How SellerApp Turned Demand Into Results
Partnering with SellerApp transformed Northside's Amazon presence, delivering improved ad efficiency, higher revenue, and a stronger brand position on the platform.
Full-Funnel PPC and AI-Driven Bidding
Campaign structures were rebuilt from the ground up to reflect purchase intent instead of broad reach. Low-quality search terms were systematically identified and removed, while new structures were built around queries with a proven track record of conversion.
At the same time, Keywords ranking on Page 2 were identified and pushed toward Page 1 visibility, using paid traffic to accelerate organic ranking. The objective was not just to drive sales, but to improve long-term discoverability and demand capture.
Amazon Marketing Cloud: Path-to-Purchase Clarity
Amazon Marketing Cloud was deployed to map the full path-to-purchase and uncover high-intent audience segments that standard reporting could not capture.
This provided visibility into how users interacted with the brand across touchpoints, allowing budget to be aligned with the stages of the funnel that were actually contributing to conversion, rather than those simply driving clicks.
Amazon DSP : Structured Full-Funnel Targeting
DSP was used to build a more controlled and layered approach to audience targeting.
At the top of the funnel, campaigns focused on category-level shoppers who had shown interest in similar products but had not yet converted, expanding reach in a more qualified way.
At the bottom of the funnel, retargeting was used to re-engage users who had previously interacted with the brand but did not convert, effectively recovering lost demand.
Audience targeting also shifted from broad segments to more refined in-market and lifestyle-based groups, ensuring that ads were reaching users closer to a purchase decision.
Inventory-Aware Automation
To prevent performance drops driven by operational factors, SellerApp integrated inventory and Buy Box signals directly into campaign optimization.
When stock levels declined or Buy Box control weakened, spend was automatically redirected toward better-performing SKUs. This ensured that conversion rates remained stable and that budget was not wasted on products that were unlikely to convert at that point.
Budget Reallocation Based on Performance
Budget allocation was continuously aligned with performance data. Campaigns and segments delivering stronger CTR and conversion rates were scaled, while those underperforming were reduced or paused.
This allowed the account to improve efficiency without cutting overall spend. Growth was driven by reallocating budget toward what was working, rather than increasing investment across the board.
Execution Framework
Once the strategy was defined, execution focused on maintaining control, consistency, and speed of optimization across the account.
Search term reports were reviewed on a weekly cadence. Irrelevant and underperforming queries were negated on an ongoing basis to prevent wasted spend from compounding, while converting terms were isolated into dedicated campaigns for tighter control.
Keyword bids were adjusted continuously based on organic positioning and conversion data, with Page 2 keywords prioritized for aggressive scaling toward Page 1.
Budget allocation was reviewed dynamically rather than fixed, shifting spend toward campaigns delivering stronger CTR and conversion rates and pulling back on segments that weren't performing.
Inventory levels and Buy Box status were monitored alongside campaign performance. Spend was redirected away from SKUs with low availability or weak Buy Box ownership to keep conversion rates stable and prevent budget waste at the product level.
