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Managing Amazon DSP well is less about constant bid changes and more about having a disciplined structure, audience strategy, creative testing, and measurement.

Here's a framework that works well for most brands:

Area| Best practice
---|---
Campaign structure| Separate campaigns by objective (Awareness, Consideration, Conversion) rather than combining everything.
Audiences| Keep retargeting, competitor shoppers, in-market audiences, and prospecting in separate line items so budgets and performance are easy to control. skalestrategy.comadvertising.amazon.com
Budget| Allocate more budget toward audiences that consistently generate incremental sales instead of evenly splitting spend.
Creative| Refresh creatives every 4–8 weeks to reduce ad fatigue. Test multiple sizes and formats (display, video, Streaming TV where appropriate).
Optimization| Review performance at least 2–3 times per week and make incremental changes instead of large daily adjustments.

Some of the highest-impact optimization tactics include:

• Start with lower-funnel audiences. Product viewers, cart abandoners, and past purchasers usually produce the strongest ROAS. Expand into competitor and lifestyle audiences only after these campaigns are performing consistently. skalestrategy.com

• Control frequency. Showing the same ad too many times wastes impressions. Use frequency caps and monitor reach versus frequency to balance awareness with efficiency. advertising.amazon.com

• Watch pacing daily. If campaigns spend too quickly or too slowly, adjust bids, audience size, or frequency caps instead of simply increasing budget. advertising.amazon.com

• Measure incrementality. Don't rely solely on ROAS. Track new-to-brand customers, detail page views, branded search lift, and total sales impact.

• Test one variable at a time. Change either audience, creative, bid strategy, or placement—not all at once—so you know what caused the performance change.

For larger advertisers, two capabilities have become especially valuable:

• Performance+ campaigns , which use AI to automate much of the optimization while still allowing control over placements and campaign goals. Amazon recommends allowing new campaigns several weeks to complete their learning period before making major judgments. advertising.amazon.comadvertising.amazon.com

• Amazon Marketing Cloud (AMC) for understanding customer journeys, audience overlap, and incrementality beyond standard DSP reporting. skalestrategy.comadlabs.app

A common review cadence looks like this:

• Daily: Budget pacing, delivery issues, and any unexpected spikes.

• Weekly: Audience performance, CPM, CTR, view-through conversions, ROAS, and creative fatigue.

• Monthly: Budget reallocation, audience expansion, creative refreshes, and incrementality analysis.

The biggest mistakes I see are:

1. Combining awareness and retargeting into one campaign.

2. Optimizing only for CPM instead of business outcomes.

3. Allowing frequency to get too high.

4. Making large bid or budget changes every day.

5. Running the same creatives for months without testing replacements.

If you're managing a sizeable DSP program (for example, \$20k+ per month), building dashboards that combine DSP reporting with sales data and AMC insights typically produces better decisions than relying on the default reports alone.

If you can share your monthly DSP budget, whether you're using self-service or managed service, and whether you're focused on Amazon sales or your own website, I can suggest a more tailored campaign structure.