Built In NYC: How These Teams Turned Their Data Into a Product

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In years gone by, upselling involved a sales representative speaking directly to a customer, and suggesting vaguely related items in hopes of enticing them to increase their purchases. Now, with data and analytics products constantly developing in real time, a recommendation algorithm does the heavy lifting.

As of October 2021, Amazon was valued at $1.75 trillion, a huge portion of which can be attributed to its sophisticated recommendation engines. By collecting and utilizing data on when customers purchase their items, how they rate those purchases and what other people with similar buying habits are purchasing, Amazon is able to recommend and offer suggestions to upsell items to their customers. This strategy presents a substantial value, but taking the large quantity of data that a company collects and turning it into a usable product is no easy feat.

“To bring our new product to life, we first had a lot of questions to answer,” explained William Watson, data engineering lead at MayStreet. “Deciding on constraints and making these decisions was a massive team effort, activating our entire organization and client base.”

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