How Good Eggs uses Segment & Snowflake to boost operational efficiencies and improve shopper experience

Good Eggs expanded how they used Segment throughout the company to improve warehouse efficiencies and create personalized experiences for customers

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“Segment has proven not only to be simple, fast, and reliable — but also an accelerant. Engineering can now focus on features and use cases, rather than infrastructure to get our events from the edge to downstream systems.”

— Bob Zoller, Founding Engineer, Good Eggs

Summary

Good Eggs is an online grocery delivery service that’s pioneering a new way for people to feed their families. The Bay-Area based company is the only service to deliver absurdly fresh local produce, meal kits, grocery staples, household products, and wine. They offer same-day delivery throughout SF, East Bay, Marin, the South Bay and the Peninsula.

The majority of Good Eggs’ assortment is from local farmers and foodmakers, and every item carried must meet a strict list of sourcing standards. In addition to good food, the company provides good jobs. Every operations team member has benefits, carries equity, and is paid a living wage. 

Bob Zoller, Founding Engineer at Good Eggs needed to innovate to compete in a growing market. Bob turned to Segment and Snowflake to tap into advanced analytics and cross-product analysis personalized shopping experiences and agile operations. 

The Challenge

Creating personalized experiences for shoppers

Good Eggs works with local growers and producers to make the freshest groceries available to its shoppers. The company is hyperfocused on sustainability and combining same-day delivery with your local farmers market. Therefore, creating personalized interactions is key to building trust on the platform and making it easier, faster, and cheaper for customers to find what they need.

Before, Good Eggs went through classic market research exercises to create customer personas. Old-school customer interviews and resource-intensive research were helping Good Eggs understand behaviors and influences that drove action. But this approach made it difficult to scale for a growing company with a diverse customer base. 

“User research and talking to people helped us craft a couple of personas. But at the moment, we could only focus on the biggest group. They became a kind of guiding light for us.” — Bob Zoller, Founding Engineer at Good Eggs. 

Bob and his team knew there was a more data science-friendly approach to building personas. And he didn’t want to get there by talking to people, he wanted to examine how customers were using the platform, learn more about their behaviors, and come up with new features based on that data. 

Essentially, the team needed a more streamlined way to:

  • Find patterns and ideas from data

  • Suggest new hypotheses for testing

  • Condense data into actionable insight

  • Integrate research results into models

Using data science, Good Eggs would be able to use analytical modeling to find new and emerging trends amongst its shoppers from a range of sources and help drive new sales for the company.  

Uncovering operational efficiencies 

Local grower and producer relationships help companies like Good Eggs make their service more attractive. More supply means more selection, availability, and price competition, all of which are powerful in bringing in new demand.   

For Good Eggs, this meant looking at how they could streamline warehouse operations. Bob adds:

“Operational efficiency is always something we’re thinking about at Good Eggs. How much does it cost to deliver groceries to customers, and how can we make it better to put more dollars into the pockets of our producers, farmers, and food makers.” 

Good Eggs needed more insight into how smoothly operations were running in the warehouse. The operations team had data coming in from a handful of different end tools but needed a way to keep it all in one place. This was not the type of data collection Good Eggs required to scale its company.  

So how could the company build and maintain a flexible data infrastructure without putting a significant tax on engineering resources? 

The Solution

After moving data warehouses from Redshift to Snowflake, Good Eggs knew Segment was the ideal solution to tap into advanced analytics and cross-product analysis. 

Bob explains:

"At first, we were using Segment solely for the sort of traditional e-commerce use case of tracking customer events. But when we saw that you could point and click that data into your data warehouse, Segment became an obvious tool for us."  

Good Eggs was already using Segment for e-commerce eventing track through its website and mobile app on the front end. When Bob realized the platform was already in place, he knew Good Eggs could also use Segment to route operational software from its backend into the platform. This led to significant cost savings because the engineering team didn’t need to build out an initial infrastructure or maintain a data pipeline. 

He adds:

"We wrote all of our warehouse management software from the beginning and have built up this ecosystem. All of that software transmits events, flows events through Segment. It’s proven cost-effective and has made managing data easier." 

E-commerce and operational data are now routed into Snowflake’s Cloud Data Platform to perform modeling in Mode. 

For example, Segment now helps Good Eggs manage Zendesk data. If a customer sends a support ticket to the Community Care team, they annotate the case in Zendesk, then route the data back into Snowflake. This has helped Good Eggs better track error rates so it can quickly solve small issues before they become big problems. And all it took was a quick integration with Segment. 

No longer do Bob and his engineering team need to craft a data pipeline to process data. He simply sends Segment an event in JSON construct, then seconds later the data shows up in their Snowflake table.

Bob describes:

“Now we send data to Segment one time, then have the platform rebroadcast to multiple different systems. We don’t have to spend any engineering time, and it continues to pay off as you add other downstream destinations.” 

The Results

Good Eggs knew personalized shopping experiences and agile operations were essential to keep customers and producers happy. A dispersed data infrastructure wouldn’t help the company scale, as its customers expected a fast and easy experience to get what they need and help drive demand for producers. 

“Segment has proven not only to be simple, fast, and reliable — but also an accelerant. Engineering can now focus on features and use cases, rather than infrastructure to get our events from the edge to downstream systems.”

By partnering with Segment and Snowflake to route and act on data, Good Eggs was able to innovate faster and run a more agile operation. Today, Good Eggs preserves engineering resources and financial investments by not building and maintaining its own infrastructure and pipelines, which gives them time back to focus on building features and product offerings that transform the business. With these new efficiencies, the Good Eggs team is able to deliver an improved shopper experience for its customers. 

Industry: Retail
Location: San Francisco, CA