Interview with Julius Fedorovicius, Founder of Analytics Mania
We continue our Expert Opinion column, and today we present to your attention an interview with Julius Fedorovicius, a digital consultant, and technical expert. Julius is a founder of Analytics Mania and a Google Tag Manager and Google Analytics Enthusiast. Also, he’s been running the Google Tag Manager community on Facebook and creating GTM online courses.
As always, here are the main topics for navigation:
Table of contents
Could you say a few words about yourself and your previous experience?
I run Analytics Mania. Here I share blog posts, video tutorials that teach marketers and analysts to work with Google Tag Manager and Google Analytics. Also, at the moment, I offer two paid courses about Google Tag Manager. Also, occasionally (when I have time in my schedule), I work as a GA/GTM freelancer. But my current main focus is content, courses, and students of those courses.
Skills and problems
What hard and soft skills are most important for analysts today?
Hard skills. I am biased here (since I am coming from the analytics implementation side) but I would say that technical skills related to tracking are important. Understanding how tracking works will help analysts better understand where the data is coming from, how it is collected, and how reliable it is. This will also help analysts to add more grains of salt into the data they are working with and the results they get.
But that is just a part of the skills needed. Tracking implementation alone is worthless if there is nothing done with the collected data. That’s where the analysis part comes in and here we have both hard and soft skills.
Speaking of hard skills here, that very much depends on the company you work in and its stack. For some, it’s enough to use Google Analytics and Data Studio, for others BigQuery, knowledge of SQL, R, Python, etc. The rabbit hole goes deep.
I would recommend not focusing on particular tools. Instead, focus on what kind of questions must be answered with data. Then pick the right tools for that. Knowing BigQuery, R, or whatever, and trying to apply that for small businesses does not make sense (at least in most cases). Small businesses will (most likely) not have enough data to benefit from those technologies.
Since I am mainly working with small/medium businesses, in most cases Google’s essential analytics stack is sufficient (GTM, GA, GDS). It is completely possible to achieve a lot of improvement/change thanks to analytics and “small” data.
So if someone is thinking about starting a career in this field, I think that having knowledge of GTM, GA, and Google Data Studio is a good start (when it comes to hard skills).
Now, the soft skills of an analyst. I would say that they are:
- Planning. If we are talking about web analytics, things like measurement plan and tag implementation plan can help you immensely. To prepare a plan, you will have to talk a lot with stakeholders, get more familiar with business objectives, processes, etc. This helps analysts see a bigger picture, hence (hopefully) provide more value with analysis.
- Sounds cheesy, but: Careful listening to what others say. Also, communication. This will help you with things like preparing measurement plans and also will allow you to better communicate your findings.
- Critical thinking. This will help you identify patterns, dig deeper, and find some insights.
What’s the biggest mistake an analyst can make? Can you share some of your analytical mistakes?
I know that you are asking for one big mistake, but I could not choose just one :) Here are my thoughts on the biggest mistakes:
Mistake #1. Always trusting your data and having no doubts in it. Data will never be perfect, there will always be inaccuracies to some extent. So whenever you discover something valuable with your analysis, always doubt it. Try to check it from different angles. If you are familiar with analytics implementation, try to think of what could have gone wrong in your data collection. Also, what is the source of that data? This is where critical thinking is needed. However, on the other hand, don’t wait for perfect data. Spending too much time on trying to get some perfect dataset will be more costly for a business rather than getting insights from “good enough” data.
Mistake #2. Thinking that an analyst's job ends with analysis and finding some insights. Analysts should also be the drivers of change in the organization. They must communicate their findings, advocate for certain solutions to get those findings implemented. And in many cases, this is the most difficult part.
Do you think miscommunication between analysts and marketing teams is common? Do you have any recommendations on how to overcome it?
Very much. And it is one of the biggest obstacles that stands between finding some insights and making sure that they drive some change, improvement. Some of the ways how miscommunication can be avoided:
- Listen carefully to what others say. This helps to better understand business requirements for particular analytics tasks. And when the marketers provide some feedback, carefully listening to it will help you reduce future miscommunication and avoid re-doing.
- Avoid your professional slang as much as possible. Try to speak in simpler terms that are understandable even by those who have no direct relationship with analytics.
- Spend as much time as you can in order to understand how business operates. Seeing a bigger picture might help analysts better understand what (and why) marketing teams need certain results from analysts.
- Don’t be afraid to ask questions. By asking “why” you are not showing that you are stupid. It shows that you want to understand the context, which will help you better perform tasks. When you get an assignment and some parts are unclear, don’t presume. Ask for clarification.
What professional resources or events can you recommend for analysts?
As for events, I would definitely recommend MeasureCamp and I can’t wait to get away from virtual events and jump back to the in-person meetups. MeasureCamp is just a perfect place to hangout, learn from others, try your own presentation skills (that are also very much needed in analytics).
