Drop-off points are most interesting to me because they're like catching someone mid-exit and asking "wait, what went wrong?"

All the other signals tell you what people do, but drop-offs tell you where you're losing them. Someone was interested enough to start but something made them bail and that’s a specific problem you can actually fix.

It's like rescuing potential customers who were already halfway there.

 Timing trends has been the most interesting to me lately as we’ve been working quite deeply on engaging with our customer member base via surveys for different products, and details gathering information from.

Timing is incredibly important for requesting surveys as they require more time and focus to not only have the member fill out the survey, but also to gather the most accurate customer responses, that aren’t just someone clicking through the survey randomly to finish it.

We’re using timing trends with time zones, but also have been uttilizing ‘Send Time Optimization’ which allows the system to analyze past trends and data points for engagement to send based on that individual user’s preferences. We’ve tested no send time vs. send time opt. and have seen some terrific results!

What we use the most right now for DDM emails is  Conversion data. We do a lot of targeting based on email engagement like clicks on specific types on content to then target for bonus messages or in-newsletter dynamic modules. We try to serve our users with more of what they’ve been engaging with.

But where I’d really like to take it a step further is  Behavioral triggers. I’d love to better connect some of our other onsite data so we have even more criteria that we could execute on. We do this a little bit for users who have saved recipes onsite or who have commented on articles, but I think we could do even more with our session data, time spent on page and affiliate revenue data.

Our team does a lot of rapid experimentation within Iterable and we heavily rely on Conversion data, specifically clicks, as the primary signal to measure lift. Our campaigns traditionally have one CTA/link, and is almost always the same. We use the Iterable experimentation tool to launch quick tests and monitor lift in CTR. We’re also digging more into AI-powered insights so we can better segment users in our journeys based on their likelihood to convert.

In a time of heavy automation and making lots of assumptions, I still stand strong for the humble  Customer feedback. We do comprehensive surveys several times a year and it helps us understand if we’re on track with our comms and engagement. It’s such a big help to us to know that people are relating to our tone, to the content we share, and to our products of course. We use Typeform for ease of analysis and for quickly dropping any data back into Iterable if we want it. I would say our top tip is to truly invest a lot of time in what we’re asking in the survey and not just ask the obvious - throw some curve balls and see what you can tell from people’s lifestyles, for example.