Key Results
BACKGROUND
Our client has a vast, rapidly evolving catalogue. Google Ads automated bidding technology struggled to keep up, taking too long to gather enough data to decide how to push every new product, creating Stock Keeping UnitSKUs that were effectively dormant.
With shoppers never being shown these products, revenue during their key initial release period was much lower than it could be, and often missed out on entirely.
CHALLENGE
Our objective was to surface these ‘Zombie SKUs’ and deliver a 10% increase to the total revenue through Google Ads shopping activity. Whilst also achieving a return on ad spend (ROAS) that was no lower than existing shopping activity – creating genuine incremental growth from the existing portfolio.
There was no unique target audience as this campaign focused on delivering more inventory to all potential customers. Typically though, our target audience was 20-40 years old.
We started by analysing our client’s historical sales data to study how profitable the different products and variations (size, colour etc.) had been for the e-commerce retailer. We aimed to predict the gross merchandise value (GMV), the total value of merchandise sold over a given period
OUR APPROACH
“Our team leveraged cloud-based machine learning tech to engineer a deep neural network powerful enough to predict how much GMV a product could generate in a given 30-day period."
ACTIVATION STRATEGY
Models are meaningless unless they can be operationalised by marketing teams so we devised a phased activation strategy to guide bidding strategies. We made our model available to our client through a secure, easy-to-use web app.
PLATFORM DEPLOYMENT
We know experimental machine learning projects without human oversight are risky. So, we designed a phased rollout, starting with a small proportion of the budget dedicated to model testing and a tiered approach to campaign development.
RISK MITIGATION
Three separate shopping campaigns, with unique bid strategies according to the expected potential of the products contained: aggressive bidding and low Return on Ad Spend (ROAS) restrictions for high-potential products, optimising for click volume. granular campaign structures and ad optimisation for mid-tier potential products and tight ROAS constraints on products with low predicted potential.
The effectiveness of this activity has seen it become a fundamental element of our Google Ads activity for our client. It is now synonymous with new product launches/introductions to market, and our investment in these campaigns has continued to grow.
“Secret Sales went from missing ad revenue on dormant products to prioritising products with the potential to generate notable incremental revenue.”
RESULTS
The numbers speak for themselves:
Through a truly innovative approach, including the development of machine learning beyond what even Google can provide at the moment, our work exceeded expectations.
than any other shopping campaigns.
across the entire Google Ads account.
> £1M incremental increase in profits driven by model prediction.
"Found's Paid Search team's acumen, engagement and relentless drive to deliver a positive ROI has given us the confidence to up-weight our media budgets several-fold in a few months. I am particularly impressed by the innovative ideas they put forward to scale these channels and look forward to furthering success together in the year ahead."
UP NEXT
CLIENT
Mission
Secret Sales needed to avoid missing ad revenue on dormant products and prioritise products with the potential to generate notable incremental revenue.
Read StoryResults
HIGHER ROAS
results
UPLIFT IN ROI