Episode 19: Smart Stores, Analytics, and the Future of Retail with Gregg Golembeski CTO at Dor

Today's Guest: Gregg Golembeski, Founding CTO at Dor

Hosts: Fahad Shoukat and Andrew Wolfe

Today's Guest: Gregg Golembeski, Founding CTO at Dor

Gregg is a software engineer passionate about shaping the architecture of complex systems at a high level. In 2015, he co-founded Dor with Michael Brand.

Show Notes

Introduction: Gregg Golembeski founding CTO at Dor (0:36)

--Gregg Golembeski founding CTO at Dor

--Before helping found Dor, managed software team at a nutrition analytics startup, as well as spent time at Apple where he worked with his co-founder

-- Dor created the first battery-powered thermal sensing people counter for physical businesses to harness real-world foot traffic for better-operating decisions

--In this episode, we speak about smart retail and smart store...how physical stores are getting smarter and the future of retail

--“Having data no one else has is invaluable”

Why measure incoming foot traffic? (10:17)

--Small retailers focused on real-time data, the big ones want to look at trends

--Need to challenge baselines and assumptions they have...the error in those assumptions have     rippling effects on decision making

--Help make decisions based on measurement

What analytics are important to retailers? (11:41)

--Foot traffic data (just like online store wants to know how many people visit site), offline retailers/brick and mortar haven’t had opportunity/cost-prohibitive until now

--Dor aims to be Google Analytics for physical retail stores

--Use foot traffic data to look at conversion rate, sales performance, when people come in (helps with staffing), marketing effectiveness as a measurable metric

What prevents retailers from having this data already? (15:11)

--Humans inherently human - get distracted, make mistakes when counting

--Getting data back from a person to a centralized system, in a timely fashion, has lots of room for error

--Capturing in real time increases chances of accurate data, to determine real trends

Privacy (17:02)

--Dor makes their sensors anonymous - retailers given data they need for correlations etc...to make informed decisions for marketing

--Still giving shoppers visiting these retailers their privacy

Integrations (17:27)

--Foot traffic data gets useful when it's correlated with other data sources especially when that coincides with what happens at the cash register

--One click integrations with Shopify, Square, Light Speed, all modern connected point-of-sale systems, but also works with older pos systems to get the data

--Also pulls in weather data, retailers see a strong correlation with weather and shopping

--Try to pull in as much data about stores as possible, but trying to maintain focus on personal privacy, while helping retailers optimize

--Dor has a dashboard that shows correlation with different data sources; this year they are going with that, but now with cheap machine learning, Dor can start doing lots of correlation analysis with different data points and find interesting trends

--More data that can be acquired about the physical store, the better the correlations will be

Smart Retail Future (21:24)

--IoT, smart devices - not in the retail space at the moment, but something Dor really thinks about. Gregg personally passionate about this particular subject

--Huge opportunity to enter the market

--Big Box stores of the world have a different set of problems than most of Dor’s customers

Additional Challenges (22:51)

--Challenges in retail to become “smart stores”

--Dor has chosen battery-operated peel and stick very easy installation approach, that doesn’t require retrofitting because that can be a significant challenge

--Easily adoptable low friction solutions have the biggest impact early on

--Can throw lots of tech at a store, but need data to be actionable and Dor does that

Conclusion (30:07)

--Cool tech moving forward?

--Dor always evaluating new technologies

--For Gregg personally, machine learning is super exciting and it’s getting so cheap to take large data sets and throw models at them to look at correlations and find answers - which is incredibly interesting

Build Thoughtful Software
Fahad Shoukat
Written by

Fahad Shoukat

Fahad has a B.S. in Electrical Engineering and an MBA. He brings over 15+ years in Business Development, Strategy, Sales, Product, and Marketing in various industries such as software development and Internet of Things (IoT). His experiences have led him on an unwavering pursuit to meet thoughtful people and build thoughtful software.