A thought provoking series that gives my account of becoming a marketing data scientist hybrid during one of the most chaotic times in the marketing industry, the Big Data boom. In the series, I discuss my enlightenment, highlight challenges to the modern marketer and those who have developed several skills classically associated with Data Scientists; conclude with recommendations for my peers and the marketing industry. The overlap of responsibilities and required skills among Data Analyst, Marketing Analyst and CRM positions with Data Scientist roles is a clear indication that the marketing industry needs to redefine the core requirements and expectations of existing positions; or, create a new title that reflects a marketer with intermediate to advanced data science skills.
Little had I known, I was partially there. My experience in marketing analytics, CRM, database management, integration of disparate datasets and data storytelling had set me upon a path that was almost parallel to the growing influence of Big Data on the marketing industry.
Decidedly on the path of my career trajectory, I began my Master of Science in Marketing program in 2014. At the time, Big Data and the IoT were becoming more mainstream terms. I hadn’t noticed that select universities started to introduce graduate programs with a focus on Big Data and Data Science until after I had already completed a little over half of my program. Although I seriously considered the options, I was wary of transferring. Universities were capitalizing on a first mover advantage, wrought with all of the inherent issues- what implications would it have on students? What if there was another technological shift? How relevant was the curriculum being offered? Would it even be worth it?
I continued on to complete my Master’s in Marketing in May 2016. I’ll admit, the focus of completing assignments in those last few months interrupted my monitoring of Big Data’s impact on marketing. Shortly after graduation, my focus had been recalibrated in an interesting way.
In July 2016, I had interviewed for a Customer Analytics Manager position with a company recruiter and was selected to interview with the hiring manager. While the position’s description matched my background, I still ‘cyber-stalked’ the hiring manager on LinkedIn and could ascertain that she was an expert in her field. I was slightly intimidated by her breadth of experience, but knew I had the skills and ambition to fulfill the duties of the position. The time for our call had come and I sat in my car on lunch (as most job-seeking employed folk usually do), quickly scanning notes scrawled on the printout of the job description.
The phone interview with the hiring manager had started off well. The line of questioning then shifted to a business question and how I would approach the solution using R. It was at this point that I began to deflate- I didn’t know what R was and it definitely wasn’t listed in the job description, nor mentioned in my previous interview with the recruiter. I had to disclaim my unfamiliarity with the program, but offered my approach based on the tools I knew. Although it was a valiant effort, I could tell that her interest in my candidacy was lost.
Luckily, my thirst for knowledge would not allow me to have that awkward conversation ever again- I would know R.
My declaration took me on an exhaustive search of appropriate certifications. Come to find out, I didn’t really grasp the reality or span of data science at all. Big data, analytics, IoT, customer insights, customer journeys, CRM, etc… numerous articles mentioned these terms, but none revealed the marketer’s map to treasure. While my intended search was focused on certification programs in R, I had discovered a wealth of information and resources dedicated to R. In the same month of my failed interview, I enrolled in a certification program. I also subscribed to the newsletters of a hidden gem I happened upon, R-bloggers.
For an analytically inclined and curious marketer like myself, the certification opened my eyes to this forever spanning wonderment. Given the open source nature of R, there will always be new packages that offer analytical enhancements- so I consider mastering it as a lifelong undertaking. The capabilities of data science tools, coupled with my marketing skills and fundamental knowledge, enable me to be an analytical force.
I am definitely not the first Marketing Data Scientist hybrid, but hope to start a movement which unites us such that our solidarity has a positive influence on the trajectory of our future. For the sake of better aligning academics with current marketing career paths and providing job seekers with more realistic descriptions, core requirements and expectations need to be redefined going forward. As the current environment of the marketing industry continues to change with rapid technological advancements, we enter: Big Data and the modern marketer’s dilemma.
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