Making Facebook Ad Publishing More Efficient

At Zulily, our Marketing Specialists previously published ads to Facebook individually. However, they quickly realized that creating ads manually was limiting to the scale they could reach in their work: acquiring new customers and retaining existing shoppers. So in partnership with the marketing team, we worked together to build a solution that would help the team use resources efficiently.

At first, we focused on automating individual tasks. For instance, we wrote a tool that Marketing used to stitch images into a video ad. That was cool and saved some time but still didn’t necessarily allow us to operate at scale.

Now, we are finally at the point where the entire process runs end-to-end efficiently, and we are able to publish hundreds of ads per day, up from a handful.

Here’s how we engineered it.

Sales Events is an internal system at zulily that stores the data about all sales events we run; typically, we launch 100+ sales each day that could include 9,000 products that last three days. Each event includes links to appropriate products and product images. The system exposes the data through a REST API.

This component holds the business logic that allows us to pick events that we want to advertise, using a rules-based system uniquely built for our high-velocity business. We implemented the component as an Airflow DAG that hits the Sales Events system multiple times a day for new events to evaluate. When a decision to advertise is made, the component triggers the next step.

In this crucial next step, our zulily-built tool creates a video advertisement, which is uploaded to AWS S3 as an MP4 file. These creatives also include metadata used to match Creatives with Placements downstream.

A sales event at Zulily could easily have dozens if not hundreds of products. We have a Machine Learning model that uses a proprietary algorithm to rank products for a given event. The Product Sort is available through a REST API, and we use it to optimize creative assets.

A creative is a visual item that needs to be published so that a potential shopper on Facebook can see it. That end result advertisement that is seen by the potential shopper is described by a Placement. A Placement defines where on Facebook the ad will go and who the audience should be for the ad. We match creatives with placements using Match Filters defined by Marketing Specialists.

Match Filters allow Marketing Specialists to define rules that will pick a Placement for a new Creative.

These rules are based on the metadata of Creatives: “If a Creative has a tag X with the value Y, match it to the Placement Z.”

Once we match a Creative with one or more Placements, we persist the result in MongoDB. We use the schemaless database technology rather than a SQL database because we want to be able to extend the schema of Creatives and Placements without having to update table definitions. MongoDB (version 3.6 and above) also gives us a change stream, which is essentially a log of changes happening to a collection. We rely on this feature to automatically kick off the next step.

Once the ad definition is ready, and the new object is pushed to the MongoDB collection, we publish the ad to Facebook through a REST API. Along the way, the process automatically picks up videos to S3 and uploads them to Facebook. Upon a successful publish, the process marks the Ad as synced in the MongoDB collection.

While this post is fairly high level, we want to share a few important technical details about the architecture that can be instructive for engineers interested in building something similar.

In short, employing automated processes can help Marketing Tech teams scale and optimize work.

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