Klaviyo Data Science Podcast EP 29 | Detecting the Unexpected

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into…

Anomaly Detection

It’s our third November on the Klaviyo Data Science Podcast, and if you work in ecommerce, you know that November means one thing: Black Friday and (usually) Cyber Monday, i.e. the month of the year where everything changes. Traditionally, we’ve talked about things that help prepare builders of software for when the world is about to change, such as infrastructure, readiness, scale-out testing, and other things along those lines. This year, we’re approaching it from another angle: ecommerce stores go through the exact same struggle every year. How can a platform like Klaviyo help prepare them for the unexpected? One answer: by automatically figuring out when unexpected things are happening, i.e., by detecting anomalous behavior. You’ll hear all about anomaly detection on this episode, including:

• How to pivot your research when your idea is valuable but your results aren’t providing that value
• How to label large swaths of data efficiently
• How to design algorithms for an extraordinarily diverse base of end users

“Imagine a sneaker company who does product drops compared to a regular ecommerce brand. Then imagine customers who are just starting up, with very low traffic…. It was definitely a challenge to generalize to the entire Klaviyo customer base.”

— Harsh Mehta, Senior Machine Learning Engineer

Read the full show notes on Medium!