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Drew Paquette

VP of Product at Optable

Drew Paquette is a professional experienced in product development, agile SDLC, product strategy, analytics & measurement, go-to-market, user privacy, and user experience with a passion for leveraging data to create engaging and profitable solutions.

His domain expertise includes identity resolution, data-as-a-service, CTV, privacy-enhancing technologies, ad verification, and programmatic advertising.

Aside from his professional endeavors, Drew identifies himself as a Father, Husband, Musician, Outdoor Enthusiast, Animal Lover, and Recreational Nihilist.

He holds a Bachelor of Arts (B.A.) in Industrial and Organizational Psychology with a Minor in Business from Penn State University and a Master of Science (M.S.) in Human-Centered Computing from the University of Maryland Baltimore County.

Drew has held key roles in various organizations, including VP of Product at Optable, Product Consultant at Optable, VP of Product, Identity at Innovid, Head of Data Products at 33Across Inc., VP of Product Management at DoubleVerify, Head of Product at Accordant Media, Director of Audience Products at Collective, Director of Product Management at Targetspot, Senior Product Manager at AOL Advertising, and Analyst at Citi.

Highlights

Aug 7 · linkedin

Ever tried building an identity graph from scratch? Well, it's not easy, friends (trust me 😅).

Better yet, say you're a publisher sitting on loads of customer data, web visitor data, mobile data - heck, possibly even CTV data, if you're lucky enough (such as OEM IDs). Now when someone invariably asks, "What is your true, deduplicated addressability, at both a person- and household-level?" Are you able to answer them succinctly and confidently?

Or how about:

  • Which sources of IDs are most contributing to your graph and where are you deficient?

  • What is the strength of your linkages (or edges), and further, do you have the ability to freely interrogate your graph to ask additional questions, applying your own heuristics, even?

  • Can you manipulate your graph to suit your "sweet spot" of accuracy and scale, by adjusting the knobs just the way you like?

  • Are you able to cleanly compare 3rd-party identity graphs, side-by-side, to determine which one is providing more incremental HEMs, IPs, MAIDs, and the like?

And probably the simplest, most important question: are you able to get an accurate, up-to-date view of your entire 1P universe at anytime, across all your data sources and ID types, including both raw and collapsed counts of IDs?

If the answer is no to any of the above, you might want to start thinking about this. Clean identity resolution is the foundation from which all monetization flows! Gotta get your house in order before tackling the fancy stuff, is what I say. (And I'll get to some of that fancy stuff in another post, btw. 😉)

In the meantime, here's a great primer to get those creative juices flowing: https://lnkd.in/eDcsnhwU

#1pdata #identityresolution #identitygraphs #buyvsbuild (hint: buy)

Aug 6 · linkedin

Whoa.

Drew Paquette
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Location

Montclair, New Jersey, United States