Email Tech Is Now Ad Tech
Topic: Machine Learning
eBay has come a long way in our CRM and email marketing in the past two years. Personalization is a relatively easy task when you’re dealing with just one region and one vertical and a hundred thousand customers. With 167M active buyers across the globe, eBay’s journey to help each of our buyers find their version of perfect was quite complex.
Like many in our industry, we’ve had to deal with legacy systems, scalability, and engineering resource constraints. And yet, we’ve made email marketing a point of pride — instead of the “check mark” that we started from. Here’s our story.
Our starting point was a batch-and-blast approach. Our outbound communications very much reflected our organizational structure: as a customer, I’d get a fashion email on Monday, a tech email on Tuesday, and a motors email on Wednesday. This of course wasn’t the kind of an experience we wanted to create.
Additionally, for each of our marketing campaigns, we hand-authored targeting criteria — just as many of our industry colleagues do today in tools like Marketo and ExactTarget. This approach worked OK, but the resulting segment size was too large — in hundreds of thousands. This meant that we were missing out on the opportunity to treat customers individually. It also didn’t scale well — as our business grew internationally, we needed to add more and more business analysts; and the complexity of our contact strategy was becoming unmanageable.
We wanted to create a structurally better experience for our customers — and our big bet was to go after 1:1 personalization using real-time data. We wanted to use machine learning to do the targeting, with real-time feedback loops powering our models.
Since email is such a powerful driver for eCommerce, we committed to a differentiated experience in this channel. After evaluating multiple off-the-shelf solutions, we settled on building an in-house targeting and personalization system — as the size of the eBay marketplace is astounding, and many opportunities and issues are quite unique. We set a high bar: every time we show an offer to a customer, it has to be driven by our up-to-the minute understanding of what the customer did and how other customers are responding to this offer.
Here are some examples of the scenarios we targeted:
- eBay has many amazing deals, and our community is very active. Deals quickly run out of inventory. We can’t send an offer to a customer and direct them to an expired deal. Thus, our approach involved open-time rendering of offers in email.
- Some of our retail events turn out to be much more popular than we anticipate. We want to respond to this real-time engagement feedback by adjusting our recommendations quickly. We thus built a feedback loop that shows an offer to a subset of customers; then, if an event is getting a much higher click-through rate than we expected, we show it to more customers. If it for some reason isn’t doing well — for example, if the creative is underperforming — the real-time “bandit” approach reduces its visibility.
Both of these scenarios required us to have real-time CRM and engagement streams. That is, we needed to know when a customer opens an email or clicks on it, and based on this knowledge, instantaneously adjust our recommendations for other customers. This of course is miles away from a typical multi-step batch system based on ETL pipelines that most retailers have today. We were no different — we had to reengineer our delivery and data collection pipes to be real-time. The payoff, however, is quite powerful — this real-time capability is foundational to our ability to personalize better: both today and in the years to come.
The resulting solution transformed email marketing at eBay: instead of hundreds of small, uncoordinated campaigns each month, we now have a small set of “flagship” campaigns, each of which is comprised of one or more offers. Each of the offers is selected at the open time of the email, and the selection is made based upon machine-learned model which uses real-time data. As a result, we saw significant growth in both engagement and sales driven by our emails.
You’ll notice that this component-level personalization approach is all about treating email content as an ad canvas. The problem is fundamentally similar: once you’ve captured the customers attention — be it via a winning bid on an ad auction, or by having that customer open your email — you need to find the most relevant offer to show. Each email slot can be thought of as a first-party ad slot. This realization allowed us to unify our approaches between display advertising and email: the same stack now powers both.
We extended this approach to scenarios like paid social campaigns, where Facebook would want to retrieve the offer from us a priori to manage their customer experience. We built a real-time push framework, where, whenever we find a deal that is better than what we previously apprised Facebook of, we immediately push that offer to Facebook.
This creates a powerful cross-channel multiplier: if we happen to see the customer on the display channel, the same ad-serving pipeline is engaged — and our flagship deal-finding campaign can be served to that customer, too. This means that evolving our flagship campaigns — adding more sophisticated machine learning, improving our creatives — contributes to all channels that are powered by this pipeline, not just email.
Orchestration across channels too becomes possible: we can choose to send an email to a customer with a relevant offer; if they don’t open it, we can then target them with a display ad, and an onsite banner; then, after showing the offer a set number of times across all channels, we can choose to stop the ad — implementing an effective cross-channel impression cap. And each condition for state transitions in this flow can itself be powered by a machine-learning model.
eBay’s scale creates an admirable engineering challenge for a true CRM. By putting our customers, and their behavioral signals, at the top of our priority list, we were able to create an asset in our CRM platform that positions us well towards In this journey towards 1:1 personalization. A single real-time, event-driven pipeline we’ve built allows for coordinated, up-to-the-minute offers to be served — wherever we happen to see the customer.
Alex Weinstein (@alexweinstein) is the Director of Marketing Technologies and CRM at eBay and the author of the Technology + Entrepreneurship blog, where he explores data-driven decision making in the face of uncertainty. Prior to eBay, Alex was the head of product development at Wetpaint, a personalization tech startup.
Graphic: Rahul Rodriguez