Imagine how many products you can buy at your local grocery store. Now multiply that number by 2,000 and you start to get a sense of how much data Peapod Digital Labs deals with on a regular basis.
With the uptick of people interested in avoiding the grocery store due to the pandemic, delivery service Peapod filled a vital need for shoppers over the last 18 months. And for those who grew accustomed to receiving deliveries, there is little upside in returning to the headache of in-person shopping.
For Peapod, that meant they needed to scale their data team to keep tabs on inventory, availability and delivery times. For Rosie Poultney, head of advanced analytics, growing a team with the backdrop of a global crisis was no easy feat — but it helped to remember the small things when searching for new team members.
“I love people with strong opinions on supermarkets or grocery products,” Poultney said. “We analyze customer behavior to create a better experience, and it helps to remember your frustration that time your favorite product was not on the shelf.”
It’s that deep empathy with the user, along with some cutting-edge tech skills, that’s lead to her team’s success. To better understand how Poultney and her crew have handled the challenges of scaling and evolving how Peapod does big data, Built In Boston connected with the local leader.
Peapod Digital Labs aims to shake up the traditional grocery-buying experience through customer-centric tech tools that allow users to order groceries for pick up and delivery.
When it comes to scaling your data team, what are the most important personnel decisions you’ve made?
Analytic and coding skills are table stakes. Aside from those, I look for people that enjoy being part of a team and are good communicators. We are a large team and often work on the same problem from different angles, so collaboration is key. I value domain expertise in retail, particularly grocery, and preferably those with an online and physical store presence.
On the technical side, what steps have you taken to make sure your tools, systems and processes are set up to scale successfully alongside your team?
Our data landscape is continually evolving, whether that be the ability to pay online using electronic benefits transfer (EBT), new tools or a new process to deploy models. It’s key to encourage curiosity and innovation in the concept stage, but successful scaling requires rigor.
For example, we might start developing a new model in R, then switch to PySpark for the final model. Code must match internal standards, be unit tested and validated, and tuned for performance. There are clear criteria for handover to the production team. Our parent company, Ahold Delhaize, has over 2,000 stores in the U.S. and a thriving online grocery business – that’s an immense amount of data every day, even before we start overlaying promotional offers and marketing communications.
It’s key to encourage curiosity and innovation in the concept stage, but successful scaling requires rigor. ”
What’s the most important lesson you’ve learned as you’ve scaled your data team?
To appreciate the value that each person brings to the team and to continue to invest in their development. That means learning new skills, attending conferences and regular benchmarking against the market. But it also means making sure every voice is heard.
Collaboration and sense of belonging became more important and challenging as we rapidly grew the team. Actually, working remotely helped us! We set up a chat channel for the team where everyone shares their accomplishments, data questions, thoughts and interests — and we’ll keep using it even when we are finally back in the office. Right now, I’m not sure we even notice that our team is spread across five states!
