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Don’t Have Your Data Strategy? That’s a Mistake

Data Strategy

The Sins of AI Adopters

Artificial intelligence adoption may be tricky. This technology is different than any other you’ve implemented before. There are rules to follow and some of them incomprehensible to someone without extensive AI knowledge. There are certain challenges companies can face while implementing AI: data quality, model errors, lack of data science experts — many of them covered in the article 12 challenges of AI adoption. Some of these issues can be prevented, but others require preparation. However, many organizations are still dreamers when it comes to AI. There’s nothing wrong with having a vision to follow, but the way you follow it matters.

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Is It Worth It? ROI of Recommender Systems

Netflix recommender system

Recommender systems promise to reduce churn and increase sales. But how do you measure their actual success? What is it that you should measure? And what challenges should you look out for when you’re building your recommendation engine? In this article, I’ll discuss some challenges of recommendation engines, the ROI, and standard metrics to help evaluate their performance.

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Challenges of Recommender Systems

Most articles about recommendation engines focus on all the bright sides of recommendations: personalized customer experience, lower churn, increase in sales, and more revenue. While all of that is true, as we can see looking at the examples of numerous companies including Amazon, adopting a new technology requires a strategic approach — so you should be realistic and well-prepared and not only optimistic about the future outcomes. There are some challenges that you have to be aware of.