Improving Marketing Efficiency with Machine Learning

Introduction

Here at Zulily, we offer thousands of new products to our customers at a great value every day. These products are available for about 72 hours; to inform existing and potential customers about our ever-changing offerings, the Marketing team launches new ads daily for these offerings on Facebook.

To get the biggest impact, we only run the best-performing ads. When done manually, choosing the best ads is time-consuming and doesn’t scale. Moreover, the optimization lags behind the continuously changing spend and customer activation data, which means wasted marketing dollars. Our solution to this problem is an automated, real-time ad pause mechanism powered by Machine Learning.

Predicting CpTA

Marketing uses various metrics to measure ad efficiency. One of them is CpTA or Cost per Total Activation (see this blog post for a deeper dive on how we calculate this metric). Lower CpTA means spending less money to get new customers so lower is better.

To pause ads with high CpTA, we trained a Machine Learning model to predict the next-hour CpTA using the historical performance data we have for ads running on Facebook. If the model predicts that the next-hour CpTA of an ad will exceed a certain threshold, that ad will be paused automatically. The marketing team is empowered to change the threshold at any time.

Ad Pause Service

We host the next-hour CpTA model as a service and have other wrapper microservices deployed to gather and pass along the real-time predictor data to the model. These predictors include both relatively static attributes about the ad and dynamic data such as the ad’s performance for the last hour. This microservice architecture allows us to iterate quickly when doing model improvements and allows for tight monitoring of the entire pipeline.

The end-to-end flow works as follows. We receive spend data from Facebook for every ad hourly. We combine that with activation and revenue data from the Zulily web site and mobile apps to calculate the current CpTA. Then we use CpTA threshold values set by Marketing and our next-hour CpTA prediction to evaluate and act on the ad. This automatic flow helps manage the large number of continuously changing ads.

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Results and Conclusion

The automatic ad pause system has increased our efficiency through the Facebook channel and gave Marketing more time to do what they do best: getting people excited about fresh and unique products offered by Zulily. Stay tuned for our next post where we take a deeper dive into the ML models.