Removing friction, and how we got our new look

Three years ago, ReSci made a big bet: that we could take artificial intelligence and apply it to email marketing automation. Every other ESP at the time was providing the same toolset that allowed marketers to set up their own campaigns. But there was no value added from the platforms: all the performance was driven by how well the marketers were setting things up themselves. We didn’t know how any ESP could provide more lift than another one—they really all were the same.

But we believed that a technology platform could provide lift, given that the platform could also make decisions. And we were sitting on a mountain of underutilized assets with our predictive models, all of which were already making decisions. So—we went all in.



Adaptive Lifecycle, circa 2015


We took all the models we had, and tried to re-invent the drip. We thought our models, which could predict best time of day to communicate to each user, how frequently they should be reached, and which content is more attractive to them, could actually drive higher open and click rates than you could get anywhere else. By using artificial intelligence predictions to score users for churn, we could save users before it was too late. And with our proprietary product recommendations, we could lift conversion rates. We called this new product Adaptive Lifecycle, because the machines would adapt to your users without you having to set any rules, or really do much of anything at all.


ReSci’s website, circa 2015


And we started marketing our vision. We wanted to save marketers time by reducing manual guesswork, and shifting attention to understanding customers better and coming up with creative ways to message them. This was definitely “ahead of its time”, but we found several forward-thinking adopters who believed in our vision.

But throughout this process, there was friction in the product. Things didn’t work exactly how we wanted them to. Users wanted more control over the decision making. Models needed tweaking as the machines learned new things. So we tinkered some more.


2016 + 2017

While we tinkered, we knew we were sitting on something special when data started rolling in, some of which you can find in our case studies. We redesigned the artificial intelligence platform to fix some of our friction issues, and rebranded “Adaptive Lifecycle” to “Cortex”. And we shortened our name from Retention Science to ReSci.


Cortex, circa 2017


We spent a lot of time optimizing our models. If things worked for one use case but not another, we dug into why. If turning a dial lifted a conversion rate (or dropped it), we dug into why. This digging resulted in a bunch of new innovations along the way, and models that kept getting more and more accurate. And continuously we tinkered with our UX to remove friction for our users. YoY from 2016, we saw a 4153.18% increase in sessions (yes, that is a real number), a 476.56% increase in pageviews, a 55.31% increase in pages per session, and 18.64% increase in average session duration. We also saw a massive shift in behavior, where our users were spending less time on batch and blast campaign forms (a 347.2% reduction), and traffic to our Cortex pages and analytics all shot into the top 5.


“Revenue is a lagging indicator, usage is a leading indicator.” – Satya Nadella


And our website evolved with Cortex:


ReSci’s website, circa 2017


But even with these encouraging stats and our exciting momentum, we never stopped tinkering, and never stopped trying to identify underlying pain points for our users.



As we saw how much our business had grown, and the amazing brands that were seeing successes with us, we realized how much we had matured as a company. We learned so much from the feedback (both good and bad!) from our users over the years, that we ended up building a better product than what we had first imagined. And as our team and business grew, we attracted more and more talent, which pushed us even further. Our recently launched website reflects how much we have grown, and is designed in a way to help guide visitors to what they’re looking for a little easier, along with a cleaner look and feel.





And a lot of the same design principles were applied to our upcoming Cortex redesign, which will be in closed beta during summer of 2018, with GA planned sometime in September of 2018. We are particularly excited about this facelift, as it solves several “friction” pain points, and is paired with several scaling projects to speed up Cortex in a few different areas. This scaling work has recently reduced caching time for some complex filtering in our platform by 43%, and increased send throughput by 300%, with many more improvements to come.

On to the sneak peek of our new Cortex look:




We really took a step back and questioned all the workflows we had put in place, and found several ways we could make things more streamlined and usable. We also wanted to make sure we provided more guidance in app, which will be a big usability focus for us throughout the year. And continuing to build out all the awesome new features our clients have been requesting.




We are incredibly excited about what’s ahead in 2018, and eternally grateful for all our clients who stuck with us and believed that artificial intelligence could be applied to marketing automation to make a difference. And we hope we keep proving ourselves to many more in the future. And cheers to no more friction.


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About the author

Derek Kwan is COO and VP of Product for Retention Science. With 15+ years experience in marketing and ad tech, Derek previously led product innovation at Yahoo!. He also trains his game theory skills in his spare time as a poker player. Follow Derek on Linkedin.

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