Exploring Different Dimensions of Analytics: MARKETING
Previously we were talking about sports analytics. Today we will deal with one more interesting field. It’s about marketing. It is the collection and analysis of customer behavior data that allows companies to evaluate the effectiveness of advertising, adjust their promotion strategy in time, and thus increase the profitability of their marketing investments.
Responsible for this marketing and web analytics. They work with a very diverse «raw material»: with statistics in social networks, and with website traffic, and with various offline metrics … To extract valuable information about the patterns of audience behavior from all this abundance of information, analysts use statistical methods.
Typical responsibilities of an analyst in marketing: creating an analytical infrastructure, collecting and processing data from different systems (Google Analytics, various CRMs), evaluating the success of advertising campaigns and market introductions, generating reports and dashboards.
To get started in marketing analytics, you need to:
● First of all, love marketing: navigate or want to understand metrics, sales funnels, promotion channels.
● Secondly, have a deep understanding of statistical methods, A/B testing, be able to handle big data, and be proficient in Excel.
● For more complex tasks, knowledge of Python or R, SQL and advanced analytics tools is useful.
In short, if you are passionate about designing A/B tests, you like to study behavioral patterns and you are not averse to working closely with PR and marketers, you can safely think about a career in marketing analytics 😃.
By the way, mastering machine learning is not considered a hard necessity for a marketing analyst. Although it can be very useful for advanced tasks:
● For example, ML models can be used to predict future trends based on historical data. Forecasts allow businesses to make informed decisions about the allocation of marketing resources.
Stop investing in a promotion channel that is about to go through a recession in time.
● And with the help of the «Uplift-modeling» algorithm, it is possible to calculate whether it makes sense to additionally motivate the user for a target action (say, offer a discount on a purchase) or whether he is ready to perform this action himself.
Before you leave:
If you enjoy reading stories like these and want to support me, don’t forget to give me a few claps 👏, follow me ✅ and thus receive all updates 🔄 about new publications.
