Return Path Data and Best Practices: Thinking Outside The Box

One of my clients is known for being an excellent sender, having impeccable inbox placement rates and implementing industry leading strategies across their various email programs. As their Technical Account Manager this makes for easy conversation week to week, but can pose a difficult hurdle to overcome, forcing one to think outside the box about how Return Path can continue to improve their email program and move the needle.

Our client has several email programs operating under the parent umbrella, one of which sends third-party promotional mail. This mail-stream is explicitly called out to the subscribers when they sign up for the entire email program, so receiving third party offers comes at no surprise to these subscribers. Third party senders can purchase the full list or a segment, based on the audience they are looking to target. Historically, campaigns from third-party senders have not performed well at Gmail achieving mid 60-70 percent inbox placement rate. Furthermore, the long-standing behaviors of my client’s subscribers interaction with these third party senders had caused my client’s IP address and domain reputation to resolve to a Medium status at Gmail’s postmaster tools.

The overall inbox placement of these third-party senders was 90-95 percent, which didn’t make Gmail’s poor performance stand out all that much. However, an average of 60-70 percent at Gmail wasn’t a rate I was willing to settle for. When approaching this issue, my initial thoughts were to take some of our best practices recency strategies, so we would be targeting the most engaged subscribers first. I was quickly informed that subscriber-level data, was not a metric we would be able to consider, as they needed to keep the playing field level for all third-party senders.

What we could test and tailor was the mailing order of the third-party senders. I dove into an analysis that was solely focused on Gmail performance, reviewing the historically top and low performing third-party senders. We quickly identified an overall trend with specific third party senders driving low inbox placement week over week. Unfortunately, if those lower performing third party senders were sent out first, particularly on a day where multiple third party senders were sending messages, this mailing order had an overarching negative impact on all of the remaining third-party senders.

After the completion of our analysis, I was introduced to the managers of this email program, who were responsible for the mailing order for each campaign. We presented the data and our recommendations, which revamped their current strategy, by leveraging the data we had at our fingertips, historical performance. The recommendation was to order campaigns based on past performance, rather than first come first serve.

The mailing order changes were implemented for the first time in Q2 and we immediately began seeing results. By the close of Q2, the inbox placement rate at Gmail went from 70 percent to 97-99 percent. They are continuing to see a consistent inbox placement rate in the mid to high 90’s, which they had never experienced before. Beyond that, the Gmail IP address and domain reputation moved from Medium to High/Green over the course of the quarter.

Gmail Performance- January 2016- Present Day


Gmail Postmaster- IP and Domain Reputation, Q4 2015

aarpipreputation   aarpdomainreputation

All in all, we took a simple strategy to driving higher inbox placement, massaged and tailored it using the data we had access to via the Return Path tools.

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