Klaviyo Data Science Podcast Klaviyo Data Science Team

How real marketers use data science

We spend a lot of time on this podcast talking about how to build data science solutions. Implicit in many of those conversations is perhaps the most fundamental truth of product design and development: we build data science solutions because people use them. We aren’t doing this just for fun — the reason we spend so much time, effort, and energy to refine our solutions is that it actually matters to real people.

This month, we talk to some of those people. In particular, we sat down with two members of the team at Made In Cookware (http://madeincookware.com/) to discuss what makes their business unique, how they approach understanding and marketing to their customers, and how data science and AI help them do all of that. You’ll hear about:


What kitchen knives can teach you about product design and development
Which type of pan you should use to cook a steak, and how that can help you understand customer segmentation
How AI saves real marketers real time while also giving them better results

About Made In

Made In Cookware (Made In) is a premium cookware brand based in Austin, TX. Founded in 2017 but born of a 4th-generation, family-owned kitchen supply business, Made In creates best-in-class cookware developed in partnership with the world’s finest chefs and foremost craftsmen. Today, you’ll find Made In products in more than 2,000 restaurants, in the hands of James Beard Award-winning chefs at Michelin-starred restaurants across the country, and in the kitchens of home cooks everywhere. Made In products have garnered over 100,000 5-star reviews, and the company was named one of Inc. Magazine’s best workplaces and Newsweek’s best online shops of 2024.

For the full show notes, including who's who, see the ⁠⁠⁠⁠⁠⁠⁠Medium writeup⁠⁠⁠⁠⁠⁠⁠.

An Introduction to ML Ops 

Building data science products requires many things we’ve discussed on this podcast before: insight, customer empathy, strategic thinking, flexibility, and a whole lot of determination. But it requires one more thing we haven’t talked about nearly as much: a stable, performant, and easy-to-use foundation. Setting up that foundation is the chief goal of the field of machine learning operations, aka ML Ops.

This month on the Klaviyo Data Science Podcast, we give a brief but thorough introduction to the field of ML Ops. You’ll hear about:


How ML Ops is different from the similar fields of data science and DevOps
What skills a successful ML Ops developer should have, and what an ML Ops developer’s day-to-day looks like
Why concepts like “velocity” and “stability” have their own special nuances in the world of ML Ops

For the full show notes, including who's who, see the ⁠⁠⁠⁠⁠⁠Medium writeup⁠⁠⁠⁠⁠⁠.

In many ways, 2023 was the year of AI in tech, which is a double-edged sword. On the one hand, the basic technology is straightforwardly exciting — but on the other hand, with seemingly every technology solution scrambling to integrate a thin wrapper around ChatGPT, it’s hard to stand out in a saturated environment. This month on the Klaviyo Data Science Podcast, we dive into a case study of how to build AI products, SegmentsAI, and discuss the principles that go into making sure your AI-powered product shines — and, more importantly, actually helps your customers. You’ll hear about:


How to know when AI is the right solution for the problem
The unique technical challenges that come with building an AI product, from user testing to validation 
The answer to the AI chicken-and-egg problem

“Why do this, why build another LLM feature? It seems like every website is rushing to get their name next to AI... How you break through the noise is to actually provide value to people, not novelty. Being able to help customers speed up or generate new, interesting segments that they otherwise wouldn’t? I think that’s valuable.”— Rob Huselid, Senior Data Scientist

For the full show notes, including who's who, see the ⁠⁠⁠⁠⁠Medium writeup⁠⁠⁠⁠⁠.

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

Equity, Diversity, and Inclusion

Equity, diversity, and inclusion (EDI) are more than just central principles of successful teams in data science and beyond — they’re also a rich field that presents interesting and challenging data science problems. This episode, we chat with two EDI specialists at Klaviyo about EDI, the data that powers it, and the challenges that come with using that data. You’ll hear about:


Why EDI is a core part of both processes and products 
How to work with self-reported data — and, sometimes, work around the fact that you don’t actually have the data you want
Examples of EDI work in action

For the full show notes, including who's who, see the ⁠⁠⁠⁠Medium writeup⁠⁠⁠⁠.

2023 Year in Review

As the new year starts, we take a look back at 2023. We spoke to 11 data scientist and people who work closely with data scientists, and we asked them all the question we ask every year: what is the coolest data science thing you learned about in 2023? You’ll hear a wide range of answers, including:


How data science moving to peripheral devices and becoming more accessible has huge implications for the future of the field
Peculiarities of working with large language models, both in terms of the tasks they can carry out and how the process of working with them is more complicated than it seems at first
How powerful simple techniques can be at even highly complex tasks

“You don’t have to have a PhD any longer to do data science. And I think that’s amazing and powerful, and it’s going to mean that the future is… where everybody is allowed to do data science stuff without having lots and lots of education.”   — Wayne Coburn, Director, Product Management

For the full show notes, including stories mentioned in the episode and who's who, see the ⁠⁠⁠Medium writeup⁠⁠⁠.

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

Knowing your customers

Customers are all unique, whether you’re building a data science product or selling an ecommerce product. In an ideal world, we’d be able to think about all of them on a truly one-on-one basis. Most of us can’t keep track of that many people in our brains, though, which is where the topic of today’s episode comes in: what is the best way to summarize an entire population of customers into a number of groups that is small enough to intuit but fine-grained enough to actually be useful in practice?

Listen along to learn more about:


Why understanding your customers is the superpower that drives any successful product
How simple-sounding concepts like recency can be trickier than expected
How to build a technical solution that draws on a vast number of data stores

For the full show notes, including resources mentioned in the episode and who's who, see the ⁠⁠Medium writeup⁠⁠.