Use Consistent And Up To Date Customer Profiles To Power Your Business With Segment Unify
Data Engineering Podcast
00:00
How to Implement a Segment Unify Product
Segment unify was designed to be less compute intensive or space efficient. The product is embedded into the connections part of the segment app. If you are using segment for identity resolution already, setting up profile sync is also fairly simple.
Transcript
Play full episode
Transcript
Episode notes
Summary
Every business has customers, and a critical element of success is understanding who they are and how they are using the companies products or services. The challenge is that most companies have a multitude of systems that contain fragments of the customer's interactions and stitching that together is complex and time consuming. Segment created the Unify product to reduce the burden of building a comprehensive view of customers and synchronizing it to all of the systems that need it. In this episode Kevin Niparko and Hanhan Wang share the details of how it is implemented and how you can use it to build and maintain rich customer profiles.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudderstack
- Your host is Tobias Macey and today I'm interviewing Kevin Niparko and Hanhan Wang about Segment's new Unify product for building and syncing comprehensive customer profiles across your data systems
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Segment Unify is and the story behind it?
- What are the net-new capabilities that it brings to the Segment product suite?
- What are some of the categories of attributes that need to be managed in a prototypical customer profile?
- What are the different use cases that are enabled/simplified by the availability of a comprehensive customer profile?
- What is the potential impact of more detailed customer profiles on LTV?
- How do you manage permissions/auditability of updating or amending profile data?
- Can you describe how the Unify product is implemented?
- What are the technical challenges that you had to address while developing/launching this product?
- What is the workflow for a team who is adopting the Unify product?
- What are the other Segment products that need to be in use to take advantage of Unify?
- What are some of the most complex edge cases to address in identity resolution?
- How does reverse ETL factor into the enrichment process for profile data?
- What are some of the issues that you have to account for in synchronizing profiles across platforms/products?
- How do you mititgate the impact of "regression to the mean" for systems that don't support all of the attributes that you want to maintain in a profile record?
- What are some of the data modeling considerations that you have had to account for to support e.g. historical changes (e.g. slowly changing dimensions)?
- What are the most interesting, innovative, or unexpected ways that you have seen Segment Unify used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Segment Unify?
- When is Segment Unify the wrong choice?
- What do you have planned for the future of Segment Unify?
Contact Info
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
- Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
- Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
- If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com) with your story.
- To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers
Links
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Sponsored By:
- Rudderstack:  RudderStack provides all your customer data pipelines in one platform. You can collect, transform, and route data across your entire stack with its event streaming, ETL, and reverse ETL pipelines. RudderStack’s warehouse-first approach means it does not store sensitive information, and it allows you to leverage your existing data warehouse/data lake infrastructure to build a single source of truth for every team. RudderStack also supports real-time use cases. You can Implement RudderStack SDKs once, then automatically send events to your warehouse and 150+ business tools, and you’ll never have to worry about API changes again. Visit [dataengineeringpodcast.com/rudderstack](https://www.dataengineeringpodcast.com/rudderstack) to sign up for free today, and snag a free T-Shirt just for being a Data Engineering Podcast listener.
The AI-powered Podcast Player
Save insights by tapping your headphones, chat with episodes, discover the best highlights - and more!
