Almost everything we do now involves some form of technology that collects data about us and our behavioral patterns. This is a digital marketer’s dream, as they can use these findings to analyse trends in the data and optimise their strategies accordingly.
As the amount of data collected increases daily so does the demand for the technical skills to help extract meaningful insight from these large data sets. Decision-making is data driven and is so much more than just determining how well the test worked, but more importantly whether the results are significant. As a result, this has led to a more scientific approach to testing, and an increase in demand for technical skills, such as data analysis, statistics, Google Analytics and data science.
Experimental set-up
In order to ensure that the results obtained in any test are viable and therefore able to be determined significant or not, it is crucial that you set up the test correctly.
To aid marketers with this, AdWords created the Drafts and Experiments feature which helps set up a controlled ‘split test’ and even determines if the differences between the two campaigns are significant. This is ideal for testing changes in ad copy, campaign settings, bidding strategies, etc. to see the effect it has on performance.
When using this feature you are limited to seeing only AdWords data, but what if you wanted to include data from multiple sources to see the full picture and conduct specific significance tests?
Combining data from multiple sources can be tedious and can lead to creating large data files that test the limits of Excel when manipulating the data. This is where programming languages can help you, by allowing you to read, manipulate and visualise these large data sets with ease. They also have tailored statistics and visual packages that can help dive deeper into the data and gain further insights.
Programming languages
Why learn a programming language when there are so many different software packages out there that will help you analyse the data already?
It may seem daunting at first, and you will wonder if it’s worth it as you struggle for an hour to complete something that would have taken ten minutes in Excel. However, as you become more familiar with the language you will realise just how valuable it can be, as you begin to find innovative ways of exploring the data. Repetitive tasks that once took up so much of your time can now be automated, freeing you up to spend more time on strategy.
The two main languages
R and Python are the two key languages, each with their own range of packages that will help perform specific tasks, ranging from merging data to data visualisation.
There’s a lot of controversy over which language is better. However, if you’re thinking about learning a language it is important to think about what you are trying to gain from the data, and what your personal preferences are.
R versus Python
R is a language that was developed specifically to perform statistical analysis and therefore has excellent visualisation packages. The main library packages used are dplyr to manipulate the data set and ggplot2 for visualisation.
Python is a general programming language that can be used for statistical analysis and will work better if you are looking to build tools to help perform data analysis. The main libraries used are Pandas and Numpy.
Both can perform complex data manipulations, such as regression analysis, market basket research, prediction, cluster analysis, customer segmentation and much more. Using specific libraries, you can access your data from Google Analytics, Search Console, and many more marketing-related software, helping bring all your data into one place for more thorough analysis.
For a lot of marketing analysis, you will be dealing with average sized data sets and not necessarily the Big Data size that is usually associated with these languages. However learning these languages will help you explore the data in new ways.
Although data science skills aren’t necessary to be a digital marketer, they will help you analyse and better understand the data so that you can make data-driven business decisions that will improve your marketing methods and therefore improve performance.
To find out more about data science in marketing and where to start learning R and Python check out these articles:
