Found.

With 61% of all shopping searches beginning on Google, it’s crucial customers can easily find your products in their search results.

If not, simply put, you’re missing out on revenue you would be making if your products were being found.

And this was the challenge Found.‘s client Secret Sales faced.

Founded in 2020, Secret Sales are a unique marketplace platform that delivers a hugely popular clearance service for designer brands.

With the aim to become the leading online designer outlet in the UK, Secret Sales has doubled in size yearly for the last two years and aims to achieve this again in 2022.

However, the vast size of their product catalog made it challenging to surface the right products to customers when advertising.

The Challenge

Secret Sales used automated bidding on Google to promote their products to consumers. Simply put, Google spends a small amount of the overall budget on each product, sees how well it does, and then decides whether or not it’s profitable to put more budget behind it.

And here’s the problem – when you have the volume of products Secret Sales has, Google Ads’ automated bidding technology takes too long to collect enough data to understand the profitability of these products, particularly new ones.

And as a result, the automated bidding was withdrawing the budget from most products. Which became dormant or ‘zombified’ – not being bid on and not appearing in any ads.

Consequently, revenue from new products during their critical initial release period was much lower than it could be and often missed out on for prolonged periods of time (or even entirely).

These were profitable products, ready to be found and bought by customers. But they weren’t because they didn’t know they existed.

So, Found. asked themselves one question: Could they identify the zombified products, surface them in ads to relevant customers and drive incremental revenue for their client?

Spoiler, they could. And here’s how.

The Objective

Found.’s objective was to surface these ‘Zombie SKUs’ and deliver a 10% increase to the total revenue through Google Ads shopping.

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, their target audience was 20-40 years old.

Found. started by analyzing Secret Sales’ historical sales data to study how profitable the different products and variations (size, color, etc.) had been for Secret Sales. They aimed to predict the gross merchandise value (GMV), the total value of merchandise sold over a given period.

The main challenge they faced was the volume of data that Found. had to analyze and train a model with. They were working with over 23 million data points from previous product sales.

Solution

To overcome this challenge, Found. leveraged BigQuery machine learning to engineer a deep neural network powerful enough to predict how much GMV a product would generate in a given 30-day period, accurate to within £100.

This significantly increased the speed of their model development and the level of complexity that we were able to build into it to achieve the desired level of accuracy.

In other words, they were now able to predict how much revenue could be made from dormant or zombified products by surfacing them through paid ads.

Found. made their model available to Secret Sales through a secure, easy-to-use web app.

This provided:

• Complete and transparent access to the models’ predictions.
• Empowering Found and Secret Sales to immediately determine the sales potential of new products.
• Wasting no time in data collection and enabling swift action.

Their model was proven to reliably predict the next 30 days of revenue for any individual product (including colour & size variants) to an accuracy of roughly +/- £100. The by-product of this was that they could see underperforming products, tier the scale of their underperformance, and treat them accordingly.

Three separate Shopping campaigns, with unique bid strategies according to the expected potential of the products they contained.

• High Potential| Aggressive bidding and low ROAS restrictions for high-potential products.
• Mid Potential | Granular campaign structures and ad optimisation for mid-tier potential products.
• Low Potential | Tight ROAS constraints on products with low predicted potential.

Results

Through a truly innovative approach, including the development of machine learning beyond what even Google currently provides, Found.’s work exceeded all expectations.

Found. aimed to deliver a 10% incremental increase in total revenue through Google Ads shopping and maintain existing ROAS. And so far, they have achieved;

• 22% increase in Shopping activity revenue
• 28% higher ROAS than all other shopping campaigns
• 5% uplift in the overall ROI of the whole ads account

On top of this, the results that Found. have seen so far are only the beginning. As Secret Sales grows, then the incremental revenue delivered via this approach will also continue to grow – the true benefit of this activity has only just begun.

The effectiveness of this activity has seen it become a fundamental element of their Google Ads activity for Secret Sales. It is now synonymous with new product launches/introductions to the market, and Found.’s investment in these campaigns has continued to grow.

If you’d like to be found by more of your target customers contact them to find out what Found. can do for you.