Ep 185: AKNF - Efficient Amazon Ads Spend Through Deep Segmentation with Clifford and Rob from Pilothouse's Amazon squad
DTC Podcast · with Clifford and Rob · March 18, 2022 · 23 min
Summary
This episode reveals how deep segmentation of Amazon ad campaigns, particularly for Sponsored Products, is crucial for efficiency and maximizing ROI. Pilothouse experts, Rob and Clifford, detail their evolved strategy of breaking down keywords into granular groups (e.g., "Red Nikes" vs. "Nike basketball shoes") to achieve precise targeting, optimized bidding, and improved ROAS. This approach allows DTC brands to gain a competitive edge by tailoring ad spend to specific customer search intents.
Key takeaways
Implement deep keyword segmentation for Amazon Sponsored Product campaigns, moving beyond broad categories to highly specific groups like brand + color or brand + material.
Structure Amazon ad campaigns with granular ad groups for variations like generic brand terms, broad branded product terms, specific branded product terms, and niche branded product terms.
Tailor ad copy and creatives to align with the precise search intent of each micro-segment to improve ad relevance and conversion.
Utilize deep segmentation to enable precise bid management, allocating budget effectively to the most profitable keyword segments and improving overall ROAS.
Regularly analyze the performance of these micro-segments to quickly identify high-performing areas and optimize underperforming ones, driving continuous efficiency gains.
Subscribe to DTC Newsletter - https://dtcnews.link/signup Rob and Clifford from Pilothouse recently attended a large Amazon vendor show, and the word everyone kept bringing up was efficiency. Everyone's looking for their Amazon Ad Spend to be more efficient.
This podcast explains how to achieve Amazon Ads efficiency this through deep product listing segmentation:
Work with Pilothouse Amazon ➝ https://pilothouse.co
As Clifford details:
Recently we've been REALLY segmenting keywords hard - we used to mention this as something to do for Sponsored Brand, but we're leaning into it for Sponsored Products as well. I'll lean into the classic example. Let's say we're Nike - selling a new, red and white basketball shoe.Our approach a year ago for sponsored products would have been something along the lines of: Build a generic keyword campaign ('basketball shoes') Build a competitor keyword campaign ('adidas basketball shoes') Build a competitor ASIN campaign Build a branded keyword campaign Build a branded asin campaign If there was some obvious segmentation we may do some ad groups within those campaigns. But we've now evolved that strategy to segment... everything. Instead of a branded keyword campaign - we'd have: Branded generic keywords ("Nike") Branded generic shoe keywords ("Nike shoes") Branded Nike basketball shoe keywords ("Nike basketball shoes") Branded Nike high-top shoe keywords ("Nike high-tops") Branded Nike Color keywords ("Red Nikes") Branded Nike Material keywords ("Polymer Rubber Nikes") So then the next question is... why? Why put in all this effort and time to segment them
What does this episode say about amazon & marketplaces?
Implement deep keyword segmentation for Amazon Sponsored Product campaigns, moving beyond broad categories to highly specific groups like brand + color or brand + material.
What does this episode say about paid acquisition?
Structure Amazon ad campaigns with granular ad groups for variations like generic brand terms, broad branded product terms, specific branded product terms, and niche branded product terms.
What does this episode say about analytics & attribution?
Tailor ad copy and creatives to align with the precise search intent of each micro-segment to improve ad relevance and conversion.
What does this episode say about amazon & marketplaces?
Utilize deep segmentation to enable precise bid management, allocating budget effectively to the most profitable keyword segments and improving overall ROAS.
What does this episode say about amazon & marketplaces?
Regularly analyze the performance of these micro-segments to quickly identify high-performing areas and optimize underperforming ones, driving continuous efficiency gains.
