Those two facts turned out to be the same fact, and Nudgely came out of working out why.
A few years ago I was running my own ecommerce store, and it was not working. Traffic showed up and left. The conversion rate was the kind of number you stop looking at.
So I did what anyone does at that point and started messing with things. One of them was the popup. Instead of the box asking for an email, I jerry-rigged together a mystery box, so a visitor picked one of three and found out what was inside. It was held together with tape.
The store did not suddenly take off. I want to be straight about that, because it is the interesting part. What happened instead was that the list exploded. People who would never have typed their email into a discount box were happy to play for it first. I ended up running a mediocre store with an email list far bigger than it had any right to be.
That was the first half of the lesson. People will hand over their details readily, as long as you make the asking worth their time.
The second half came from my day job. I spent years as a business development rep, which in practice means sending an enormous number of cold emails and living with what comes back. Thousands of them, tracked, measured, argued about.
The pattern in that data was never subtle. The generic email got ignored, and the email that knew one real thing about the person got a reply. It was never the cleverer email or the longer one that worked, it was the specific one, and the more I actually knew about somebody before I wrote, the better it went.
The hard part is the research. Personalizing one message is easy, but personalizing a thousand is a job nobody has time for, so almost everybody sends the generic one and accepts the worse result.
That is the whole idea behind Nudgely. Capture the way that actually works, by giving someone a reason to want to be on your list. Then follow up the way that actually works, one person at a time, with real detail about them, at a moment that makes sense.
The second half used to be impossible at a store's scale. It is not any more. So the AI does the research on every single customer, which is the part that was never going to happen by hand, and writes the message in your voice.
It means that when you email support, you get me, rather than a queue, a canned reply, or somebody reading from a script who has to pass your question to a team that will answer on Tuesday. Several reviews on the App Store mention how fast the replies come back, and that is the entire reason why.
It also means the roadmap is short and honest. I build what merchants ask for, in the order they ask for it, and I would rather tell you something is not ready than ship a version of it that makes a mess in your store.
The tradeoff is real and worth saying out loud. If you need a named account manager and a procurement process, the big SMS platforms will serve you better. What you get here instead is somebody who knows the entire codebase and answers his own email.
Carefully, because texting somebody is not a small thing to get wrong. Deterministic code decides whether a message is allowed to go out, and the AI is only asked to write the words once every check has passed. Consent is stored with the exact wording each person agreed to. Every send and every blocked send is logged with its reason. That machinery is written up in plain English on the AI SMS page, because you are the one whose name is on the message.
If something here does not add up, ask me directly rather than guessing.