8. What Is DeepBI’s Automatic Bid Adjustment Logic?

DeepBI’s automatic bid adjustment does not simply raise or lower all bids based on data from a single day. Instead, it uses the ongoing performance of advertising activities as the basis for daily bid and budget iterations. The system focuses on clicks, conversions, spend, and ACOS over the previous seven days. It gradually searches for a more suitable advertising range while also considering the traffic stage, the conversion quality of keywords or competitor ASINs, budget consumption, and inventory. As a result, different advertising activities can receive different treatment rather than following one universal bidding rule.

Using Seven-Day Data to Identify the Underlying Trend

Advertising performance can fluctuate, and clicks, orders, and attribution in Amazon advertising may also be delayed. If bids are adjusted solely according to the day’s impressions, clicks, or orders, short-term events—such as a promotion, a competitor’s price change, or an occasional traffic spike—may be mistaken for a long-term trend. For this reason, DeepBI uses the combined performance of the previous seven days as its primary reference. It evaluates the clicks, conversions, actual spend, and ACOS generated by advertising activity as a whole.

When a type of traffic continues to generate orders over a period of time and its ACOS remains within an acceptable range, the system may determine that the traffic has a basis for additional testing or increased investment. If traffic continues to consume spend without producing enough conversions, or if ACOS remains high over time, the system gradually tightens bids and limits exposure. This helps prevent additional budget from flowing toward lower-quality traffic.

The term “gradually” is important. Each adjustment needs to leave enough time for new data to accumulate. Making large or repeated changes too quickly can create strategy fluctuations and make it difficult to determine whether an adjustment actually improved performance.

Coordinating Bid Adjustments with the Four-Level Traffic Funnel

Automatic bid adjustment does not operate independently of traffic structure. DeepBI progressively evaluates advertising traffic through four levels: the exploration level, initial screening level, precision level, and scaling level.

The exploration level uses automatic campaigns, different match types, and competitor ASIN advertising to broaden coverage. It records users’ search terms and potential opportunities to generate orders. The initial screening level focuses on keywords and ASINs that have produced new orders, using ACOS as part of the first round of filtering. At the precision level, the system continues testing and comparing long-term conversion performance, removing traffic that converted only because of a short-term or isolated event. The scaling level concentrates on high-quality targets with strong click-through rates, high conversion rates, and relatively low ACOS.

The bidding objective is different at each level. During exploration, some room for testing needs to be preserved. Traffic should not be stopped immediately just because it has not produced an order in the short term. After traffic reaches the precision level, continued conversion and cost stability become more important. At the scaling level, bids or budgets may be increased to obtain more effective impressions, particularly for important placements involving keywords with strong conversion performance.

This means that automatic bid adjustment follows a sequence of testing, screening, and resource concentration. It does not apply the same bid rule to every keyword, ASIN, or advertising activity.

Coordinating Budget, Inventory, and ACOS Controls

Bids determine how actively an advertising activity competes for traffic, while the budget determines how much traffic the activity can support. DeepBI combines bid adjustments with intelligent budget flows and dynamic budget reallocation. Advertising activities with more stable performance and stronger conversion quality have a greater opportunity to receive additional budget. Activities that continue to consume budget without producing sufficient results may require lower bids or budget limits to reduce ineffective spending.

Inventory is also an important constraint in bid decisions. When inventory is sufficient and advertising performance is healthy, effective traffic can be maintained or expanded. When inventory is limited, the system needs to avoid continuing to compete for traffic at a high cost. Otherwise, advertising spend may become misaligned with the product’s ability to fulfill demand.

Seasonal products require an additional balance between the sales cycle and inventory changes. A single bid level should not be maintained indefinitely when demand patterns and available inventory are changing. Bid and budget decisions therefore need to account for the relationship between expected sales timing, inventory conditions, and advertising performance.

Why DeepBI Does Not Adjust Bids Continuously in Real Time

DeepBI adjusts bids daily to match the update and attribution cycle of Amazon advertising. After a bid changes, impressions, clicks, orders, and ACOS do not necessarily reflect the full effect of that change within a few hours. If bids are repeatedly modified during the same day, the system may change the strategy again before enough data is available to evaluate the previous adjustment. This makes it difficult to determine whether the earlier change was effective.

The core feedback loop is therefore to collect data over a period of time, identify the underlying trend, make a small adjustment according to the advertising activity and traffic level, and then observe the result after the adjustment. This approach addresses cost control while preserving room to test high-potential keywords, competitor ASINs, and opportunities for growth in organic traffic.

Summary

DeepBI’s automatic bid adjustment logic can be summarized as seven-day performance evaluation, daily iterative adjustment, traffic management by funnel level, and coordinated control of budgets and inventory. The system does not decide to raise or lower a bid based only on a single day’s orders or one isolated metric. Instead, it considers clicks, conversions, spend, ACOS, and longer-term traffic performance, gradually shifting resources away from lower-quality traffic and toward keywords and ASINs with more consistent conversion potential.