Ep. 124 | The Benefits of Predictive Send Time Optimization for Your Email Marketing
Jose is the Vice President and General Manager of Email and Mobile Messaging for Merkle. I interviewed him at SEMpdx’s Engage conference, where he was a speaker. We also talked about the role of AI and machine learning in email marketing, and the compound effect of predictive sending and optimizing your email subject lines.
What is predictive send time optimization for email?
Nathan: We are at the Engage conference, where you were talking about best practices in email marketing. One of the topics you discussed was predictive sending for emails. What is predictive send time optimization for email?
Jose: The presentation was really around email and its role in the customer experience and we talked about different ways to optimize email from a channel perspective and to integrate it with other channels.
In the context of what we’re calling send time optimization, it’s a service offered by many platforms that send email today as well as external partners. What it does is it allows you to optimize your send time at an individual level versus at a campaign level.
So basically they’re using analytics to go through your email lists, look at opens, and then in some cases compare them to opens in other email programs and determine the best time to send to an individual. The output is a list of times, basically broken down by hours to send email campaigns. Most of these tools are integrated so you don’t have to do all the targeting yourself.
How does adaptive sending actually send email to a big list?
Nathan: With a tool like that and you have say a list of 20,000 people, is there going to be 20,000 times that that email is going to optimize times as it’s going to be sent out or does it bucket it into these 9 p.m. time?
Jose: Great question. It buckets it into hours. So most platforms will determine, will be able to determine optimized send times for a portion of the list and the rest ultimately gets sent at a default time.
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What audience segments should we use optimized predictive email on?
Nathan: You made a good point in the presentation about what audience segments we should use adaptive email sending tools on. I was like, “Ah, yes.” The people we should be using an optimized send time with is what kind of audience?
Jose: Yeah, so send time optimization works for really all facets of your audience. But if you think about it, the people that tend to open our already opening, so that’s great to send it to them.
But it’s really the less engaged group or the non-engaged group where you can start to see lift in open rates into that group. So because they’re normally not opening, if you can get lift in that at all, that’s good.
The compound effects of optimized subjects lines and email send times
Nathan: Then earlier we also talked about subject line optimization and the benefits of that. Then you talked about the benefits of when you do that, both of them, they have a compound effect. Can you expand on that a little bit?
Jose: Sure. So what we’re talking about is subject line testing has evolved from a couple of people in a room coming up with subject lines and then putting it into campaigns. Now we can use machine learning and artificial intelligence to determine subject lines and pick a winner based on the results.
That creates pretty significant lift in open rates, which means higher click rates, which means higher site traffic and therefore presumably revenue, right? Send time optimization does the same thing but with send times. So now you have a mix of timing and then also offer in essence.
So the two can be used together to get a compound effect.
What is the role of AI or machine learning in email marketing?
Nathan: In both of those cases, you’re seeing machine learning and or AI or whatever people are calling it have some sort of role in there. How do you see AI really changing email marketing moving forward?
Jose: That’s a good question. I got to say, on a personal level, I get tired of like the buzzwords and I can’t believe I said it.
But where I believe it’s really headed is not necessarily so much in kind of the send times and things like that. I think that’s the first use case. It’s really in pouring through the volumes of data that we have on people’s interactions with a company and determining who to send to and then what to send to.
So if you abstract it out from email as a channel, you start to think about the AI and machine learning and really in decisioning systems and what to send when, what to send or what to offer to somebody in what channel. That’s the part where I really see the bulk of the lifting happening.