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Facebook Ads 170% Improvement

The Brief

The client would break even only on repeat purchases. But as the client’s D2C brand was relatively new, the returning customer rate was seriously affecting the growth rate. We will go over what we did and what results we got for the client after we took over from the previous agency.

The Solution

The client had excellent relationships with their current agency which were doing everything marketing related for them. As our speciality is demand generation we offered to collaborate with the brand and the agency they worked with to get them the best results. 

  • An audit discovered an opportunity to reach new untested audiences via Facebook ads.
  • A more simplified testing approach delivered insights faster which allowing for greater campaign optimisation.
  • A more sophisticated tracking solution was put in place to improve the accuracy of the tracked conversions.
  • The machine learning algorithms was given more data and freedom to make decisions.

The Outcome

Our client was able to break even on the first order. They reported that the small profit margin has been increasing ever since we introduced them to machine learning.

  • 170% improvement compared to the all-in-one marketing agency
  • ROAS increased from 1.8x to 3.06x
  • The cost per order decreased from 22.69 GBP to 11.92 GBP

Tracking & attributing the marketing efforts

After iOS 14.6 update it has been more difficult to attribute conversions to digital campaigns.

To fix this issue we did a couple of things:

  1. Updated the tracking via browser pixel.
  2. Implemented tracking via server pixel (CAPI).
  3. Automated raw sales data (Offline Conversions) upload & matching.

For attribution, we used a new  system of looking at data from:

  1. Facebook Ads Manager
  2. Google Analytics UTM FB Sales Tracker
  3. Shopify multi-channel tracking

Facebook’s ALGO has changed. It no longer reacts to real-time performance. Its algorithm for learning is limited. It has lots of missing data points for it to use and for us to make decisions with. However, with our approach, we were able to identify which ad sets were performing best and scale them successfully without having to shoot in the dark.

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