25/01/2021
This is an anonymised case study for a beauty brand we helped add £2.2m in attributable revenue using Facebook/Instagram ads. In the interest of keeping this case study concise, and relevant, I’ll only focus on the core factors that contributed to growth.
🟢 Foundation
In January 2019, we begun by analysing their current performance to see what is or isn’t working through an audit. The focus of the audit came down to the following factors:
- Audience
- Creative
- Funnel metrics
- Placements
- Lifetime Value / Cohort Analysis
The variables above interlink various ways but the key questions posed through the foundational analysis are:
⚫️ Is there misallocated spend in the ad account? Which segments are consistently showing a high return / lower cost per acquisition?
⚫️ Are there enough creative variations in the account? Is there a testing structure in place? Is copy being tested? Are best performers being iterated on?
⚫️What is the budget allocation between prospecting, retargeting and re-marketing? Are the right audiences being targeted? Are exclusions set up?
⚫️ Which placements are receiving the highest spend? Are the creatives fit for the relevant placement? Are there custom assets for particular placements?
⚫️ Is there an effort to improve customer lifetime value? Is spend allocated to segments with a higher LTV? What is the customer acquisition cost to 30-60-90 day life time value?
→The key findings of the audit were as follows:
A lot of misplaced ad spend directed towards underperforming segments. The current spend allocation looks like this where proportional spend is allocated to segments with a low CPM - high ROAS ratio (scalability factor).
The creative types tested consisted of only simple images and videos with limited testing of copy.
The budget split from prospecting to retargeting was 1:1 when it should’ve been closer to 4:1. A large majority of the ads were directing to the homepage rather than a collection or PDP.
A large portion of the creatives look misplaced on the IG feed & stories so creating ads fit for the placement is crucial.
LTV analysis: the brand was relatively new and had limited past data so calculating LTV was limited. But, we needed to establish a baseline metric to improve upon.
Testing
There are many different methods to test creatives on FB - each of which has its own merits / constraints. In this case, the client gave us a high testing budget but limited marketing assets (a handful of images and videos). We determined that we needed to test and iterate on existing assets whilst we waited for more content. Using the ‘AIDA’ framework that quantifies creative assets into 4 KPIs:
Awareness = Thumb Stop (3s vid views / Impressions) - different metric for images
Interest = Video verage % watch time
Desire = Unique outbound CTR
Action = ROAS or Cost per purchase
Mapping the creative analysis onto our testing framework was the next step. For example, If we had improved the thumb stop and average watch time but the outbound CTR was low, we’d focus on improving the value proposition and call to action. There are many more nuances to this but this is the gist of it.
We also set up stop loss and scaling using a third party tool to ensure we don’t overspend on testing and scale what is working within our defined benchmarks. The rules were as follows:
[Ad Level] Pause ad IF:
Spend >1.2x avg CPC, PUR < 1
Spend >1.5x avg cATC, PUR < 1
Spend >1.7x avg cIC, PUR < 1
Spend >2.0x avg cPurchase
→[Ad Set Level] Increase budget by 20% daily IF:
Spend > £100 & ROAS > 2.5x (Last 3 Days) [7DC/1DV]
Spend > £300 & ROAS > 2.3x (Last 3 Days) [7DC/1DV]
→[Ad Level] Add to name: (POTENTIAL WINNER)
Impressions > 8000 & ROAS > 1.1x Prospecting Campaign Average & OB CTR > 1.1x Prospecting Ads Average
🟢 Verifying & Scaling
Within 6 weeks, we had tested hundreds of ad variations against a range of audiences and market segments. We established a marketing plan that included sales, promotions and new product launches. All which were tied to monthly and quarterly revenue goals as well as as a broader goal to improve 3-month LTV.
Increasing the 3 month LTV included a combined effort with the brands email team but we’ll save that for another day.
🟢 The Results
In 2019, we spend exactly £488,200.41 at an average return of 463% return on ad spend which results in £2,262,600.26 in directly attributable revenue (28 day click, 1 day view).
