The Media House was facing these 2 conundrums and was considering their options. Should they hire a team of Data Scientists and wait 1-2 years with no promise of reaching the end goal?
That sounded like a risky and expensive approach – not without reason, other known players in this field have gone with this approach, and ended up having spent large amounts of resources for a disappointing result.
Without giving the full number (because it depends on the compute power needed) that number covers more than 20 years of software subscription (not even considering the running maintenance costs).
The Media House needed a solution if they were to keep and grow market share long-term. Since you are reading this use case, you know that they didn’t go for the in-house-built solution.
If you are in the media space you know that this is by no means an easy task to solve. There are so many variables with so much different content coming out every single day.
Luckily, we built Allyy.io for recommendations on deep content, exactly like news articles, and although it took some extra time as this was the first time working with a Media House, once we got it done, it could easily be adopted by any other Media House.
Once we got all the integrations and models set up, our client decided to test the approach, by feeding our recommendations into a “recommended for you” carousel for a test group. We would then go ahead and compare the performance to a regular group with the old setup (Most read, Newest content).