You Ask, I Answer: Leveraging Engineering Talent for Marketing?

In today's episode, Heather asks if you had the ability to add an engineer to your team with a penchant for data analysis and interest in SEO but no marketing comms background or experience, what are some of the ways you would use them? That's an interesting question because it depends. It depends on the kind of engineer a person is. Those are I would not soft skills, but they're core engineering capabilities that you can actually pick up and uh do uh domain transfer to the discipline of marketing. So let's take uh stochiometry, the ability to do balanced formulas.

Somebody who's good at that is going to be somebody who is uh good at taking uh complex marketing formulas. If you think about some of the more complex formulas we deal with in marketing um that require advanced calculus and linear algebra skills, multiple regressions, uh gradient boosting, um techniques like uh really complex calculus equations for understanding uh how knowledge spreads across a graph. Uh those are all things that are not necessarily native to engineering but an engineer could pick up and learn very very quickly very easily it would not be a very difficult lift for them to go from uh say doing mass energy transfer to understanding uh network dynamics the challenge that you face um here is determining what that particular engineering person's background is and what they're conceptually good at and then mapping that to your needs so if you have needs in SEO or uh needs in uh basic marketing analytics and stuff how do you map that person's skills to that somebody who's really good for example with biostatistics is gonna have an easy time an easy time with Google Analytics right in terms of uh uh complexity of environments Google Analytics is way less complex than a lab bench um and so you can take those that person's skills and then try to figure out how do you solve um your challenges with their skills. The the tougher part actually is on your side, which is to be clear about what your needs are. Like this is uh when you're dealing with engineers, uh you have to be clear.

Uh you have to be very, very clear. This is what I want, this is the outcome I'm looking for. Uh not, hey, I'd like to you know make SEO better. That's way too open-ended. That is not something that is scientifically rigorous, uh, and is not something that uh an engineer is gonna be able to go, oh, I know what to do with that.

They're gonna look at you and go, and what would you like me to do? So your challenge would be something like, I need a way to do regression testing with Google Analytics data. Um there will be some things that they will not have aptitudes for. Um a lot of data engineering is not something that in my experience, um, you know, other scientific engineers have had a lot of success with, excepting of course, um, software engineering. Uh that is, for example, if you sat down a chemical engineer in front of Google Analytics data, they could probably make something of it.

If you told them to write software to go connect to the Google Analytics API and retrieve the data, that's not gonna go so well. Um there are exceptions to every rule, but in my experience in in working with various people, that's not something that they would be able to do easily. So you'll still have some challenges on the data engineering side that really only data engineers and software engineers are best suited for. Obviously, anybody can learn anything, but out of the box, that's the person's not gonna have strong aptitudes there. The big question I would also have is what software skills do does it this engineering person have.

Someone in you know, biostatistics, for example, is gonna have a very strong background in either SPSS or R, and those two languages are obviously very, very well suited for working with marketing data. Um I use R all the time, literally every single day, um, to do even relatively basic stuff like getting data out of Google Analytics. It's just a fantastic uh piece of software for that. Um uh software engineers probably gonna have some experience in Python, and that's definitely going to be something that uh will be coming exceptional handy when pulling data out of various applications. Uh again, with the caveat that most folks in scientific engineering don't have a lot of background in extracting and you know data out of APIs.

Um so just know that that's a a thing. Uh you do want to look for somebody ideally who has coding skills because there are certainly a lot of purpose-built applications in every scientific discipline, that you know, pre-packaged apps, that those will not transfer well into marketing. Uh those will not transfer uh probably at all into marketing, but somebody who can write code um is gonna do well. Uh the other thing that is important is you're gonna need to have good management of the person and understand that managing engineers typically is different than managing uh other kinds of employees. Uh they are, again, very outcome driven.

You have to be extremely clear. They they some have good project management skills, some do not. Uh that varies pretty wildly. Um and giving them feedback and things. One of the challenges you're gonna run into is you may not necessarily know how they're doing something, and uh you will have to work with them to have them explain how they do things um so that you can offer feedback about how they might do it better.

Uh one of the challenges I've had in my career is uh I've worked for people who don't understand what I do and so therefore cannot offer guidance about how to improve my processes or how to improve my code. Um certainly they can offer feedback on how I can improve the outcomes I deliver, but it is then left to me as a self-directed learner to figure out how can I make this better on my own. And if you have a person who needs a mentor, who needs um external assistance to be um uh motivated to learn, uh you're gonna have to learn that early and find somebody who can provide that guidance uh because it's really challenging for the person if they get frustrated and there's nobody they can turn to for help. So there's a few of the thoughts. Um I would have them again focusing on all if they're if they're a good engineer, they should have a good technical background uh in data analysis.

Uh that's true of almost every kind of engineering. And I would look at the data challenges you have and say, okay, how can we map this person's skills to this person to our challenges? And again, that requires really getting to know that person and what the skills are that they have. Um interviewing, uh seeing their work, uh, trying to understand the p kinds of problems they've solved. I think is a really important uh to be able to ask them, show me show me some of the tougher problems you've solved, some of the the more difficult experiments that you've run and how you navigated those challenges.

That kind of informational uh questioning before or after hiring the person uh will be beneficial to you to understand them better. If you've got follow-up questions, leave them in the comments box below. Subscribe to the YouTube channel on the newsletter. I'll talk to you soon. Take care.

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