A Day in the Life of a Marketing Analytics Professional
Marketing Analytics is a multifaceted but often misunderstood practice. Here’s an example day to highlight the diversity of the role.
Marketing Analytics is often the foundation of any world-class Marketing program. But conferences, interviews and meetings have taught me that very few people understand the world of Marketing Analytics.
Some incorrectly describe Marketing Analytics solely as digital analytics — tracking visits, clicks and conversions. Yes, we do that but that’s not all we do. I’ve heard others confuse Marketing Analytics with Market Research. I work closely with my Market Research colleagues but I don’t typically do research. Once, I had someone angrily tell me I was responsible for their ads in Spotify. I’ve never worked at Spotify.
The reason I love my job is that my day can vary from light SQL coding through to full-blown machine-learning algorithms. My role is diverse and has impact; my analyses drive multi-million dollar decisions. I have the opportunity to meet with everyone from the CFO to energetic interns. And I look at data across the ecosystem. I review data on every product area and deep-dive into the relationship between product behavior, demographic information and cultural trends. I describe this work as applied computational social science.
To lift the vale and shed some light on the Marketing Analytics profession, I’ve pulled together a ‘day in the life of a Marketing Analytics professional’. Projects and tasks have been condensed to 45 minute intervals to ensure I’m able to provide a representative overview of projects that I regularly tackle.
Welcome to my day as a Marketing Analytics practitioner.
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7.30am —Running Resource-heavy SQL queries.
I like to get in early. Breakfast, coffee and SQL code. Databases run the fastest in the morning because there are fewer Analysts running queries and sucking up computing resources. I kickstart a few large queries that need a lot of computing power. Run, you sweet database, you.
8.15am — Emails and Admin.
My inbox is taunting me. I support marketers all over the globe so I try to check my inbox early and answer questions, particularly from my EMEA colleagues who are just finishing their day.
9am — Cluster Analysis.
Clustering time. I pull up R (statistics software) and start coding. I’m looking for below-average users of a product for an education-based marketing campaign. Clustering is an ML (machine learning) technique to help me find ‘natural’ groupings of users. In this case, I’m using ML to identify the natural definition of ‘below-average’. My ML tool gives me clusters of high-to-low users based on certain metrics. I take the assigned definition(s) of users below average and that becomes my audience.
9.45am — Consultation with Marketing Colleagues.
I meet with marketing team members to help them define strategy and approach for an upcoming campaign. We talk about potential audiences, Key Performance Indicators (KPIs), strategies and budget. It’s fun. I enjoy the creativity and brainstorming.
10.30am — Reviewing Dashboards and Trends.
Back at my desk and it’s “dashboard review and email” time. I maintain a total of four automated dashboards for the marketing team. These dashboards cover demographics, marketing performance, regional data segmentation and marketing benchmarks. I aim to send out a short email every two weeks covering trends in the dashboards. Today, I’m reviewing and emailing an update about the regional data trends dashboard — looking at country-specific trends to help the in-country marketing teams.
11.15am — Campaign Results Analysis.
Results time. A two-month marketing campaign in France just finished and the team is looking for results. I pull up the media impression files and start the analysis. Almost all of our marketing is measured using a test and control format — test audiences receive marketing while a similar control audience receives no marketing. I use SQL and R to compare the behavior of the test and control groups. My goal is to see if there is a statistically significance difference between the groups on product behavior metrics. Results and learnings go into a summary report card document as well as our benchmarking database.
12 noon — Meeting with Product Analysts.
Lunch meeting. I meet with product Data Scientists who provide the latest trends they’re seeing and new data tables they’ve set up. Product Analysts are responsible for understanding deep trends and nuances in a specific product area. They also build and maintain key data tables for their product area. As a Marketing Analyst, I’m responsible for looking at correlations and interaction across all product areas. So I rely on Product Analysts for their in-depth insights and use their various data tables to measure the marketing impact on user behavior. We talk Hive tables and trends. #BigData
12.45pm — Working Session to Build a Machine Learning Tool.
I meet with the data engineer on my team. We’re building a machine-learning tool that will automate sections of our marketing campaigns to help us test and learn at scale. It’s an exciting project. We spend the time talking algorithms, translating my R code into Python and figuring out the databases we need to set up. Tech nerds unite.
1.30pm — Predicting ROI.
Back at my desk. My next project is focused on predicting the ROI (return on investment) of a planned marketing campaign. The goal is to figure out if the campaign is worth the investment. If I give the green light, millions of dollars will be committed and three different teams will work on this project — so I make sure I check my numbers carefully. I look at metrics such as potential reach, estimated click-through rate and approximate CPAs (cost-per-acquisition). I’m balancing the need to quickly reply with accuracy . This juggling act is a common ailment of the modern marketing analyst. I use benchmarks and multivariate regression to run the prediction.
2.15pm — Geographic Mapping of Activity.
Location, location, location. I shift my focus to geography and mapping. Some teammates have been given the green light to create a program of ‘pop-up’ events in cities around the UK. I’ve already pre-selected the cities in our planning and strategy sessions . Now, they need to know specifically where in the cities they should run their event. They ask for locations that have lot of lunchtime foot traffic. I use location data with mapping functionality in R (I like using Leaflet mapping API) to create heat maps of foot traffic by location. I pull out the top three locations and send it to the marketing team. I love this project, I’m a nerd for a good heat map.
3pm — Writing a Measurement Plan.
Next up, I need to write the measurement plan portion of a marketing campaign. All campaign plans are required to include a measurement section that outlines KPIs, secondary metrics, goals, target audiences, geographies and measurement approach. My research colleagues will also add to this section if there’s an awareness or sentiment research component. Our organization has a strong culture of test and learn — so analytics and research (if applicable) sign off on all marketing plans.
3.45pm — Emails Round Two.
It’s nearing the end of the day. I take a moment to check my emails and answer questions from the day — or outstanding questions from previous days. I receive a lot of questions throughout the day which, over time, has taught me to be succinct and direct in my replies. I lean into my active voice as much as I can. I’ve all but removed the fluff from my emails these days because there’s simply not enough time. I save the chit-chat for in-person conversations and coffee meet-ups.
4.30pm — Review Data Trends in Japan.
My last meeting of the day — I’m talking behavior trends with our Country Marketing Manager for Japan. He’s a passionate, funny guy who loves to dive into the numbers. I try to meet with the country marketing managers every two months to give them an update on what I’m seeing in the dashboards. It’s also a great opportunity to hear from them about important issues, areas I can provide extra insight and their promotional plans. I pull up the interactive dashboard I’ve built for Japan and we chat data. Tableau is my ‘go-to’ for small data sets but it can’t handle the data table I work with so I also use an internal dashboard tool that can handle large (read: the new normal) data sets.
5.15pm — Setting up SQL Queries to Run Overnight.
It’s getting to the end of my day. My last task to set up some SQL queries to run overnight that will create data tables for tomorrow’s analyses. I run them overnight because there are more server resources available after people go home. And when you’re dealing with tables that have billions and trillions of data rows, you need all the server resources you can wrangle.
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As you can see, the Marketing Analytics profession goes far beyond clicks and conversions. It’s a mixed bag of dashboards, algorithms, coding and internal consultation. This variety is what I enjoy about the profession. Weeks are rarely the same.
Hopefully this article has highlighted some of the diversity on offer within Marketing Analytics. Oh … and if you’re thinking about a career that combines data, variety and impact — Marketing Analytics would be worth considering.
