What do you do if you want to excel in Marketing Analytics by learning the key programming languages?

To excel in Marketing Analytics, start by mastering Python basics. Python is versatile and offers powerful libraries like Pandas for data manipulation, Matplotlib for visualization, and Scikit-learn for machine learning. Practice analyzing real-world marketing data to gain insights. Dive into data visualization with Matplotlib and Seaborn to understand trends. Learn Pandas for data manipulation. Explore machine learning with Scikit-learn for tasks like customer segmentation. Apply these skills to real projects and stay updated with the latest trends.

I started my career in a marketing science consultancy 2 decades ago and I still remember the pain of 90% of project time spent on data preparation in Excel! A few years later the company shifted from Excel to R, which significantly improved the analytics team's performance. And was a game changer for me! Learning curve was steep but thanks to my studies in economics, R stuck with me much more than Python did. Back then, R gained momentum as one of the first open source languages for statistical analysis, which it still benefits from if you think that only 2 years ago Meta developed their MMM library (Robyn) entirely in R! So to anyone interested in a career in Marketing Analytics, I’d say that R would make for a great starting point!

R is a potent tool tailored for statistical analysis and data visualization. It's ideal for tasks like hypothesis testing, time-series analysis, and customer segmentation. Start with fundamental concepts like vector operations and data frames in R. Then, explore packages like ggplot2 for crafting impactful visualizations. Applying R to marketing data unveils trends vital for strategic decision-making. Practice using R with marketing datasets to harness its full potential for insightful analysis.

R is another programming language commonly used for statistical analysis and data visualization. It offers a wide range of packages such as ggplot2, dplyr, and tidyr for data manipulation and visualization. Learn R to perform advanced statistical analysis, predictive modeling, and visualizations in marketing analytics.

SQL is one of the foundational skill of any data analytics role. PERIOD! As a client-facing marketing scientist, you should really get to a good intermediate level, where you are able to do 2 things: - write simple to semi-complex queries that would grant you independent access to databases that you already know the structure of - get the gist of what more complex queries are doing so that you are able to tweak them to suit your more immediate data needs Beyond that level, I'd argue your "trespassing" into the realm of data engineering! That's why, in any new jobs, you should make it a priority to befriend the data engineers They hold the keys to data and to your success as a client-facing analyst

Working at startups, scale-ups and well established companies, I can clearly say, that two things are coming with the maturity of companies: - higher specialization and thus demand for particular skills - emphasis on doing things quick - with basic skills and quick-and-dirty approach Start with the basics and master SQL. It will always come in handy and be in demand. The data will never be sparkling clean, and a lot of preparation must be done. Master it and then build on top of it. Python and R come later in the career, but the basics are done in SQL. Whatever nice and shiny project you're going to deliver, no matter which language, logic or approach it will use - the QA will always be done in SQL.

SQL (Structured Query Language) is essential for querying and manipulating data stored in relational databases. Learn SQL to retrieve, filter, and aggregate data from databases such as MySQL, PostgreSQL, or SQL Server. Mastering SQL will enable you to conduct data analysis and reporting using large datasets in marketing analytics.

JavaScript plays a vital role in deploying and maintaining web analytics tracking codes. Mastering JavaScript basics enables you to tailor tracking scripts for Google Analytics and other platforms, ensuring precise data collection. Gain proficiency in manipulating the Document Object Model (DOM) to track user interactions such as clicks or form submissions. This skill empowers you to furnish accurate data for evaluating website performance and user behavior effectively.

Personally, I see VBA for Excel as a bit of a dying art, especially in the world of big data. Nowadays, advertising in general and digital advertising in particular are data-rich environnments and I saw VBA applications gasping for air and slow down sensibly above the hundreds of thousands of rows, especially with complex operations to run. Granted! RAM and CPU size on your PC and efficiency of the VBA code also play a role but if you have millions of rows to process, you should really consider to combine database accessing tools (SQL Server, PostgreSQL, etc.) with data analysis libraries like Python's pandas or R's dplyr

Excel's Visual Basic for Applications (VBA) may not be a conventional programming language, but it's a potent tool for automating tasks and conducting intricate analyses within Excel. Begin by recording macros to grasp the VBA code underlying your actions. Then, progress to writing custom scripts for automating data cleaning or report generation. Proficiency in Excel VBA can substantially enhance your efficiency in managing marketing data.

I came across SAS very early in my carreer and I remember it being a breeze to use. I loved it! Still to these days there are entire industries (biostatistics, pharma, clinical trials, some goverment bodies and financial institutions) that could simply not operate their analytics without SAS. Yet, I remember how unimpressed my manager was when it came to renew the licenses. I believe they are expensive but I have no visibility of the magnitude. In a world where R and Python provide a trialed-n-tested open source (i.e. free to use) alternative for your forays into Marketing analytics, adopted by the analytics teams at advertising giants like Meta and Google, I would personally not recommend spending time learning SAS.

SAS is a tailored software suite for sophisticated analytics in corporate settings. Mastering SAS basics is advantageous for roles in large corporations. Start by learning the SAS programming language fundamentals, such as data step processing and PROC SQL for database management. Acquaint yourself with SAS's analytical procedures to conduct advanced statistical analyses on extensive marketing datasets.

Focus on languages like Python and SQL, which are widely used in data analysis and manipulation. Start by familiarizing yourself with basic programming concepts and gradually delve into libraries and packages specific to data analysis. Additionally, gaining proficiency in SQL will enable you to efficiently query and manipulate databases, which is essential for accessing and analyzing large datasets commonly used in marketing analytics. Engage in hands-on projects, participate in online courses or tutorials, and seek opportunities to apply your skills in real-world marketing scenarios to solidify your expertise in programming for marketing analytics.

I'm somewhat surpsise that no one mentioned Excel, the Swiss army knife of analytics! Excel has been around since the mid 80s, and it's unlikely to disappear any time soon! As a matter of fact, it's very likely that the "last mile" of many back-of-the-envelope analysis (marketing related of otherwise) is still going to happen in excel! It's versatility stretches from basic data entry to complex analytics, especially with the Analysis Toolpack and Solver Add ins enabled. Microsoft ongoing updates will make sure that it will continue to cater for many users' needs, cementing it's status as the Swiss army knife of analytics