Thought Leadership

Confessions of an adtech product manager

October 19, 2016
By Adam Soroca

Over the past two decades, I’ve been incredibly fortunate to have been a part of teams that built cutting edge search and digital advertising platforms. From paid-search to bid-management tools to bidded display marketplaces to demand side platforms the tools we imagined and brought to life were used by leading advertisers all over the world.

As I reflect upon a common thread that clearly ran through these previous roadmaps, we planned to do things much differently this time around at nToggle.

Historically, my teams followed the same methodology – throw people at the problem, then solve with software. We launched and scaled our businesses with teams typing in metadata to describe mobile search listings or optimizing the top mobile games of the era through a UI. The controls, optimization and results we gave were state-of-the-art and the approach absolutely made sense at the time.

In fact, some R&D leaders guided us to watch how humans used the tools before automating. Through this approach we identified the right levers to optimize upon and built successful platforms.

As I’ve now learned, this same phenomenon played out in droves across the industry. The inside baseball view into other leading companies revealed to me that perceived algorithmic leaders optimized behind the scenes with spreadsheets initially followed by automation. Described to me were rooms full of spreadsheet jockeys, crunching data to determine how to best boost campaign performance. Even if machines were doing some of the work, people fine-tuned campaigns by hand.

As we launched nToggle, we knew we had to take a different approach. Programmatic buying delivers great results since demand precisely cherry picks across the many attributes broadcast in the bid request. This leads to hundreds of thousands of combinations for each demand – supply pair.

While voices from product management past whispered to throw people at the problem first and automate later, our solution stretched way beyond human capabilities. Buying time to get the machine learning off the ground was not an option. Machines were the only option to optimize the bid stream.

Over the past 18 months we invested heavily in AutoToggle, our machine learning platform. Not just the algorithmic math equations, but the end-to-end system that collects data all the way through to the automatic toggle publishing. With 60%+ of our engineering resources developing the platform, we used best practices through prototyping and clear MVP deliverables to avoid over-investing before we had proof of concept.  (Read Young Blom’s blog: Product Management Best Practices.)

Looking back, we are fortunate to have made the decision to invest in AutoToggle early on. Instead of just starting to automate this far into a product as past history might have suggested, we are now on AutoToggle V2.0 delivering fantastic results for our buyers.

Stay tuned for more details on AutoToggle from Shrikanth Mysore our Chief Data Scientist.

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