Here we go again. This is the second episode of our docuseries about how we are managing retailers’ data for our beloved customers. If you did not read our previous post about this topic, you should spend a few minutes catching up on it. With this part, we would like to introduce the hidden aspect of our product and I promise, it is not a buzzword: Machine Learning. In the following paragraphs, we are going to show new technical improvements and the analytics aspects under the hood.
Reading Time: 5minutesTL;DR: We developed Uppy, an on-premises way to distribute Android and iOS apps via the open web. The SDK for iOS is here and the one for Android is here, whilst the backend will be published later because it needs a bit more polishing! 😜
I’m so excited to unveil this project publicly that I’d like to go straight to features, but first, let me introduce some of the backstories!
Why we built Uppy
As you may already know, internally we are developing two apps: one for customers and one for shoppers and while the first one is publicly available on major stores, the other one has very specific needs that can’t be achieved on those platforms.
Specifically, we value the shopper app as a Working tool with a capitalized “W”, meaning that the app should be not only as efficient as possible but updates must be on-point and align with regulations and new features in a timely fashion.
Hold onto your seats; I am going to guide you around our brief history of the price updating process. Prices must be updated every day, following our Retailers procedures to provide the best experience to our customers. If you think it should be a simple procedure, consisting of just updating the right record and go on with the next one, I totally agree with you!
When we were in the start-up phase back in the days it was that simple, but soon we had to forget about doing it this easily when we had to scale it up from a few thousand updates to millions. In the next paragraphs, I will show you the evolutions of this process during the years.