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...

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...

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...

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: ...

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...

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...

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… When Things Break Welcome to the November episode of the Klaviyo Data Science Podcast for this year! November is a unique month for ecommerce, which makes it a unique month for any software solution built for ecommerce; it’s a tradition on this podcast to take the opportunity to celebrate some of those unique challenges. In an ideal world, software and data science products would never break. We do not live in an...

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… Off the Happy Path In most discussions about data science and data science features on this podcast, we make a basic, foundational assumption: the users whose data we are thinking about and customer experience we are trying to improve are, generally speaking, trying to use the platform in a way we recognize and approve of. Not all users of an application have this intention, and the data science behind detecting...

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… Presenting your work for fun and profit Presenting technical work is not something you automatically learn how to do — just like the technical skills themselves, it has to be learned and practiced, and opportunities to practice it can be hard to find. This episode, we discuss one opportunity that Klaviyo put together for its R&D teams this summer: the Klaviyo R&D Science Fair. Listen along to hear...

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… An introduction to production What comes after you finish building a data science model? If you’re working on a software project, the answer likely involves that model serving customers in production. Understanding production is crucial for any data scientist or software engineer, so we spend this episode learning about best practices from three experienced Klaviyo engineers. Listen along to learn more about: ...

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… Research is a core part of data science. But data science is far from alone in that respect — other fields rely on research just as heavily, and they have their own set of hypotheses, methods, complications, and concerns. This month, we talk to three Klaviyos about research they did before joining the team — both data science research and other kinds — to see what we can learn about conducting effective data...

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… Few parts of your product, application, or webpage are more crucial than the very initial experience. In a web application like Klaviyo, that means the home page. Everyone sees it every time they log on to do anything, and interactions with that page set the tone for everything that follows. Meaning: if you’re going to change the home page, you need to really know what you’re doing. This month, we talk with the...

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… The question is slightly tongue-in-cheek, but only slightly. Data science is a new field — while many people today are graduating with degrees in data science, the same was not true a decade ago. Many of the people who work (and will work) as data scientists were not classically trained as a data scientist, but as something else. This month, we examine that process: the process of working in a field that’s distinct...

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… Back by popular demand: data science is a broad, deep field with an extraordinary amount to learn, and we’re here to help you learn it. We asked four members of the Data Science team at Klaviyo what one of their favorite data science books was, and we got four different answers. Listen on if you’ve wanted to know more ways to learn about: How to think about and employ the Bayesian framework (and corgis) ...

Listen to the full episode on Anchor, or in your favorite podcast distribution platform! Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… Starting from scratch We’ve talked about a lot of aspects of data science on this podcast — building software features, conducting research, learning new methods and skills, recruiting new members — but there’s one we’ve always avoided: building a new team from the ground up. A large reason for that is personnel — while your...

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… When the data science world changes When you work in data science, it’s inevitable that the world will change for you. Sometimes it’s due to global events, macroeconomic trends, or sudden shifts in consumer behavior. Other times it’s due to new features added by a commonly-used piece of software. When your lifeblood is data, all of these can be equally shocking and disruptive. This month, we discuss one of the...

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… 2022 Year in Review As the new year starts, we take a look back at 2022. We spoke to 8 data scientist and people who work closely with data scientists, and we asked them all the same question: what is the coolest data science thing you learned about in 2022? You’ll hear about fascinating data science topics, including: Advances in AI, NLP, and data science in general in 2022 How understanding data science and...

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… Tools of the Trade We talk a lot on this podcast about the results of data science and software engineering work. We even talk about the process of doing data science and software engineering work. But one thing we haven’t shed much light on, until this month, is: what specific tools help a Data Science team — or any developer or data scientist similarly engaged in building a scalable and intelligent...

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,...

I’ll let you in on a secret: this podcast does not cover everything. We cover a wide array of projects, go into detail on a variety of aspects of them, and speak to a diverse panel of data scientists and people related to the data science world, but we still can’t cover everything. This month, to give you a taste of what we haven’t been able to showcase on this podcast, we’re asking six Klaviyos who work on or with the Data Science team one simple question: what is your favorite data science...

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… Using NLP to communicate at scale Last episode, we discussed the history and practice of natural language processing, or NLP. This month, we’re here to discuss an exciting and cutting-edge application: using NLP to help businesses converse with their customers at scale. See the power of NLP in action as we talk with NLP experts on the Conversation AI team at Klaviyo about: How NLP enables a qualitative shift in...

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… What’s the deal with natural language? Natural language processing, or NLP, is one of the dominant forces in modern data science, and it’s produced a host of data science-powered products many people take for granted as a basic fact of life. It hasn’t always been so powerful or pervasive, though — NLP has a long and interesting history, and some of the advances powering today’s technology would have seemed like...

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… Thinking big-picture with A/B testing We’ve discussed A/B testing multiple times on this podcast, for good reason. But there’s an important angle we have yet to cover: in the life of a researcher or marketer, there’s no such thing as an A/B test. There’s an entire system of A/B tests run for specific purposes over time. What is the best way to construct a system of A/B tests to help you learn, improve, and grow...

Welcome back to the Klaviyo Data Science podcast! This episode, we dive into… Using data science to help people write Using machine learning models to generate text, images, and other creative objects is, as they say, a bit of a hot topic right now. There are examples of models like this in action all across the internet and across different fields and disciplines. Today, we discuss one of those fields in more depth: marketing. In particular, the Klaviyo data science team recently released...