This week we’re talking with roboboogie Account Specialist, Tyler Hudson. Tyler comes from an analytics background, and loves using data to solve problems for our clients. Curious about how this process works? Read on to get a snapshot of Tyler’s role at roboboogie, his top picks for exciting trends in the industry, and tips for how to get started with data-backed decision making.

Tyler Hudson Tyler Hudson

Why did you choose to pursue a career in analytics?

Looking back, I think my decision to pursue a career in analytics stems from a conversation I had with a role model of mine on the future of data analysis and the rapid advancement of the field.  It’s a conversation I’ll always remember because it was the driving factor for me to change my degree. Once I switched, I immediately saw the opportunities analytics can provide, and after I researched marketing analysts, I knew this is where I should be.

Working at an agency has allowed me to show clients the applicability of data and its ability to paint a more holistic user picture. My job is very rarely strictly pulling out numbers; that’s the start, but the majority of the work is digesting and interpreting the data in a meaningful way. I’ve always enjoyed searching for the minute details in things that may occasionally be overlooked, and a career in analytics has allowed me to do just that.

How do you approach solving problems with data?

In my opinion, data provides a solid foundation for solving almost any problem. I like to think of data as an opportunity to remove subjectivity from decision making. It’s fundamentally grounding, because no matter what level or rank you are in your organization, if you have the data to support a decision, it’s hard to dispute.  That said, it’s important to have deliberate and accurate data collection methods to ensure your team is receiving the most useful information possible. Inaccurate or misleading data can be worse than no data at all.

The real power comes from leveraging the collected data to influence decision making. I’ve found it’s often well-received in a visual format, so after the hard analysis is done for the problem, I then implement a visual presentation.  If the visual element is done correctly, it will allow anyone, no matter their experience with data, to understand the information you’re presenting.

What digital analytics trends are you currently most excited about?

Predictive analytics. I’m using this term as a catch-all for a large number of other skills, but in my opinion, predictive analytics is the future of data analysis. The power to accurately predict events is something every marketer wants in their back pocket. However, I do not think it will come to a time where you sit back and let the algorithms go to work. That will be the first step, but there is still a need for analysts to convey the results to a broader team to make impactful decisions. At roboboogie, we’ve found our niche in leveraging data to guide design decisions, and the results are impressive. Data in itself is compelling, but the real potential is uncovered when you instill data fundamentals across multiple teams.

What is an opinion you have about about the analytics and optimization industry that others might not agree with?

One thing I feel very strongly about is ensuring there is a level of empathy when conducting data analysis, especially when building out machine learning algorithms. It’s a hot topic in the industry, and the timing could not be more important.  

At a surface level, empathy and data do not naturally pair together. It’s the classic left brain/right brain argument, and after taking a look at the potential implications of data analysis, the two begin to align more cohesively. There’s a point where leaning on data can divide demographics even more, particularly when AI is being used to analyze data and make predictions. It’s important to remember that behind every single point of data there is a person. I’ve found maintaining this mindset has improved my day-to-day work, and allowed me to take customers into account. At the end of the day, our goal as marketers is to delight and engage with our consumers, and if we are already segmenting them based solely on what the data says our jobs become significantly more challenging.

What advice do you have marketers who are interested in harnessing both quantitative and qualitative data to make decisions?

Dive in and ask questions. This is applicable no matter your level of experience.  If you’re early in your career, the best way to learn is by doing. Data science can be daunting with the amount of information available and the rapid advancement of the field.  You can learn a new skill every day and still not know everything, but it’s the effort to develop your skill set that is most important.

One skill I’ve recently been working on is developing my qualitative analysis abilities. The insights qualitative analysis can provide are astonishing. When you think about it, the majority of qualitative data is user-generated, so it makes sense to listen and learn from what your customers are directly telling you. The real power comes when both quantitative and qualitative data are taken into account simultaneously. It’s a balance of weighing the sometimes heated reviews, and the overly positive reviews to find the most relevant, intentional data. Adding this analysis skill will give you a holistic view of your most important demographic, your users.  

Thanks for letting us pick your brain, Tyler! Want more insights from the roboboogie team? Stay tuned for our next interview with a roboboogie pro.

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