Traveling the Path from Irvine to Brooklyn with The Quirks Event

Irvine CAI’m just back from the Quirk’s Event, California edition.

If you haven’t heard, Quirk’s Marketing Research Review launched an industry conference in 2015. A bold move in an already crowded conference market. Bolder still? They held it in Brooklyn—in February! Despite the risk of New York snow, the event was a huge success; so much so that they did it again in 2016. This year’s Brooklyn event will be held March 21-22, 2017. Also this year they added a west coast version. Kudos to the Quirks’ team: the new location was a good move. I met many research and insights professionals from the west coast.

Personally, I attend industry events to have conversations with current and new contacts. I want to hear what work challenges they are facing so I can determine what new skills they’ll want to develop.

So, what did I learn this week about the professional challenges research and insights professionals are facing? I picked up on a few themes, but one really stands out to me: let’s call it the specialization paradox.

Research and insights professionals are under pressure to complete diverse types of projects with excellence: customer satisfaction, product concept testing, message testing, pricing and more. But each of these areas, and others, are becoming highly specialized. That is, the methods and tools for doing any one of them well requires significant time and effort to master. So, how can one person master several of them? Sure, there are some methods and tools that are horizontal, but the number of vertically-optimized methods and tools is rising rapidly.

What’s a researcher to do?

I think professionals must answer a tough question:

Do you want to be a generalist or a specialist?

Both options are valid—but different. The skills are different. The allocation of workday hours is different. The client relationships are different. The training is different.

Professionals who try to be both awesome generalists and specialists will be frustrated. There is simply too much to know, too many new methods and too many tools to consider and test.

So, which path will you choose?

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