We will bring our marketing and platform expertise from millions of £ of ad spend, to audit and optimise your ad accounts to boost performance
When this brand approached us, they had already ran some Google Shopping campaigns previously but faced several key challenges. They were present on Meta :
Retailer Competition: Competing with retailers for certain products
Unknown metrics: No established ROAS targets or spend patterns
Mix of brand and generic traffic: Campaigns had been set up before, but up, but
Our first step involved establishing a comprehensive financial and measurement foundation:
Pre-launch Analysis: Keyword research, Competitor research, Cost structure
Breakeven calculations: Determined target Cost Per Acquisition (CPA) and Return on Ad Spend (ROAS) thresholds based on financials to maintain oversight of blended acquisition costs and listing sales across brand and performance campaigns
Custom dashboard development: Built specialised tracking for marketing costs, variable expenses, and fixed overheads aligned with their specific financial KPIs
Tracking setup: Implemented comprehensive checkout funnel analysis to optimise abandonment points in funnel
Piggyback on strong brand awareness: utilise existing video assets from Meta, generated from working with influencer. As well as statics using influencers
Monthly promotions: push monthly promotions to cold audience campaigns on Google to drive low nCPA
Clear Campaign Structure
Once targets were established, we ran a simple 4 tiered bottom of funnel campaign setup with:
Generic Search
Performance Max (Generic)
Generic Shopping
Brand Shopping
Performance Max was set up with asset groups by product category in order to expand out queries, for dynamic search, and some remarketing on Display/Youtube.
All campaigns were set up with Klaviyo customer and GA4 audience/purchaser exclusions to target cold traffic.
Feed Optimisation Strategy
The high number of skus meant plenty of opportunity for feed optimisation:
Custom labels: Set up custom labels by high/low/zombie products so we could eventually move towards a streamlined approach depending on product performance. Though we started with single pmax/shopping campaign in order to gather data
Enhanced product data: Deep research to identify crucial search-driven attributes to bolster titles and descriptions
Datafeedwatch: Moved to a feed provider that would allow for proper feed customisation and rules
Single PMAX campaign for expansion
Asset groups by category: We set up asset groups per product category within a single PMAX campaign
PMAX for mining: PMAX is set on slightly higher ROAS targets so our shopping campaigns maximise total impressions. PMAX is then used for some display and Youtube expansion as well as mining new search terms with dynamic search. We also didn’t want PMAX to compete with generic search. We’ve then used separate Youtube and Display campaigns for cold traffic expansion
Search Campaign Development
We adopted a layered approach to campaign expansion, only starting with certain campaign types after others due to budget and campaign constraints.
Shopping foundation, PMAX integration: Started with Shopping campaigns to gain search term visibility and capture BOFU queries first. We used PMAX to mine new BOFU queries but not let it out compete Search, as we want control over queries and landing pages
Bottom up Search approach: Started with high-intent buyer terms, such as “hair oil” and then expanded out into broader, problem, solution terms with landing pages such as “how to fix dry hair”
Landing Pages: Scaled out broader terms to target cold traffic with landing pages - problem/solution, listicle, influencer sponsored etc.
Display & Youtube Expansion
The next step is to expand out top of funnel traffic with display and youtube. Alongside continued growth on search and shopping, this will increase total spend on top of funnel campaigns and should improve the overall account performance
