A smart data strategy

Fashion is a fiercely competitive industry in which brands face the challenge of staying on top of an ever-changing supply of available products and a constant stream of return orders. Brands must use data smartly and efficiently to get maximum value from campaigns.

Our collaboration with Omoda began by building a marketing hub that offered a 360° customer view in the Google Cloud Platform. By incorporating smart technology features like machine learning and AI, we unlocked additional value within Omoda’s marketing efforts. 

Next, we went further by helping Omoda complete full data migration to the Google Cloud Platform, supported by a data-driven strategy and culture. While this remains an ongoing journey in which we will continue to experiment and innovate with what data activations have to offer, our findings have already yielded impressive results.

Segmenting customer data

With DEPT®’s help, Omoda began centralizing its marketing data by creating a Customer 360 view in BigQuery. 

Customer 360 integrates customer data from different sources in a centralized location in one overview. For this purpose, we collected various types of data, such as offline purchase data and clickstream data, from Google Analytics 360. Using cluster algorithms, we segmented the data before applying Google Cloud and Google Tag Manager to convert the data-driven clusters into target groups so that marketing campaigns could be adjusted accordingly. 

We used the same centralized data to predict conversion intent or the likelihood that potential customers would make a purchase. This allowed us better to target our retargeting campaigns to the right target group and improve return on ad spend (ROAS).

Growth in net margin through product segmentation

By using multiple data sources, we were also able to leverage Google Shopping campaign more intelligently and focus on products that achieve a higher net margin. 

We accomplished this by adding attributes to campaigns to hone in on products with a higher chance of sales, increase Omoda’s net margin, and are less likely to be returned. Each product is then assigned a unique score based on sales, returns, stock, and margin data. Products with a high score—and therefore a high net value—receive more focus and are displayed more often in Shopping campaigns to maximize results. 

The model transforms all product data and segments products in Google Cloud BigQuery. From there, the output is fully automated, refreshed and integrated with the product feeds, on which various digital marketing campaigns run.

Return based bidding

Like many other retailers, Omoda sees many returns, which hurts their business’s net margin. We wanted to focus on customers with a low return value to improve Omoda’s net margin even further because, as Omoda has observed, a customer who makes many purchases is not always the most valuable to the bottom line. 

To accomplish this, we developed an AI model that predicts the expected return value when a transaction takes place in real-time. These values are forwarded ​​to Google Ads, which, in turn, optimizes the bidding algorithm based on the business goals that are important to Omoda. 

It’s also worth noting that minimizing returns doesn’t only have a positive effect on business, it makes a positive contribution to the environment as well.

Results

By collecting and centralizing data from different platforms, our collaboration with Omoda has provided additional insights that can be harnessed to make valuable predictions and optimize digital campaigns (SEA, social, email, etc.). 

As a result, we can create more worth with less media budget. The solutions are scalable and flexible thanks to the infrastructure of Google Cloud Platform and fully tailored to Omoda’s goals. Looking ahead, we will continue to innovate as partners—-not only to stay at the forefront of digital marketing, but to leverage data for the entire organization.