Also, I just love SuperWeek. But I don’t look at it as a conference where I will be constantly learning something new. I look at it more like an analyst's vacation where you have a bunch of like-minded geeks talking about what they love for almost a week. You meet a bunch of stars of the industry here and casually talk with them, network. There is just something magical there.
As for resources, that really depends on what the analyst specializes in. Is it closer to the mainstream analytics (like GTM, GA) or is it closer to the actual data science, data engineering, etc. If it's the former, then (shameless plug) my own blog and youtube channel can definitely help. Simo Ahava’s blog, Measureschool, to name a few. If it's the latter, then Datacamp can help.
What knowledge are analysts and marketing specialists missing in order to make companies data-driven?
I think that the most important knowledge needed here is understanding that the data/analytics stack (and processes) in the company must be picked based on the company’s level. And then it should grow together with the company.
In my opinion, small companies should not be chasing shiny items like AI, ML, Big Data. First of all, small companies don’t even have enough “small” data. I recently read this and it perfectly summarizes the situation and the reasons why company’s analytics should grow/scale together with the company.
What analytical challenges do you have at your company right now? What tools do you need to overcome them?
Currently, I am self-employed. So my main challenge is to practice what I preach :) By constantly creating content, updating my courses, and supporting course students, I don’t always have enough time to dig into my own data and add some additional tweaks. A shoemaker without shoes.
What difficulties do you see when it comes to implementing analytics and how would you assess the overall development of the market?
Friction with the IT department is definitely a large one. Otherwise, GTM would not be that popular.
Ever-changing landscape of user privacy. Especially when it comes to major players (like Apple). What you build today has a high chance of not working in the next 6-12 months. Staying somewhat up-to-date is very time consuming (but necessary). I may be wrong, but it feels like we had more changes in the last 2-3 years than we had in the previous 6-7 years). I hated this phrase throughout the pandemic, but “this is the new normal” for us.
How can an analyst have a greater impact on marketing? How can they be useful for the marketing team?
- I think that previous questions about communication are super relevant here. Start with that and you will definitely see improvement.
- Ask a lot of questions to understand the context (and see a bigger picture).
- Don’t presume something and don’t try to think that you are smarter than others in the room. Maybe you know better how to use your analytics tool. But others might know more about how the business operates, they might know some puzzle pieces you are missing.
How do you evaluate the current maturity of marketing analytics in your company?
This answer is very much related to one of the previous questions about me not having enough time for my own analytics :)
And speaking of general maturity, this varies a lot. I have seen companies that are quite mature while others use buzzwords like “multi-touch attribution” but at the same time, they don’t track *any* events except pageviews with Google Analytics. And that is all they do with “analytics” :)
The now and then of the analytics
What do you think is the future of marketing analytics? What trends do you see coming and what’s in demand?
Continuing growth of privacy. And that is not only because of regulations like GDPR. Vendors and companies like Apple or Brave are building the products and marketing around a privacy-centric approach. More businesses will rationally build privacy-aware data collection processes while others will be forced to do this.
With more complex setups, the entry barrier for aspiring analysts will be higher. In this context, I am talking from the perspective where beginners often start with popular tools like Google Analytics. But looking at GA4, things are not getting easier. Yes, I know it is still kind of in beta (although the “beta” badge is gone). But looking at how data is stored (and its retention - max 14 months) and limited reporting capabilities, it looks like the knowledge of SQL and BigQuery will not be “nice to have” anymore. Naturally, the entry barrier for beginners becomes much higher because that’s one more tool to learn (next to GTM, GA, GDS, etc.).
What problems do you see on the market today?
Many people still don’t give a damn about how inaccurate their data is becoming due to the latest (and upcoming changes) in the industry (ITP, etc.).
Constantly changing landscape in the privacy context. Don’t get me wrong - I am for privacy. It is definitely good for consumers. But this is a real ever-changing challenge for marketers and analysts to adapt. So the word “problem” maybe is too strong here. Let’s call it a “challenge”.
Since I am focusing more on smaller businesses and freelancers (to help them learn GA and GTM), I notice that many of them focus way too much on the tools and tracking techniques rather than planning, understanding business goals, and asking the right question. Obviously, I am guilty of that too (because most of my content is about the tools and tracking techniques). But hopefully, more people will focus on the fundamentals and the “softer” part of their job, not just hard skills.
How can analysts help the business grow nowadays despite the crisis?
I could not think of anything specific here. Just continue doing their job and improving themselves. Focus not only on hard skills, but on soft skills too.
Tag the one person in the industry whose answers to these questions you would love to read.
I think you haven’t had an interview with Mark Edmondson.
Focus on the goals you want to achieve with data, pick the right tools, and may the odds be in your favour!
We really appreciate Julius’ honest responses about and sharing his experience. We hope this interview was useful and you’ve enjoyed reading it.
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