How to Launch and Scale Your Amazon Ad Campaigns

Ever launched an Amazon ad campaign only to see a small influx of orders and nothing more?

If yes, you’re not alone. We know how frustrating that feels, especially after putting time, budget, and effort into getting everything live. The reality is that nearly 90% of eCommerce businesses struggle with Amazon ads at some point, even those with strong products and clear growth goals. 

At first, it’s easy to assume the fix lies in tightening the fundamentals - better creatives, competitive pricing, or a “solid” high-level strategy. But more often than not, that’s not where things break.

The real issue is a lack of granularity in metrics and decision-making. 

On Amazon, success isn’t driven by broad performance views; it’s driven by understanding what’s happening at the keyword, search term, placement, and ASIN level. Even the best product can fail if your marketing strategy lacks these granular insights.

So how do you expand and refine your Amazon advertising strategy to ensure every dollar you spend is an investment, not a gamble? That’s exactly what this article covers, broken down into three clear parts:

• Part 1: Common terms you need to know for Amazon ads
• Part 2: Launch - Establishing a data-driven campaign architecture
• Part 3: Scale - Implementing a repeatable process for optimization and expansion

But, firstly to make your Amazon ads working, identifying the root cause is crucial. From there, you can build a strategic, insight-led Amazon PPC campaign. Once traction follows, scaling becomes a controlled, repeatable process - not a leap of faith. 

Let’s dive right in! 

Key Amazon Advertising Terms You Need to Know

Before you launch or scale any Amazon ad campaign, it’s critical to understand the three core ad formats and the role each one plays in your growth strategy.

• Sponsored Products (SP) are keyword- and ASIN-targeted ads that promote individual product listings directly within search results and product detail pages. These ads sit closest to the point of purchase and are typically the highest-converting format. This is why most Amazon ad frameworks, and this guide, begin with mastering Sponsored Products.
• Sponsored Brands (SB) showcase your brand logo, a custom headline, and multiple products at the top of search results. They are ideal for brand discovery, capturing high-intent searches, and driving traffic to Storefronts.
• Sponsored Display (SD) ads extend reach beyond search, allowing you to retarget shoppers based on browsing behavior and product views.

Phase 1: Launch - Establishing a Data-Driven Campaign Architecture

In this phase, the goal is to build a clean, structured Amazon Ads account that clearly shows how shoppers discover, evaluate, and purchase your products.

Without this foundation, scaling later becomes guesswork.

Step 1: Budget Allocation & Expectations

In the early stages, your ad budget is a research tool, not a profit lever. Many brands fail here because they expect efficient returns before Amazon’s algorithm and their own team has enough data to learn from.

You should allocate a dedicated test budget that you’re comfortable investing purely for insights. During this phase, higher ACOS and TACOS are expected and acceptable. These metrics will normalize only after you identify eCommerce high-intent keywords and winning search patterns. 

Actions to take:

• Define a fixed testing budget that can run consistently for 3-4 weeks without interruptions.
• Set performance expectations internally: success equals usable data, not ROAS.
• Monitor spend pacing daily to ensure data volume grows steadily without budget shocks.

Step 2: The Initial Data-Gathering Campaign

Your first campaign should always be a Sponsored Products (SP) Auto-Targeting campaign. Auto campaigns allow Amazon to match your product to real customer searches, uncovering terms you would never think to target manually. 

This campaign must be allowed to run long enough to reach statistical relevance. Cutting it early because of poor efficiency defeats its purpose. What you’re buying here is visibility into how Amazon understands your product and how customers actually search. 

Actions to take:

• Launch one SP Auto campaign with broad discovery enabled across close, loose, substitutes, and complements.
• Set conservative bids that allow for reach without overspending.
• Let the campaign run uninterrupted for several weeks to build a meaningful dataset.

Step 3: Data Analysis and Keyword Harvesting

Once enough data is collected, you now need to get the Search Term Report. It shows exactly which customer searches triggered your ads and which of those searches resulted in clicks and sales.

The focus here is on identifying high-intent, conversion-driven terms, not vanity traffic. These insights will directly inform your manual keyword campaigns in the next phase.

Actions to take:

• Pull the Search Term Report and sort by conversions and orders.
• Identify search terms with strong conversion signals, even if ACOS is high.
• Export and categorize these terms to use as exact and phrase keywords in future campaigns.

Step 4: Building Manual, Segmented Campaigns

Once you’ve harvested high-intent search terms, the next step is control. Manual Sponsored Product (SP) campaigns allow you to decide how aggressively to bid, where to show, and how to evaluate performance by intent type. Segmentation prevents data dilution and makes optimization measurable.

Create five separate manual SP campaigns:

• Keyword-Targeted Campaigns
  
  
  
  • SP | Keyword | Generic: Non-branded, intent-driven searches (highest scale potential).
  • SP | Keyword | Brand: Defensive, high-conversion traffic you should always own.
  • SP | Keyword | Competition: Capture shoppers evaluating alternatives.
    
