Finding great analytics people isn’t easy (trust me; I’ve looked at LOTS of resumes in building my team at 3Q). I’m often asked what it takes to be (or show the skills to become) a trusted, valuable analytics expert in a rapidly changing field. As you can imagine, I’ve got a lot of thoughts on the matter. Here goes…
Fulfill the table stakes
- Be intelligent and have a “love” for numbers
- Have an interest in understanding the “why” behind the “what”
- Translate business requirements into measurement strategies and requirements
- Have a fundamental-to-expert understanding (grows with seniority) of measurement platforms and ad-buying platforms
- e.g. Google Analytics, Google AdWords, Facebook Insights, etc.
- Be an Excel, PowerPoint, and data visualization ninja
- Be a data “junkie” – understand and harness the power of what data can bring out and provide. Understand and have the ability to tell a story that explains issues, both good and bad performance, etc.
Fully understand the platforms and how they work
Analytics experts shouldn’t just know how to pull the data out for reporting, because implementations can have flaws and data can be over-or-under collected. Without this upstream understanding, they’ll be less likely to catch process errors that can muddy the results.
I’ll give a couple of examples of what I mean. In the first instance, let’s say add-to-cart event counts match the number of product page views. In that case, the tag is being triggered by the page view and not the actual button-click or add-to-cart event.
Another good example would be a tag being implemented twice on the same page. According to the data, we’re doing great – page views are up at least 100%, and bounce rate is minuscule. Great work, everyone…right? What a disappointment for everyone once someone finds out it’s a tagging issue. Great analytics people need to be able to catch these problems before it’s too late and make sure that incorrect performance results aren’t communicated to marketing folks – and, even worse, executives.
Be able to problem-solve
Being able to figure out why data looks off, might not be reporting correctly, or just doesn’t make sense is key to someone in this role. They need to be able to leverage the data available to them to help solve problems for the business, whether it be marketing performance, UX/Conversion, product performance, brand dissatisfaction, etc. They have to do more than diagnose; they have to contribute to the solution.
Do more “dissecting” than thinking
They should never take data on face value. They should always strive to understand the “why” behind the “what” so that they understand and deliver/explain why something happened and can contribute to “what we do next” in response.
We’ve spent too much time training and growing “report monkeys.” Sorry, I know that sounds bad, but it’s the truth. We’ve also come far enough along with technology to automate much of the work that we’ve asked these same individuals to do manually. It’s critically important that we bring in people who can adjust and have fun in their day-to-day by using the data that’s available to them to make an impact.
Be a partner
It’s crucial for analytics folks to be able to integrate well with the rest of the company and its disciplines. They shouldn’t just provide reporting; they have to be invaluable, fundamental resources for teams kicking off projects or campaigns. They have to provide insights on things such as what are people searching for, what searches are not resonating, how are they navigating a website, what they aren’t looking at on the digital properties, what their most common barrier is to getting a user to complete the desired action, etc. These are all powerful insights to bring to the table that others tend not to be able to find.
Make few mistakes
People make mistakes, and it’s acceptable; we’re all humans, after all. But we have to remember that we’re dealing with revenue and media investments that many people rely on and use to make critical decisions. If we can’t pull the numbers correctly, don’t copy them correctly, make a mistake on the timing, etc., folks will start to trust us less. If this happens several times in a short duration, there’s a risk that teams won’t trust the person responsible again.
Excel at presentation
I’m not just talking about PowerPoints or Excel dashboards here. Data, when used as it’s meant to be, can get very sophisticated. The number of sources we work with continues to grow, in granularity and depth, and that added layer of complexity can make it harder and harder for people to get meaningful insights. So what I’m referring to here is the ability to take in all of this data, and simplify it and tell a meaningful, digestible, visually persuasive story. While this may not be received well by all, I always recommend approaching this as “assume that your audience knows nothing and doesn’t understand the data that you’re working with at all.” The goal for my team is always to make it as simple as possible while also making it as powerful and meaningful as possible.
Provide incremental value
If all an analytics person can do is state the obvious, and someone in marketing, finance, IT, other business can do the same, then they’re not providing any incremental value -so what’s the point in paying their salary?
He/she needs to be able to dig deeper into the data and find the “hidden gems” that get everyone in the room to have an “AHA!” moment.