short-term fluctuations.

The scaling layer focuses on traffic that demonstrates relatively high click-through rates, relatively high conversion rates, and relatively low ACOS. The screening results may differ by SKU. A keyword may become a scaling keyword for SKU A, while only being suitable for a low-budget test for SKU B. For another product, the same keyword may require reduced exposure.

This means that the four-layer funnel does not necessarily produce one completely separate keyword structure for every SKU. Instead, it can produce different levels of control based on how the same keyword performs for each product.

Budget, Bids, and Inventory Also Affect Post-Split Management

Whether a keyword is managed independently does not depend only on whether the keyword name is the same. It also depends on the operating data generated after the keyword is launched. DeepBI’s dynamic adjustment mechanism can modify campaign bids and budgets according to recent clicks, conversions, spending, and ACOS performance. The combined data from the previous seven days is used as the primary reference to help filter short-term noise and delays in advertising attribution.

If an SKU converts consistently and has favorable profit room, its related keywords may receive a larger budget or more active bidding. If another SKU receives many clicks but does not generate enough conversions, its exposure may be limited, its bids may be reduced, or its budget may be tightened.

Inventory status can also affect this decision. Inventory-budget linkage and SKU shutdown strategies can help avoid continued advertising spending when a product does not have sufficient inventory. Therefore, even when multiple SKUs use the same keyword, they do not necessarily receive the same advertising intensity or follow the same management approach.

For product combinations with substantially different keyword sets, splitting campaigns can make it easier for operators to evaluate each SKU’s search terms, costs, and contribution to orders. However, when products are highly similar and their keywords overlap significantly, unnecessary splitting should be avoided. Splitting without sufficient evidence can distribute the data across too many campaigns, extend the learning period, and make budget management more complicated.

How to Understand the Strategy in Practice: Dynamic Grouping by Relevance and Performance

The accurate interpretation of whether DeepBI splits keyword advertising for different SKUs is that the system supports and can apply differentiated management according to SKU, ASIN, keyword relevance, and advertising performance. However, the available information does not support describing the process as an automatic requirement for every SKU to use a completely independent keyword advertising structure.

The advertising structure can be separated according to product differences. Keywords can also be reassigned based on search-term feedback and conversion results. When keywords are shared, differences can be reflected through tiered bidding, budget reallocation, and exposure controls rather than through complete structural separation.

From an operational perspective, it is more important to monitor the clicks, orders, CVR, ACOS, and inventory status generated by each SKU’s keywords than to focus only on whether the keywords have been divided into separate campaigns. Listing elements, including the title, bullet points, and keyword placement, also affect product relevance and conversion performance. Advertising data and Listing optimization should therefore remain aligned. Otherwise, even after campaigns are split at the SKU level, a product may still receive substantial exposure but generate few conversions.

Summary

DeepBI does not apply a rigid, one-size-fits-all split of keyword advertising by SKU. It first explores keywords, then uses a four-layer funnel to screen traffic quality, and adjusts allocation based on SKU performance, ACOS, budget, bids, and inventory.

The same keyword may be used by multiple relevant SKUs, but the advertising intensity and management method do not have to be the same. When SKU differences are significant, independent or differentiated controls may be more appropriate. The key is to manage keywords according to relevance and actual performance rather than splitting every SKU through a fixed rule.