    
• Product-Targeted Campaigns
  
  
  
  • SP | Product: ASIN-level targeting against competitors or complements.
  • SP | Category: Broader discovery and scale within your category.

Each campaign type behaves differently, which is why separation is critical.

Actions to take:

• Create five individual manual SP campaigns with tightly themed ad groups.
• Match harvested search terms to the correct intent bucket before launch.
• Set differentiated bids based on conversion intent and competitive pressure.

Step 5: Account Hygiene and Keyword Negation

As you add manual campaigns, failing to control overlap will cause internal competition and wasted spend. This is where disciplined negation protects efficiency.

Return to your original SP | Auto campaign and negate every keyword you’ve moved into manual targeting. This forces Amazon to stop spending on terms you now actively manage.

The auto campaign’s role becomes singular: discover new search behavior, not scale known winners.

Actions to take:

• Add harvested keywords as negative exact or phrase in the auto campaign.
• Audit overlap weekly to prevent keyword cannibalization.
• Monitor CPC trends to confirm internal competition has been eliminated.

Step 6: The Importance of Campaign Nomenclature

A standardized naming convention isn’t cosmetic - it’s operational leverage. As your account grows, clear nomenclature allows faster audits, cleaner reporting, and safer scaling decisions.

When every campaign name instantly communicates ad type, targeting method, and intent, optimization becomes systematic instead of manual guesswork.

Actions to take:

• Use consistent prefixes like “SP | Keyword | Generic” across all campaigns.
• Avoid creative or ambiguous naming that hides campaign intent.
• Align naming with reporting views so performance can be analyzed at scale.

Phase 2: Scale - Implementing a Repeatable Process for Optimization and Expansion 

With your campaign foundation in place, Phase 2 is where performance is shaped and scaled. The focus now shifts to a continuous, data-driven optimization loop that should be executed every 10–15 days. 

This cadence ensures you react quickly to performance signals without over-optimizing or disrupting learning. The objective is to systematically reduce wasted spend, improve efficiency, and expand on what’s already converting.

Step 1: Generate Key Performance Reports

Every optimization cycle begins with pulling the right reports. Two reports are essential for informed decision-making:

• Search Term Report (STR): This reveals exactly which customer search terms are driving spend, clicks, and conversions. It helps separate high-intent demand from irrelevant traffic.‍
• Search Term Impression Share Report (STISR): This shows how much of the available traffic you’re capturing for each converting search term, highlighting missed scale opportunities.

Together, these reports answer two critical questions: Where is money being wasted? And where is growth being capped?

Actions to take:

• Export STR and STISR data for the last 10–15 days to ensure statistical relevance.
• Sort STR by spend and conversions to quickly identify inefficiencies.
• Cross-reference converting terms with impression share to locate scale gaps.

Step 2: Optimize Ad Spend and Pause Low Performers

Once the data is in place, the first priority is eliminating inefficient spend. Using the STR, identify keywords, search terms, or targets with high spend and low or zero conversions. These terms dilute performance and inflate ACOS without contributing to revenue.

This is where Graas becomes a force multiplier. Graas consolidates data from Amazon Ads along with your other sales and marketing platforms into a single, unified dashboard. Instead of evaluating Amazon performance in isolation, you can clearly see how wasted ad spend impacts overall revenue efficiency and marketing ROI, making optimization decisions faster and more confident.

Actions to take:

• Pause keywords and targets that exceed your spend threshold without conversions.
• Add non-performing search terms as negative keywords at the correct match type.
• Track post-optimization performance to confirm reductions in wasted spend.

Step 3: Scale High-Performing Keywords

After pruning inefficiencies, shift focus to expansion. Using the STR and STISR together, identify high-converting keywords with low impression share. These terms have already proven purchase intent but aren’t receiving enough visibility.

Graas helps uncover these opportunities by correlating conversion performance with traffic share across campaigns. This makes it easy to prioritize scale where demand already exists, rather than guessing where to increase budgets.

Actions to take:

• Increase bids on high-converting, low-impression share keywords.
• Reallocate budget from paused terms toward proven winners.
• Monitor impression share and conversion stability after scaling adjustments.

Executed consistently, this optimization routine turns Amazon advertising into a predictable growth engine rather than a reactive spend channel.

Take Your Amazon Ads to the Next Level with Graas

Launching Amazon ads is easy. Scaling them profitably is not. As this guide shows, sustainable growth comes from disciplined campaign structure, granular data analysis, and a repeatable optimization process - not guesswork or one-time tweaks. 

When every keyword, bid, and budget decision is backed by real performance data, Amazon ads shift from being a cost center to a predictable revenue driver. 

Graas makes this possible by bringing your Amazon Ads data together with insights from all your sales and marketing channels into one unified dashboard. This clarity helps you spot inefficiencies faster, scale what’s working with confidence, and ensure every dollar spent is accountable. 

If you’re ready to move beyond trial-and-error and launchAmazon ads that scale, book a demo with Graas today and see the difference data-driven Amazon advertising makes.