Iron Yard Alum Spent Focuses on Relevance and Releases First Product: "Relify"
It’s no secret that consumer demand for personalized products and experiences is increasing at incredible speed, and that demand is creating new standards.
Nobody gets this perfect, because humans don’t make sense. Our tastes are not neatly defined. I legitimately enjoy listening to D’Angelo, Nickelback and Huey Lewis so… good luck with music suggestions for me, Rdio. Lo, I Am Become Enigma, Destroyer of Algorithms. Netflix or Amazon regularly suggest the most ridiculous things to me. But they’re trying. Tweaking. Spending millions of man hours and dollars to improve those algorithms by 1%, because 1% more relevant is a massive improvement. —Joshua Blankenship
Spent, one of our alumni, is excited about this trend, and they’re building products that will help all types of companies provide relevance to their users.
Their team set out to build a recommendation engine for the grocery industry, allowing stores to offer personalized offers based on customer purchase behavior. As they dug into the world of relevance, though they realized how powerful the technology could be for just about any industry.
They took a few steps back, built their product as an API anyone can use, and Relify was born as their first product.
Relify provides recommendations as a service and is geared towards developers, offering quick and easy integration with their apps:
“Relify eliminates the complexity of building a recommendation engine from scratch, and makes it simple for companies to use the data they already have to reduce the paradox of choice. The solution fits companies that track data about their customers but don’t understand how to use it, or don’t have the resources to tackle building their own recommendation engine.
Relify’s technology uses existing recommendation algorithms, combined with proprietary methods, to quickly analyze data and serve recommendations. Using data generated from everyday activities like buying groceries or browsing the Internet, recommendations are personalized for each user. Developers import their data, create data sets and use recommenders to customize the types of recommendations they use in their applications.”
Congrats, Spent, and hello Relify!